Sustainability Assessment of Rice Farming: Insights from Four Italian Farms Under Climate Stress
by
, , , , and
Savoini Guglielmo
1, 
De Marinis Pietro
1,*
,
Casson Andrea
2
,
Abhishek Dattu Narote
2,
Riccardo Guidetti
2
,
Stefano Bocchi
1 and
Valentina Vaglia
3 1
Department of Environmental Science and Policy, University of Milan, 20133 Milan, Italy
2
Department of Agri-environmental Science, Production, Landscape, Agroenergy, University of Milan, 20133 Milan, Italy
3
Department of Earth and Environmental Science, University of Pavia, 27100 Pavia, Italy
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(17), 1797; https://doi.org/10.3390/agriculture15171797
Submission received: 10 July 2025
/
Revised: 23 July 2025
/
Accepted: 30 July 2025
/
Published: 22 August 2025
(This article belongs to the Section Agricultural Systems and Management)
Abstract
The study compares the overall sustainability of two organic and two conventional rice farming systems during the 2022 drought. The research aimed to develop an experiment exploring the ability of an integrated methodological approach to identify tradeoffs and provide actionable insights for a sustainable agricultural transition under extreme climate stress. To this aim, the study employed economic analysis, Life Cycle Assessment (LCA) for environmental impact, and the OASIS framework for broader social and resilience indicators. The study revealed tradeoffs between the economic efficiency of conventional rice farming and the ecological resilience of organic systems, a conclusion made possible only through its integrated assessment methodology. By combining different methods, the research suggested that while conventional farms achieved clear financial superiority and greater efficiency per ton of rice, organic systems showcased superior ecological performance per hectare, greater biodiversity, and enhanced resilience. This highlights a crucial research frontier focused on designing hybrid systems or new economic models that can translate the environmental resilience of organic methods into tangible market value, effectively resolving the very tradeoffs this comprehensive assessment suggested.
1. Introduction
With over half the world’s population relying on rice as a staple, and its unmatched cultural, economic, and nutritional significance, the role of rice cropping in shaping more just and sustainable food systems is widely accepted [1,2,3]. Yet, rice cropping brings severe environmental impacts, including land degradation and greenhouse gas emissions. With rice covering approximately 167 million hectares globally, its water-intensive nature, consuming 34–43% of the world’s irrigation water, exacerbates water scarcity issues [4]. Furthermore, the flooding of paddy fields contributes to around 10% of global methane emissions, highlighting its environmental impact [5].
The environmental and social challenges of rice cropping are particularly severe because rice cultivation persists globally, with over 715 million tons harvested annually across more than a hundred countries [6]. China and India lead the global production, while Italy emerges as Europe’s primary rice producer, with around 230,000 hectares cultivated [7]. Italian farms predominantly cultivate japonica varieties, and witness a growing interest in sustainable agricultural practices, evidenced by the increasing adoption of organic rice cultivation [8]. Therefore, Italian rice farming plays a significant role in Europe’s sustainable food system [9], requiring evaluation across environmental, economic, and social aspects.
The debate now demands the identification of the model of rice production that may better contribute to matching the agrifood sector transition agenda. In fact, organic rice farming and fair trade value chains constitute an interesting alternative that is currently studied to understand its potentials for the mandatory transition of global food systems [10,11,12]. In particular, organic systems show greater resilience to climate extremes like drought and flooding, due to improved soil structure and water retention. Moreover, in high-input or degraded settings such as the Po valley, organic practices can significantly enhance ecosystem service productivity, still safeguarding yields and optimizing local resources [13,14]. Previous research comparing conventional and organic rice cultivation in Italy encompasses a broad spectrum of socio-economic and environmental assessments but focuses mainly on specific dimensions, emissions, soil quality, agricultural practices, health impact, and gross margin studies. Some studies focus specifically the environmental impact and the pollution generated by different cropping managements. Studies using Life Cycle Assessment (LCA) on Italian rice cropping reveal significant environmental impacts [15,16,17,18,19]. Blengini et al. conducted an LCA in Vercelli, highlighting CO2eq emissions, energy consumption, and water use per kg of rice [17]. Despite potential yield reductions, they advocated for organic and upland farming to mitigate these impacts. Bacenetti et al. [15] assessed organic rice cultivation in Lombardy, identifying methane and nitrogen emissions and mechanization as key concerns. Vaglia et al. [20] emphasized the environmental and health challenges of rice farming in Italy, advocating for organic agriculture to align with sustainable food production goals, highlighting reduced environmental impacts of organic methods [20].
LCAs typically focus on biomass production for food, fiber, or bioenergy [21,22] but often overlook broader agroecological functions and ecosystem services [23]. Current LCAs may misrepresent less intensive practices like organic agriculture [24,25,26], inadequately addressing biodiversity, pesticide effects, animal welfare, and nutritional quality. Integrating LCAs with broader sustainability assessments could enhance decision-making for rice cropping [27,28]. Few assessments in rice production focus specifically on the socio-economic dimension [29]. Social impacts of rice cropping have been studied mainly in connection with fair trade initiatives, looking at the extent to which imported rice affects the social sustainability of the European food consumption [30]. Some previous papers analyze the economic sustainability of production systems in rice cropping, confirming a tradeoff between economic impact and socio-environmental impacts exists in the Italian context [31,32,33]. From the economic standpoint, the gross margin method is a widely recognized indicator providing clear and immediate evaluation of agricultural activities in terms of financial returns, especially useful for comparisons between conventional and agroecological cropping models [34,35,36].
All these studies collectively enrich understanding of rice cultivation in Italy, providing crucial insights into environmental sustainability, soil management, and the potential of organic and hybrid farming to improve productivity while mitigating environmental and social impacts.
Nevertheless, from an agroecological perspective, social, environmental and economic impacts of the management choice are equally important, and, consequently, it is fundamental to adopt integrated evaluation approaches and methods and to understand the tradeoffs between the impacts of different farming approaches, such as the conventional and the organic.
In this context, this study addresses an integrated sustainability assessment of rice farming systems (conventional and organic) in Italy by analyzing their economic, environmental, and social performance through a mixed-method approach.
To our knowledge, few studies address the integrated sustainability assessment of rice farming in the European and, namely, Italian context. Previous studies highlighted the scarce availability of clear operational data for calculating integrated sustainability indicators of rice farming. The exceptions to these limitations are represented by a few methodologies developed under the umbrella of important international organizations promoting sustainability and research efficiency in specific agricultural production systems, such as the SRP (sustainable rice platform) for rice, or the SAFA and TAPE methodologies [37].
Among the different methodologies used to run a holistic assessment of farm sustainability, the Original Agroecological Survey and Indicator System (OASIS) method is gaining momentum due to the need to study the tradeoffs and differences among farms, taking into consideration the need to assess and communicate the complexity of the larger set of agricultural products, i.e., the ecosystem services other than food provision. As a holistic framework of assessment, OASIS is based on scores defined by the direct survey of primary and secondary, qualitative and quantitative, data. The OASIS tool is therefore open to being integrated and supported by other methodologies that may deepen some of the dimensions of sustainability.
To obtain a clear understanding of the tradeoffs between the economic, social, and environmental dimensions among the conventional and organic models, it is necessary not only to identify production processes with the lowest social and environmental impacts but also to identify those activities that have affordable costs while leading to higher margins, to identify sustainable management systems considering synergistically all the sustainability facets.
To this aim, this study investigates whether an integrated assessment approach can effectively identify tradeoffs and inform sustainable rice production under climate stress. By following the international call for transdisciplinary evaluations of sustainability, the present research aims at (i) shedding light on the operational implementation of a mixed LCA-OASIS assessment tool, supported by a specific gross margin analysis; (ii) contributing to comparing conventional and organic cropping models; (iii) making this comparison in the driest year in 150 years.
2. Materials and Methods
2.1. Mixed Methods Approach for Integrated Sustainability Assessment
Integrated sustainability assessments in agriculture benefit from the use of complementary methodologies. These methodologies can collectively capture the complexity of farm systems. Common data such as input use, yields, emissions, and economic variables are pivotal across different analytical frameworks—Life Cycle Assessment (LCA), gross margin analysis, and holistic tools like OASIS. Recognizing the interoperability of these data allows for a coherent analysis that simultaneously addresses environmental performance, economic viability, and broader agroecological indicators [38]. This approach is particularly effective when data from the analyzed farms are collected simultaneously during the same season and by the same research team, in order to minimize internal inconsistencies and maximize the accuracy of the resulting estimates [39]. Sustainability assessment of agricultural farms requires a holistic approach to capture relevant tradeoffs between the economic, social, and environmental impacts of the activities. Mixed methods are extremely useful when dealing with multifaceted concepts such as sustainability because they allow the researchers to consider separately the facets of each sustainability dimension, with an adaptable degree of detail, and then to triangulate the results to build solid knowledge of the tradeoffs existing between sustainability dimensions. Our research adopts a mixed method that integrates an LCA analysis and a Gross Margin economic analysis in a widely accepted, holistic framework for agroecological performance assessment, i.e., the Original Agroecological Survey and Indicator System—OASIS, in different Italian areas. The overall objective is to provide a comparative tool for strengthening the evidence about organic model impacts when compared with the conventional production model, in dry cropping season such as 2022 in Northern Italy.
2.2. Case Studies
Our research focuses on the North Italian rice cultivation district, where 4 different farms under two different production models were monitored during the 2022 cropping season. Target farms were chosen for being located in two of the most important rice-producing provinces in northern Italy, Pavia and Vercelli, and for their production model, organic or conventional. Pavia and Vercelli provinces account for 35.6% of the total area and 30.1% of national production and 32.1% of the total area and 36.9% of national production, respectively [40]. Two farms are in Bereguardo rural area (PV), and two are in Rovasenda rural area (VC) (Figure 1). Located in the plains of Lombardy and Piedmont, respectively, these regions lay in a Cfa climate zone according to the Köppen classification, characterized by hot, humid summers and mild, wet winters. Bereguardo receives a mean (2021–2024) annual rainfall of 631.1 mm (450 mm in 2022, data from ARPA Lombardy), with an average yearly (2021–2024) temperature of around 13.7 °C (14.4 °C in 2022, data from ARPA Lombardy). Rovasenda, on the other hand, has a mean annual (2021–2024) rainfall of 1143 mm (576 mm in 2022, ARPA Piedmont), with an average annual (2022–2024) temperature of 13.5 °C (14.1 °C in 2022, ARPA Piedmont). These favorable climatic and hydrological conditions, coupled with rich alluvial soil composition, provide optimal conditions for rice cultivation, which has been supported over time by the development of advanced irrigation systems, including traditional water management techniques refined over centuries.
In such a suitable context for rice cropping, the choice of 2022 as the reference year for this research is particularly significant, as it was one of the driest years in Italy in the past 150 years [41]. The extreme drought conditions that characterized the year provide a unique and valuable opportunity to observe the behavior of different production models—organic and conventional—under a shared and highly stressful environmental constraint. In 2022, the wholesale price of rice increased by 13% compared to the 2011–2021 average, reaching €600.20 per ton [42]. The annual average temperature was 16.6 °C, which is 10% higher than the average of the previous decade [40]. Total annual precipitation dropped significantly to 409.2 mm, marking a 54% decrease compared to the 2006–2015 period [40]. Finally, the average diesel price rose by 40% compared to the 2015–2025 average excluding 2022, reaching €1.51 per liter [43]. This context reflects climate scenarios that are expected to become increasingly frequent in the future, thus offering insight into the adaptive capacity and resilience of cropping systems facing climatic extremes. The year’s challenging conditions serve as a lens through which the potential of each farming model to cope with water scarcity and environmental stressors can be critically evaluated.
The four target farms differ in terms of cropping management, with two farms implementing organic rice cropping practices (Rovasenda Organic—RO and Bereguardo Organic—BO) and two farms adopting conventional rice farming practices (Rovasenda Conventional—RC and Bereguardo Conventional—BC). Appendix A, Table A1A–D describes the management practices implemented in the four target farms. Appendix A, Table A3 shows the main features of each farm. Appendix A, Table A5 describing the difference between 2022 and the reference periods.
Regarding the internal organization of the farms, all 4 farms have fields near the farm center (500 to 2000 m distance). Regarding the state of the farms in terms of mechanization, all four farms own more than one tractor and all the needed operating machines, with the only exception being one farm that relies on contractors for mowing–baling (cover crops) and harvesting (rice grains).
For all the farms, information concerning the agricultural practices, agronomic inputs, and the resulting grain yield (ton/ha at 14% of commercial moisture), reported in (Appendix A, Table A1 and Table A2), were collected through face-to-face farmer interviews and field surveys during the 2022 crop cycle.
2.2.1. Organic Model
This study adopts the IFOAM I definition for Organic Agriculture, which is, “Organic Agriculture is a production system that sustains the health of soils, ecosystems, and people. It relies on ecological processes, biodiversity and cycles adapted to local conditions, rather than the use of inputs with adverse effects. Organic Agriculture combines tradition, innovation, and science to benefit the shared environment and promote fair relationships and good quality of life for all involved”.
Organic rice farming in Northern Italy employs strategies that increase cropping sustainability, mainly focusing on weed management. These methods include mechanical weeding in dry or wet conditions and green mulching with cover crops. These methods aim to reduce reliance on chemicals, promote biodiversity, and enhance soil health. Despite challenges like precise water management and labor intensity, organic rice farming offers environmental benefits and aligns with the agroecological vision of many farmers.
In our case study, the two farms (BO and RO) followed 2 different models: one green mulching and the other mechanical weeding. In Rovasenda, Lolium multiflorum (50 kg/ha) was sown in autumn. At the end of April, water was introduced, and rice was broadcast-seeded directly onto the cover crop to exploit the allelopathic effect against weeds. On the second farm, in Bereguardo, false sowing was carried out with early flooding 15 days in advance, followed by mechanical weeding with a harrow to allow for broadcast seeding on a weed-free paddy field. Appendix A, Table A1A,B describe in detail the adopted practices. Appendix A, Table A3 completes the description for each farm.
2.2.2. Conventional Model
The conventional rice farming model in Northern Italy uses two main approaches: submerged and dry seeding. Submerged seeding offers thermal flywheel effects, nutritional benefits, and reduced nitrate pollution but increases greenhouse gas emissions. Dry seeding saves water and labor but faces challenges in weed management and water management practices. Critical operations for submerged seeding include plowing, leveling, embankment breaking, harrowing, submersion, puddling, pre-seeding weeding, broadcast seeding, fertilization, post-seeding weeding, harvesting, transportation, and drying. Dry seeding uses row seeding and post-sowing rolling for optimal germination. Water management concludes with pre-harvest drying for expedited harvesting and grain quality.
In our case study, the two farms (BC and RC) followed two different models: in Rovasenda, water seeding was carried out, preceded by base fertilization and pre-seeding weeding 15 days prior, followed by post-emergence weeding and fertilization. In Bereguardo, dry seeding was carried out, immediately followed by pre-emergence weeding, post-emergence weeding, and two fertilizations. Our research focuses on two conventional farms that adopt both dry and wet seeding methods, and Appendix A, Table A2A,D describe the adopted practices in detail.
2.3. Single Methods
2.3.1. Original Agroecological Survey Indicator System
The Original Agroecological Survey Indicator System (OASIS) is a comprehensive framework designed to assess farms’ agroecological transitions [44]. It combines rigorous data collection through interviews, systematic transcription and calculation, and comprehensive reporting to evaluate farm practices holistically. Among the few existing recognized frameworks for agroecological evaluation, the Original Agroecological Survey and Indicator System (OASIS) was selected for this study. While OASIS shares core principles with other methodologies like the FAO’s Tool for Agroecology Performance Evaluation (TAPE)—which assesses all dimensions of agroecological performance, including participatory moments for validation—OASIS presents two key advantages for this specific research. Firstly, its development by Agroecology Europe ensures its framework is highly relevant to the European context, making it well-suited for analyzing the nuances of Italian rice farming systems. Secondly, OASIS is intentionally more farm-oriented, employing a granular set of 56 indicators that allows for a more detailed and in-depth comparison of individual farm performances, which aligns perfectly with the case-study methodology of this paper.
OASIS includes a comprehensive set of criteria and indicators divided into various dimensions such as agroecological farming practices, economic viability, and sociopolitical aspects.
According to the methodology, we followed a three-step procedure.
During step 1, i.e., Study, an in-depth study of the OASIS evaluation criteria was conducted. All indicators proposed by the OASIS operational manual [44] were selected and used in the interview.
During step 2, i.e., Interview, data were collected through direct interviews with the four farmers, their families, and their workers. The global sample included 4 farm households (2 organic, 2 conventional) and 6 agricultural workers, totaling 21 individuals: 4 farmers, 4 spouses, 7 children, and 6 workers. The average age was approximately 39.7 years (excluding children: 45.8 years), with a balanced gender distribution among adults (6 women, 8 men). Surveyors were encouraged to be observant and attentive, allowing for flexibility in the interview to cover additional relevant topics as needed. This ensured a thorough and holistic collection of data.
The interviews were conducted during the winter of 2022. Before starting the interview, the farmer of each farm was requested to bring the farm map with crop distribution, records of pesticide, fertilizer, and BCA (biological control agents) application, and annual expense records in order to obtain the most objective and truthful data possible.
During the interview, more than 250 questions are asked, which can be summarized into 5 main groups (Table 1): an initial phase of descriptive questions about the farm, a section regarding crops and their management, a third phase concerning costs and revenues, a fourth focusing on life quality and gender equity, and finally, the last phase on labor conditions.
During step 3, i.e., Transcription and Calculation, data were organized into a spreadsheet designed for this purpose. This step ensures that the qualitative data collected during the interviews are translated into quantitative metrics that can be analyzed and compared across different farms.
The evaluation of criteria is semi-quantitative, with scores assigned on a scale from 1 to 5, where a score of 5 represents the highest degree of practice of a fully fledged agroecological system. In general, the final values for each criterion are estimated by the evaluator based on the information gathered through the interview and documentation provided by the farmer, interpreted according to the specific indicators and scales outlined in the OASIS methodology. Most criteria are assessed using practice-based indicators [44]. For instance, considering efficient water management (subindicator 1.3.1), the scoring scale explicitly uses numerical thresholds based on the percentage of farmed land where practices are applied (e.g., >75% for a score of 5) and/or water is consumed by m3/ha/year (e.g., <500 m3/ha/year for a score of 5 under the optional indicator 5.2.7.1). Thus, for this criterion, achieving a score of 5 could be considered reaching the “maximal” or most “efficient” level as defined by the scale’s thresholds. Considering another example, the maximization of soil cover (subindicator 1.1.6) is defined as the average proportion of time the soil is covered and the adopted numerical thresholds corresponding to each score level (1–5, where 1 is 0% and 5 is 100%). Finally, for other indicators, the scoring might involve combining objective data, the farmer’s subjective judgment (e.g., satisfaction with income), and the evaluator’s observations [44]. A complete list of scales used to estimate final OASIS values is presented in Appendix A, Table A4.
2.3.2. Life Cycle Assessment
The Life Cycle Assessment (LCA) methodology was used to estimate the environmental impacts of 4 rice management scenarios (BO, BC, RO, RC). ISO 14040/44 standards (ISO, 2021) and the Product Category Rule guideline “ARABLE AND VEGETABLE CROPS” developed by International EPD System were used as references [45].
According to ISO standards (2021), the functional unit (FU) provides a reference to which the inputs and outputs can be related. To enable comparison of the different scenarios, the FU defined in this study was “the management of 1 hectare of rice field during the year 2022”.
This approach aligns with the standardized methodology outlined in ISO 14040/14044 for Life Cycle Assessment, which emphasizes consistent functional units to ensure comparability across environmental impact studies [46]. Furthermore, defining a clear functional unit is critical in agricultural sustainability assessments to accurately evaluate resource use and environmental outcomes [47].
The choice of 1 hectare as the functional unit (FU) was made to reflect the management practices over a standard land area, which is common in agricultural LCA studies for comparability and practical relevance. While yield-based FUs, such as per ton of rice, can capture productivity differences, they may obscure environmental impacts related to land use and management intensity. The literature suggests that organic farming often shows better environmental performance per hectare due to lower inputs, though yield-based assessments sometimes favor conventional systems due to higher productivity, highlighting the importance of FU selection based on study goals.
The LCA conducted in this study focused on a from-cradle-to-farm gate scope, intentionally excluding post-farm gate processes such as distribution, marketing, and consumption. This exclusion is common in agricultural LCAs to isolate the environmental impacts directly related to production practices. However, it is important to consider that conventional and organic rice systems often differ significantly in their marketing channels, with organic products typically involving shorter, more localized value chains that may reduce emissions and resource use beyond the farm gate. These differences highlight the potential need for complementary studies addressing post-farm gate impacts to fully capture the sustainability of each system.
Figure 2 indicates the life cycle steps assessed in all the rice cultivation management scenarios.
Data regarding all agricultural operations were collected from farm diaries. All the model data was based on the Ecoinvent database (version 3.9).
Life cycle inventory consists of a detailed compilation of all the relevant inputs (material and energy) and outputs (gaseous, liquid, and solid emissions to air, water, or soil) at each stage of the life cycle studied. Mainly, the data collected through agricultural diaries of farms and interviews focused on the different operations and inputs carried out on the field. Soil preparation and cultivation using agricultural machinery were modeled using the amount of fuel consumed. This was quantified for each operation, as visible in Appendix A, Table A1. Specific consumption data and related emissions were quantified for each scenario, and datasets from the Ecoinvent database were selected for these components. The use of herbicides, fungicides, and insecticides was considered in all 4 scenarios, and the active ingredients were used as drivers to model the different substances applied in the field. Regarding fertilizers, the nutrients that compose substances (N-P-K) and their quantities were considered, and datasets from the Ecoinvent database were selected. Regarding field emissions, ammonia (NH3), nitric oxide (NO), emissions of CH4 from paddy water, and nitrous oxide (N2O) were quantified. Water emissions were computed considering leaching and runoff of nitrates (NO3-). Furthermore, the impact on the 3 environmental compartments (soil, air, and water) of the applied pesticides was quantified based on their active ingredient content. Quantities calculated for every emission for every scenario are reported in Table 2.
Emissions of ammonia (volatilized kg NH3-N per kg of N applied) and nitric oxide (volatilized kg NO-N per kg of N applied) were calculated by multiplying each kg of applied nitrogen (N) by an emission factor, differentiated according to the type of the fertilizer [48]. Direct and indirect emissions of nitrous oxide (N2O) were estimated using the emission factor reported in Zampori and Pant [49], namely, 0.022 kg of N2O emitted to air per each kg of N synthetic fertilizer and manure applied. Direct emissions of CH4 from paddy water were estimated using the IPCC standard. The emissions into water due to leaching and runoff of nitrates (NO3-) and phosphorus (P) were calculated using the emission factors reported in Table B.16 of Zampori and Pant [49], which are 0.24 kg of NO3-N emitted per kg of N in fertilizers applied. Pesticide emissions were calculated by taking active ingredients into account. The approach reported by Zampori and Pant [49] was adopted: it was assumed that 90% of pesticides applied in the field were emitted into the agricultural soil compartment, 9% were emitted into the air, and 1% was emitted into water.
The software Simapro version 9.5 (PRè Sustainability, Amersfoort, The Netherlands) and Ecoinvent database (version 3.9) were used to assess the environmental impacts of the 4 rice cultivation management scenarios considered. Among the various assessment methods available in SimaPro, we chose to use the Environmental Footprint Method 3.1 developed by the European Union Product Environmental Footprint. The Environmental Footprint (EF) method 3.1 v. has a global scope. It can be used to analyze intermediate impact categories, including characterization, normalization, and weighting factors [49]. The impact category definitions, method performance, characterization, and normalization adopted in this study can be consulted in the European Commission technical report [49]. All 16 impact categories of the EF method were initially used for each step of rice cultivation management (Figure 1), and are as follows: Acidification (ACID); Climate change (CC); Ecotoxicity, freshwater (ETX-FW); Particulate matter (PM); Eutrophication, marine (EU-MAR); Eutrophication, freshwater (EU-FW); Eutrophication, terrestrial (EU-TERR); Human toxicity, cancer (HT-C); Human toxicity, non-cancer (HT-NC); Ionizing radiation (IR); Land use (LU); Ozone depletion (OD); Photochemical ozone formation (POF); Resource use, fossils (RU-F); Resource use, minerals and metals (RU-MM); Water use (WU).
The results of the LCA can be converted into a Single Score by aggregating the environmental impacts across all considered impact categories into one weighted value, providing a simplified yet comprehensive representation of the overall environmental burden. The conversion of Life Cycle Assessment (LCA) results into a single score relies on the aggregation of multiple environmental impact categories—such as climate change, eutrophication, and toxicity—into one weighted value. This process involves 2 key steps: normalization and weighting. During normalization, raw impact data are scaled relative to a reference value (e.g., global or regional emissions), thereby transforming diverse impact indicators into comparable dimensionless units. This step is crucial because it provides a common basis for comparing impacts that are otherwise expressed in different units and scales. For instance, normalization may involve using average global emissions data as a reference to assess the relative burden of each environmental impact [50].
Following normalization, each impact indicator is multiplied by a weighting factor that reflects its relative importance or societal concern. These weighting factors are typically derived through stakeholder input or societal value judgments, and their application allows for the aggregation of various impacts—each now comparable on a common scale—into a single composite score [51]. The weighted impacts, when summed, yield a single score (often expressed in environmental points, Pt, or milliPoints, mPt), which provides a simplified yet holistic representation of the overall environmental burden. This single score not only facilitates the comparison among different products, processes, or scenarios but also supports more transparent and informed decision-making in environmental management [51,52].
By reducing the complexity inherent in LCA outputs to a single actionable metric, decision makers are better equipped to prioritize environmental improvements and compare alternatives within broad sustainability frameworks, per Kalbar et al. [52]. Moreover, the process ensures that even though diverse impacts are involved, the final metric remains rooted in the actual environmental data, thereby maintaining the integrity and comprehensibility of the assessment [50]. In sum, the use of normalization and weighting to generate a single score serves as an effective tool to distill complex environmental data into a form that is both accessible and applicable across diverse decision-making contexts [51].
2.3.3. Economic Analysis
The analysis of expenses associated with the 4 case studies pertains to a single season and considers only operating costs (variable costs). Despite relying on certain assumptions and offering partial economic insights, comparing expenses and revenues across various technical and management scenarios helps evaluate tradeoffs between the economic, environmental, and social impacts of different production models.
The costs of the 4 management models were grouped as follows: (a) Fuel consumption, (b) Water, (c) Seeds, (d) Treatments, (e) Fertilizations, (f) Depreciation of equipment, and (g) Contractors. Incomes were estimated based on the official selling prices of conventional and organic rice in the Italian Bourse (autumn 2022). Appendix A, Table A1 provides detailed information about each cost category.
Fuel costs were aggregated regardless of the operation for which the fuel was used. Diesel fuel costs were calculated based on the market price and the tax relief regime as of January 2022, using average consumption coefficients for different tractors. This estimation was based on fuel consumption per operation provided by the National Union of Agricultural Contractors.
Water for the 4 farms comes from a local network managed by the Irrigation Consortium (or similar organization). Only the resource cost was considered, assuming uniform water supply system upkeep and provision to fields without active pumping.
Seed usage ranges between 180 and 200 kg per hectare, with costs from 130 euros for Baldo to 200 euros for Carnaroli (per 100 kg). Sowing is performed using rotary seed spreaders across all farms, with varying tractors affecting fuel consumption and costs.
Treatments in conventional farms include commercial herbicides, fungicides, and insecticides, whereas organic farms use approved fertilizers. Appendix A, Table A1 lists fertilizers used in the 4 farms, corresponding to common rice cropping practices. Organic farms use specific fertilizers like hoof horn and cow manure, with doses varying among farms and management models (see Appendix A, Table A1, Table A2, Table A3 and Table A4). Typical applications include diammonium phosphate at 150 kg/ha, potassium sulfate at 180 kg/ha, hoof horn at 250 kg/ha, and manure at 3000 kg/ha.
Yearly amortization per hectare of mechanical equipment was calculated based on a 15-year life span for tractors and operating machines serving different areas across the farms (see Appendix A, Table A1, Table A2, Table A3 and Table A4).
Some farms rely on contractors for tasks such as mowing, round baling, and harvesting. Cost estimations were based on 2022 regional contractors’ rates provided by the Italian Confederation of Agri-mechanics and Farmers, validated by the involved farmers and fuel consumption per operation supplied by UNCAI.
Gross income was calculated using official stock exchange prices for conventional and organic rice, specifically the Carnaroli variety. This income was then used to calculate the gross margin by subtracting variable costs. Notably, 2022 saw the selling price of organic rice nearly equal that of conventional rice due to a general shortage that disproportionately increased prices for both. Additionally, the 3 varieties (Baldo, Carnaroli, and Rosa Marchetti) have different average yields per hectare. The unique price context of 2022 influenced the differential margin results between conventional and organic farms.
3. Results
3.1. Sustainability Assessment of Case Studies (OASIS)
Table 3 and Figure 3 show the overall scores for the five indicators selected to describe the agroecological performances of the target farms. Appendix A, Table A4 contains further details concerning the chosen criteria and the list of indicators used to commute the overall scores.
Concerning the adoption of agroecological practices, RO leads in agroecological adoption with a score of 3.45. It uses an innovative green mulch system, sowing rice over a Lolium multiflorum cover crop, and avoids mechanical weeding post-seeding. Weed suppression is achieved via the allelopathic effects of the cover crop. Fertilization is purely organic, and pest management relies solely on sulfur-based treatments.
BO, scoring 3.15, follows traditional organic principles: dry broadcast seeding after early flooding, mechanical and manual weed control, and organic manure application. However, its heavier dependence on contractors for key operations like harvesting slightly limits its agroecological independence.
BC (2.50) and RC (2.15) score lower. BC applies chemical herbicides (Alcance, Pendimetalin pre-emergence; Beyond, Dash, Marins, Permit post-emergence). It uses a fully mechanized dry-seeding system without any biological pest control measures. Similarly, RC uses chemical weed control in a flooded environment, and heavy synthetic NPK fertilization, without any agroforestry or ecological diversification.
Concerning the economic aspects, BC and RC lead economically with scores of 4.01 and 3.99, respectively. BC achieves high returns by optimizing mechanization (tractors, seeders, sprayers, and harvesters). No contractors are used, keeping operations efficient. However, profitability depends on heavy external inputs (fertilizers, pesticides). RC maintains an extensive tractor fleet, ensuring full in-house operations. It broadcasts rice into flooded fields after puddling, using NPK fertilizers and fungicides like Amistar. Its diversified local marketing of conventional rice boosts profits, despite high energy and labor costs.
RO (3.72) and BO (3.63) maintain moderate economic performance. RO achieves short supply chain marketing, adding value through niche organic products. It independently manages production with modern organic rice machinery. BO, though compliant with organic standards and using organic-certified Baldo seed, depends on contractors for harvesting, which slightly increases operational costs.
Concerning the sociopolitical aspects, RC ranks highest (4.07) due to strong labor contracts, training participation in agroecology circuits, and the use of culturally significant rice varieties. However, its engagement in social cooperation networks is limited. BC (4.05) also scores well due to respect for labor conditions but shows low involvement in enhancing local markets or traditions. BO (3.99) and RO (3.97) foster stronger community and cultural networks. RO, in particular, emphasizes ancient rice varieties and biodiversity and actively cooperates with local networks. Nonetheless, the intense workload in RO, due to fully autonomous operations, negatively affects the quality of life of workers. BO implements participatory work management but relies externally for machinery-intensive phases like harvesting.
Concerning the impact on the environment and biodiversity, RO achieves the highest environmental impact score (3.37) through its green mulching system, organic fertilization, sulfur-based treatments, and maintenance of woodlands and hedgerows. However, soil compaction issues from harvesting machinery partially lower its environmental benefits. BO (3.20) uses mechanical weed control and early flooding and avoids synthetic inputs, enhancing biodiversity and carbon management. Like RO, its environmental impact is slightly limited by infrastructural gaps (e.g., no water storage). BC (3.02) reaches average levels by maintaining traditional irrigation structures but suffers from soil erosion and salinization risks due to heavy reliance on chemical products and the absence of ecological buffers. RC, scoring lowest (2.50), is the scenario exhibiting the highest environmental impact scores: heavy synthetic fertilizer application, monoculture, no wetlands or tree areas, and total mechanical reliance.
Finally, concerning the resilience of the farming activity, BO and RO show the highest resilience scores (3.74 and 3.72). BO strengthens its resilience via cover cropping, seed recycling, mechanical weed control, and diversified products. RO shows high climate resilience through its green mulch system and reduced input dependency, although resilience is slightly limited by the fragility of rice varieties and lack of water reserves. BC (3.67) benefits from infrastructure and consistent yields, though high input dependency compromises ecological resilience. RC, scoring 2.83, exhibits severe weaknesses. Despite high mechanization, its conventional monoculture model, chemical reliance, and rigid market demands (focused on white rice) reduce farm autonomy and expose it to climate and market stresses.
3.2. Environmental Sustainability
Life Cycle Analysis
Figure 4 presents a comparative analysis of the four rice management scenarios (BO, BC, RO, RC) using the EF 3.1 method. The analysis is expressed as a percentage on the vertical axis, while the horizontal axis represents each impact category. For each category, the scenario with the highest impact is set at 100%, and the other scenarios are compared to this value.
The results demonstrate that the scenarios exhibit diverse environmental performance across all impact categories. Upon examination of the raw data from the comparison, an analysis of the average impact percentages in all categories indicates that RC has the highest average impact (88%), followed by BC (80%), RO (70%), and, finally, BO (48%). This indicates that, in general, the RC scenario has the highest environmental impact.
However, when examining the individual scenarios and specific impact categories, the BO scenario exhibits lower impacts than the others in the majority of impact categories (15 out of 16), with the exception of OD, where the best performance is represented by the scenario RO. A comparison of the four scenarios reveals an increasing trend between the organic and conventional scenarios in all impact categories. In at least one of the conventional scenarios (BC and RC), the impact levels are higher than in the organic scenario (BO and RO), apart from the WU impact category, where the water management for RO is almost 40% more than the conventional.
Figure 5 focuses on the gravity analysis within different scenarios. The analysis shows that soil preparation and cultivation activities represent the largest contributions of environmental impact (BO 49%, BC 37%, RO 46%, and RC 40%). The second and third largest contributors vary depending on the scenario being considered. In the BO, RO, and RC scenarios, field emissions have an average between impact categories of 20–33% and represent the second contributor. Conversely, in the BC scenario, the second contribution comes from the fertilizer production of 24%.
Key contributing factors vary by scenario across environmental categories such as Acidification (ACID), Climate change (CC), Freshwater eutrophication (EU-FW), Terrestrial eutrophication (EU-TERR), Land use (LU), and wWter use (WU). (For a complete list of acronyms used in LCA, please see Appendix A, Table A6). In the ACID and CC impact categories, field emissions are the predominant contributor in all scenarios, driven by ammonia volatilization, with the highest impacts observed in scenarios RO and RC at 70–80%. Water management in both farms causes significant field emissions, and mitigation strategies should include alternate wetting and drying irrigation practices. Only in the case of BC the impact on climate change is explained by fertilizer production, i.e., the main contributor to climate change is represented by fertilizer production with 39% responsibility. In the ETX-FW impact category, the differences between organic and conventional scenarios are expressed by different factors. Major differences between BC and BO are related to fertilizers (BC shows 53 times more emissions than BO). In contrast, soil preparation and cultivation, field emissions, and seeds factors showed no significant variations. The EU-TERR impact category shows a lower trend, where the organic scenarios (BO and RO) showed half of the values of the conventional scenarios (BC and RC). Although the contributions in the WU impact category are derived entirely from the management of the water resource to irrigate the fields and to keep them flooded for the required periods, in fact, keeping the paddy field flooded for a longer time, and the number of flood cycles, led to the RO scenario representing the worst case.
To drive eco-design of rice cultivation management systems and reach more sustainable cultivation strategies, it is fundamental to focus on the factors analyzed. If one of the current paper’s aims is to compare different field management systems, yield (as suggested in the PCR) should be considered in the analysis, as different field management produces different results. Single score results can help identify the best-case scenario, also considering the maximum yield, as the same CPC product (rice) is cultivated in different conditions.
Analyzing Figure 6, the environmental impact of 1 ha of field management systems (reported in blue columns) shows that the Bereguardo management system’s organic field management is better than the conventional field management. When focusing on the Rovasenda management systems, conversely, the conventional field management represents a better environmental footprint than the organic field management.
However, when considering LCA results according to the FU “yield (tons/ha)”, conventional field management scenarios (BC and RC) obtain higher scores.
When the yield produced is considered as an FU, conventional scenarios represent the best solution in both the Rovasenda and Bereguardo farms, regardless of sustainable field management practices.
It may seem a paradox to reach a merged consideration between products’ and organizations’ sustainable field practices, but the two perspectives are essentially expression of two different standpoints, one more interested in narrowing the analysis on the sole provision of food, and the other more interested in informing holistic knowledge about the whole set of ecosystem services provided by agricultural farms and landscapes. Both perspectives are important to satisfy the need for increased efficiency and the economic return in production processes and to safeguard the environment and the community.
3.3. Gross Margin Analysis
The economic analysis produced the results in Table 4, showing different economic returns for the different production models and farms. On average, the highest gross margin is for the conventional farming models; namely, RC shows a gross profit (4379.42 €/ha, gross margin 74.86%) higher than the BC farm (2649.99 €/ha, gross margin 70.11%). Nevertheless, it should be underlined that the organic farming model allows the two farms to earn a gross profit of 1880.56 €/ha and 1318,01 €/ha, respectively, showing a gross margin equal to 56.02% and 52.72% only.
In 2022, low rice yields in Italy led to minimal price differences between organic and conventional rice. The overall shortage raised prices for both types nearly equally. This influenced the study’s results, making the gross margins between conventional and organic farming appear less distinct than usual. Consequently, the economic benefits of organic farming might seem understated compared to a typical year, where the price premium for organic rice would be more significant.
As shown in Figure 7, the main costs vary across the four scenarios. In general terms, the consumption of diesel is the most relevant cost for all farms, while the second most relevant cost is represented by seeds for the organic farms and by treatments or equipment depreciation for conventional farms. Fertilization counts for an important share for all farms, ranging from 8.2% for RC to 18.6% for BC. The irrigation consortium fee was equal for all the scenarios, and all four farms use direct surface water withdrawal without pumping, so water management impacts 3.4–4.4% of the total cost across the four target farms.
In particular, in the BO scenario, the most relevant cost is represented by seed provision (33.8%), followed by diesel consumption (25.1%), contractors fee (14.8%), and equipment depreciation (9.3%). In fact, BO uses an organic-certified, expensive (200 euro/100 kg) Baldo rice variety, with a dose of 200 kg/ha, which is higher than the dose used by the other farms (from 180 to 190 kg /ha). BO is also the only farm in the sample that hires contractors, whose cost negatively impacts the gross margin obtained.
In the BC scenario, the cost of fuel, seeds, and treatments are comparable (23.7%, 20.7%, and 22.8%, respectively), followed by the cost of fertilization (18.6%) and equipment depreciation (9.8%). BC is conventional and implemented, also in a not-ideal and dry-cropping season, with three fertilizations and three treatments (see Appendix A, Table A1A–D), and uses high doses of chemical fertilizers (450 kg/ha divided into two interventions) and treatments (chemical weeding with Beyond 1.1 L/ha + Dash 1 L/ha + Marins 1.5 L/ha + Permit 0.05 kg/ha and fungicide one L/ha).
In the critical RO scenario, the highest cost per hectare is seed provision (27.3%), followed by diesel consumption (25.0%) and equipment depreciation (21.9%). Following the organic model, fertilization counts for 15.2% and treatment for only 6.8% of the total cost per hectare.
The RC scenario shows its highest share of cost for equipment depreciation (32.2%), followed by diesel consumption (23.9%), treatment, and seed costs (18.8% and 13.6%, respectively). In fact, RC does not use contractors for any of the farming activities and relies on owned machinery, showing the highest number of tractors within the sample.
Overall, yield-based functional units favor intensive systems, while area-based FUs align with ecosystem service assessments. A multi-FU approach may better capture tradeoffs.
4. Discussion
Integrated Sustainability Assessment
The adopted mixed method allows an overall evaluation of farm sustainability by considering, comparing, and cross-checking the results from different methodologies from the environmental and economic standpoints, which are indeed the most debated dimensions when dealing with the comparison of the organic and conventional farming systems. Moreover, the adoption of a holistic methodology such as OASIS includes the analysis of the social dimension regarding the four target scenarios and informing the assessment of the tradeoffs between the impacts of the studied farming models across the three main dimensions of sustainability.
In fact, according to FAO [13], organic agriculture demonstrates comparable or even superior economic performance relative to conventional systems, particularly when evaluated over the full crop rotation and whole-farm production rather than only primary production. Profitability is supported by price premiums, lower input costs, and diversified production. Socially, organic agriculture encourages community cooperation, revitalizes local knowledge, and can promote fair working conditions, especially when aligned with fair trade standards. Crop diversification in organic systems helps stabilize employment throughout the season, reduces turnover, and spreads economic risk. At the same time, market dynamics and price premiums are influenced by both demand and supply-side factors, with costs for certification, segregation, transportation, and limited economies of scale contributing to higher retail prices. Our integrated sustainability assessment seems to confirm previous evidence.
Regarding the different results proposed with the three methodological approaches in the previous sections, the conventional and organic scenarios were averaged to propose a benchmark to provide an overall evaluation of the sustainability of rice cultivation in the four scenarios (Figure 8). The radar graph below proposes how the scores obtained by each target farm may be rearranged and measured against the best scenario, which is different for each indicator. Value “1” indicates that the farm (or management system) reaches the best performance within the target group.
From an economic point of view, the results of the gross margin analysis and OASIS seem to mutually confirm the positioning between the farms and the production models. However, the two methodologies highlight differences in distinct ways. While from the gross margin analysis standpoint there is a clear gap of 25% in favor of conventional farms, which outperform the organic ones, the gap reduces when considering the OASIS results. However, it should be highlighted that 2022 was a very particular year where the prices of paddy rice on the market, both for organic and conventional, became very close. It means that normally the organic price is much higher than the conventional one, and therefore in “normal” years, the gap between the two gross margins would have been decidedly smaller. Moreover, concerning comparing the two adopted methodologies, the gross margin method seems to highlight the reduction of revenue for the organic model. In contrast, the OASIS indicator of economic aspects seems to capture a less relevant difference. This difference between the results of the two methodologies is attributed to the type and diversity of data elaborated. In fact, OASIS considers other facets of the company’s economic behavior in addition to the difference between variable costs and revenue. Among the aspects not considered by the gross margin, an indicator relating to fixed costs has to be underlined. Furthermore, we can also mention the economic treatment of workers and the ways in which income is generated and transformed into profit, such as the type of market used, food processing on a farm scale, and the comparison, from a farmer’s perception, with existing trends in the study area. In other words, the OASIS methodology captures more qualitative information, showing that reduced gross margins may be offset by how income is generated and managed. According to previous studies, the measurement of economic benefits based on qualitative and context-specific perception by the farmer are rare in the literature but still valuable in centering farmer values when generating indicators of wellbeing [53]. Moreover, according to previous reviews, the analysis of farm economic outcome can be successfully implemented through mixed methods that are able to capture both quantitative and qualitative aspects of economic outcome and take into account both farmer-centered and quantitative/objective measures such as the more recurrent indicators for farm economic viability (i.e., gross margin, Return on Equity, Debt Ratio, Net Return, and Output to Economic Size Unit Ratio) [54,55].
Our findings that economic factors are highly influential in overall farm viability align with Orounladji et al. [54], who also found that the economic dimension carried the most weight in farmers’ opinions regarding farm viability. However, Orounladji et al. [54] notably suggested that medium-sized farms (T2) achieved the “best balance” between agroecology and viability, even if not explicitly the most profitable, by effectively managing land and livestock without the challenges associated with very large operations. This mirrors the nuanced picture revealed by our integrated assessment, suggesting that singular economic metrics might not fully capture the complex balance of sustainability.
Concerning sociopolitical outcomes, the four farms in the study yielded similar sociopolitical outcomes, though one organic farm notably reduced the sub-group mean due to the intensive workload associated with organic management. This outcome should be interpreted within the context of the study’s setting: one of the wealthiest agricultural areas in Italy, renowned for its profitable rice value chain. In such an affluent region, the introduction of organic practices, with their associated labor intensiveness, can provoke significant local discussion regarding labor, environmental sustainability, and market demands. These discussions are part of broader sociopolitical dynamics where farming practices intersect with societal expectations for sustainability and community wellbeing [56].
Farms in areas like this are not only economic units but also hubs of social interaction, where political factors like environmental regulations and land use policies can have profound effects. The sociopolitical implications of farming extend beyond the farm gate, influencing local policies on land management, rural development, and even health regulations. As noted in the OECD report on social issues in agriculture, the evolving nature of farming systems—especially those that integrate environmental sustainability—can create tensions in rural areas, particularly when such practices challenge established agricultural norms or economic interests [56]. The shift towards organic farming, for example, may lead to the rise of new labor demands, as well as influence policies on farm subsidies and public funding for agricultural innovation.
Moreover, the rich agricultural context of this study is framed by a complex interaction of internal and external social factors that affect how farms interact with their communities. For instance, the demand for locally produced foods and organic products is tied to broader external social factors such as increasing consumer awareness and environmental concerns. These concerns are particularly relevant in affluent agricultural regions, where local populations increasingly value environmental quality and the traceability of food production. This shift can challenge the dominance of industrial farming practices and reshape local political priorities, influencing decisions on subsidies, land conservation programs, and the regulatory landscape governing farming practices [37,57].
In such a politically active and affluent region, the push towards sustainable farming practices—exemplified by the organic farm’s management style—also intersects with broader discussions on food security and public health. The OECD report highlights that the transition towards more sustainable farming systems often involves contentious debates about resource allocation, labor rights, and the role of public institutions in supporting these changes [56]. These sociopolitical discussions shape the local agricultural policies and influence farmers’ decisions on whether to adopt or resist such practices. However, with OASIS being the only available data on the sociopolitical outcomes in this study, triangulation with other methodologies remains a limitation, especially in light of these broader sociopolitical dynamics at play.
These findings align with a previous study [58], which emphasizes the importance of “Harmonization of Policies at Various Scales” for successful organic rice production, highlighting the need for comprehensive policies from local to national levels to support green markets and tax incentives. The intense workload observed in the RO farm also corresponds to the previous literature discussion on the physical requirements of organic rice production and the impact on the quality of life of workers. This indicates that the social costs, such as labor intensity, are a consistent factor in organic transitions across different contexts. Furthermore, another previous study [54] identified a “political” dimension of viability, relating to difficulties in accessing and managing non-owned agro-pastoral resources and increased disputes over resource use due to population pressure. This reinforces our observation that broader sociopolitical factors and resource governance are integral to farm sustainability, especially in areas with competing land uses and high population densities
Conversely, from an environmental perspective, LCA and OASIS yield distinct and sometimes contrasting results. Concerning the LCA comparison, and the total impact (single score) of target farms and scenarios, the results swap if we consider the functional unit of area (ha) and produce (ton). On one hand, it emerges that if one ton of product is regarded as the functional unit, the conventional model impacts significantly less as it manages to have a higher efficiency rate, i.e., input/output rate is lower. The impact is spread across several tons of production. On the other hand, if we consider the hectare as the functional unit, the result changes and the average single score for organic and conventional models overlaps. It is because BO turns out to be the farm that shows the least impact concerning the surface area, and even if RO remains the worst, it means that the organic model has potential for improvement as a whole. RO’s result is mainly due to the mismanagement of water, which has a highly relevant signature in terms of GHG emissions in rice cropping [59,60,61,62,63]. In particular, even if RC and RO show similar durations of submersion, RO adopts an additional flooding, which entails prolonged submersion and consequent methane emissions and additional use of irrigation water. Comparing the farms on a geographical base, it should be noted that the Bereguardo farms in 2022 suffered from a nearly total lack of water, while in the Rovasenda area, water was more available, depending on the relative position of the farm within the local irrigation system of canals. It suggests that one possible reason for RO to implement additional flooding, compared to RC, was the simple availability of water for this specific farm, against any environmental consideration. These specific features of the studied systems may explain why the available literature on LCA comparisons of organic and conventional systems does not confirm the swapping [29,64,65,66]. Indeed, recent systematic reviews confirm that the choice of functional unit is crucial in comparative LCA studies of organic and conventional systems, as it can lead to different conclusions about environmental performance [66]. Similarly, Hashemi et al. found that organic food generally has lower environmental impacts per area unit but similar climate impacts per mass unit compared to conventional systems, reinforcing the importance of FU selection based on study goals [65].
It is also important to say that choosing a mass-related or area-related functional unit is widely accepted to be a fundamental decision in LCA studies focusing on comparing the organic and conventional models. According to Matthews et al. (Matthews, Matthews, and Hendrickson 2014) [67], FUs should include several dimensions, which are the responses to “What?”, “How much?”, “How well?”, “For how long/how many times?”, and “Where?” by the object of study. In our case, we suggest giving a higher weight to the area-related FU because we are looking at agricultural activities as multifunctional production systems, not limited to the provision of food. In other words, choosing the area-related FU allows us to account for the other ecosystem services provided by the act of cropping within a landscape and a socioecological system.
The comparison with OASIS results supports this perspective. In fact, under the frame of OASIS analysis, it emerges that from the agroecological practices adoption standpoint, organic farms are decidedly more advanced than conventional ones, particularly RO. As already witnessed by the analysis of economic aspects, the adoption of agroecological practices entails a tradeoff between economic and “other” yields [68].
In fact, a clear tradeoff pattern can be seen in terms of biodiversity (left side of the graph), where the two organic farms outperform the conventional ones, with both the organic and conventional farms obtaining scores near the respective sub-group means. This is once more in contrast with the results of LCA, namely with the results expressed as a mass-related FU. The clear distinction of preferability between conventional and organic management systems that occurs when adopting the mass-related FU cannot be seen in the case of an area-related FU. The crevasse between the mass-related and the area-related FU between organic and conventional is due to the complexity and variability of choices that the farmers face during the rice cultivation season and could be explained in terms of tradeoffs in biodiversity indicators, as captured by the OASIS methodology. LCA methods, while accounting for fertilizers and pesticides from a GHG emission standpoint, overlook significant risks to various organisms. These impacts affect vertebrates in rice fields and surrounding habitats, as shown in studies from the Philippines [69] and South America [70]. Other previous studies report that changes in irrigation structures further disrupt ecosystems, impacting fauna, and that rice field ecosystems host diverse primary producers, including over 1800 weed species, with algae being vital for soil fertility, highlighting the complex ecological dynamics within these agroecosystems [71].
This emphasis on biodiversity aligns strongly with Panpakdee [58], who identifies “Biodiversity for Protection and Restoration” as a key social–ecological resilience indicator for organic rice production, noting its contribution to food security, alternative income sources, natural pest control, and enhanced nutrient cycling through the presence of diverse plant and animal species and ecological systems. This supports our finding that organic farms show superior performance in biodiversity, underscoring the limitations of LCA in fully capturing these multi-faceted ecological benefits.
A similar tradeoff pattern can be acknowledged concerning farming resilience. Here the BO, RO, and RC farms show similar scores, higher than BC and contributing to the best average outcome of the organic versus conventional model. Building on the OASIS indicators contributing to the overall resilience score, these results confirm previous findings on the ability of organic approaches to increase farm resilience [58,72,73,74,75].
On one hand, the fact that the two organic farms obtain the same resilience score as the RC farm indicates the possibility for conventional farming to adopt resilient measures that balance improvements in climatic and economic resilience at the same time. On the other hand, organic farms show the best scores in climatic resilience, adopting a larger set of practices that, as a whole, provide a larger contribution to the farming system, i.e., the diversity of their products and income sources, the self-production of seeds, the choice of culturally relevant varieties, and the adoption of crop rotations and cover crops.
These findings are supported by previous research [72], which defines resilience as the ability of a system to anticipate, absorb, accommodate, or recover from hazardous events. They further emphasize that farmers who can count on a suitable “Natural Capital” component (e.g., soil fertility, water availability) are positively influenced in their readiness to adopt more organic practices. Our observation of higher climatic resilience in organic farms, especially during the 2022 drought, directly corroborates this link, as organic practices foster improved soil structure and water retention, enhancing natural capital.
5. Conclusions
In conclusion, the present research provides a snapshot of four farms with different management models and different availability and management of water during a drought year, i.e., 2022, in the Po Valley. We adopted a mixed method approach to analyze several features simultaneously and highlight how different methodologies can explain different shades of sustainability. The results show that the existing tradeoffs in terms of economic and environmental can be explained when the same issue is assessed through a more specialized and holistic method and that a margin of improvement exists both in organic and conventional schemes. However, the proposed mixed method approach was able to provide a more holistic view and to analyze all the three dimensions of sustainability, helping to understand the tradeoffs and set a goal path for sustainable management of rice cropping.
In general, we conclude that, with regards to agroecological aspects, environmental impact and biodiversity, and resilience, organic farms are more advanced and better projected towards the sustainability objectives of the European Union and the Agenda 2030. This observation is especially important considering that it was obtained in the driest year in the last century and a half. These unusual weather conditions, which are likely to become more frequent due to climate change, reveal the greater adaptive capacity of organic compared to conventional paddy systems.
From an economic point of view, conventional farms confirm their better performance, even if the specific weather conditions in 2022 limited the added consumer price for organic farms. The 2022 markets for organic and conventional paddy rice showed very similar selling prices due to lack of product. Consequently, the higher production of conventional rice has led to significant margins not compensated by the usual organic added value.
While the choice of 2022 as the reference year may introduce specific contextual influences, it was a deliberate methodological decision aimed at highlighting the differential responses of organic and conventional systems under drought-prone conditions. While this approach allowed us to capture system-level responses under a high-stress scenario, it also limits the generalizability of the results across more typical years. Future research could expand this analysis by incorporating multi-year comparisons or sensitivity analyses to better isolate the contribution of individual factors such as climate variability, input prices, and yield dynamics.
Overall, water use remains the most critical issue for both environmental impact and system resilience in rice cultivation. Building on the case study of RO, we may conclude that the highest environmental impact is largely attributable to the additional, and probably not justified, submersion it adopts compared to RC, which had the same availability of water in Rovasenda but did not implement the third flooding. Conversely, in Bereguardo, where water was not available, the organic and conventional farms obtained similar impacts. This is a very interesting conclusion, because it paves the way for new studies on water management in relation to the production and fundamental phenological phases of rice.
Limitations of the Study
Limitations of the study stem primarily from the small sample size— only four farms assessed—which restricts the statistical robustness and generalizability of the findings. Additionally, the systems analyzed differ significantly in both geographical context and technical implementation, complicating direct comparisons between organic and conventional models. This complexity is further compounded by the variability in farm management intensity, market access, and resource availability—factors that critically influence the performance and adoption of organic practices, especially in diverse socio-economic settings. To ensure a more comprehensive and nuanced understanding of sustainability tradeoffs, future research should expand the number of case studies across varied agroecological zones and integrate additional methodological frameworks. Incorporating tools such as Social Life Cycle Assessment (S-LCA) and biodiversity-specific evaluation methods within the OASIS framework would enable a more holistic triangulation of social and ecological impacts. Moreover, while the focus on an exceptionally dry year represents a key strength of this study—allowing for the observation of unique dynamics, such as the physiological reduction in inputs and operations often seen in conventional systems under climatic stress—it also introduces important limitations. The specificity of this climatic context, combined with the short temporal horizon, may not fully capture the systemic and long-term nature of organic farming, which relies on multi-year crop rotations, ecological feedback loops, and the accumulation of soil and ecosystem health benefits over time. As such, integrating longer-term farm trajectories and broader market dynamics would provide a more accurate and comprehensive understanding of sustainability tradeoffs. Addressing these aspects is crucial to support more robust and evidence-based pathways for sustainable agrifood transitions.
Author Contributions
Conceptualization, S.G., V.V. and S.B.; methodology, V.V. and S.B.; software, R.G.; validation, D.M.P., V.V. and S.B.; formal analysis, S.G., C.A. and A.D.N.; investigation, S.G.; resources, S.B. and R.G.; data curation, S.G. and D.M.P.; writing—original draft preparation, D.M.P., S.G., C.A. and A.D.N.; writing—review and editing, D.M.P.; visualization, S.G. and D.M.P.; supervision, S.B. and R.G.; project administration, S.B.; funding acquisition, S.B. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Data available on request.
Acknowledgments
We would like to acknowledge the spirit of collaboration showed by the four farmers involved in the interviews and data collection. Furthermore, we acknowledge that during the preparation of this manuscript/study, the author(s) used Google AI Studio for the purposes of improving the English language of the manuscript. The authors have reviewed and edited the output and take full responsibility for the content of this publication.
Conflicts of Interest
The authors declare no conflicts of interest.
Abbreviations
The following abbreviations are used in this manuscript:
| ACID | Acidification |
| ARPA | Regional Agency for Environmental Protection |
| BC | Bereguardo Conventional |
| BCA | Biological Control Agents |
| BO | Bereguardo Organic |
| CC | Climate change |
| CCIAA | Chamber of Commerce, Industry, Crafts, and Agriculture |
| CH4 | Methane |
| CO2eq | CO2 equivalent |
| EBITDA | Earnings Before Interest, Taxes, Depreciation, and Amortization |
| EF | Environmental footprint |
| EN | Ecological Networks |
| ETX-FW | Ecotoxicity, freshwater |
| EU | European Union |
| EU-FW | Eutrophication, freshwater |
| EU-MAR | Eutrophication, marine |
| EU-TERR | Eutrophication, terrestrial |
| FAO | Food and Agriculture Organization |
| FAOSTAT | FAO Statistics Division (database) |
| FTE | Full-Time Equivalents |
| FU | Functional unit |
| GHG | Greenhouse gas |
| HNVf | High-Nature Value farming |
| HT-C | Human toxicity, cancer effects |
| HT-NC | Human toxicity, non-cancer effects |
| IPCC | Intergovernmental Panel on Climate Change |
| IPM | Integrated Pest Management |
| IR | Ionizing radiation |
| ISO | International Organization for Standardization |
| ISPRA | Italian National Institute for Environmental Protection and Research |
| ISTAT | Italian National Institute of Statistics |
| LCA | Life Cycle Assessment |
| LU | Land use |
| N-P-K | Nitrogen–Phosphorus–Potassium |
| OASIS | Original Agroecological Survey and Indicator System |
| OD | Ozone depletion |
| OECD | Organisation for Economic Co-operation and Development |
| PCR | Product Category Rules |
| PGS | Participatory Guarantee Systems |
| PM | Particulate matter |
| POF | Photochemical ozone formation |
| Pt/mPt | Points / milliPoints |
| PV | Province of Pavia |
| RC | Rovasenda Conventional |
| RO | Rovasenda Organic |
| RU-F | Resource use, fossils |
| RU-MM | Resource use, minerals and metals |
| S1 | Supplementary data 1 |
| SAFA | Sustainability Assessment of Food and Agriculture systems |
| S-LCA | Social Life Cycle Assessment |
| SRP | Sustainable Rice Platform |
| TAPE | Tool for Agroecology Performance Evaluation |
| TFI | Treatment Frequency Index |
| UAA | Utilized Agricultural Area |
| UNCAI | National Union of Agro-mechanical and Industrial Contractors |
| VC | Province of Vercelli |
| WU | Water use |
Appendix A
Table A1.
(A) Cropping practices: operations and products used in Bereguardo Organic—BO; (B) Cropping practices: operations and products used in Bereguardo Conventional—BC; (C) Cropping practices: operations and products used in Rovasenda Organic—RO; (D) Cropping practices: operations and products used in Rovasenda Conventional—RC.
Table A1.
(A) Cropping practices: operations and products used in Bereguardo Organic—BO; (B) Cropping practices: operations and products used in Bereguardo Conventional—BC; (C) Cropping practices: operations and products used in Rovasenda Organic—RO; (D) Cropping practices: operations and products used in Rovasenda Conventional—RC.
| A—Practices in BO | ||||||
|---|---|---|---|---|---|---|
| OPERATION | INPUT | PRODUCT | AMOUNT | UNIT | FUEL | UNIT |
| Manuring | Manure | Manure | 30 | Ton/ha | 30 | L/ha |
| Plowing | 1 | 70 | L/ha | |||
| Harrowing | 2 | 20 | L/ha | |||
| Sowing | Seed | Baldo rice seed | 200 | Kg/ha | 10 | L/ha |
| Pruning machine | 1 | 20 | L/ha | |||
| Harvest | 2.5 | Ton/ha | 50 | L/ha | ||
| B—Practices in BC | ||||||
| OPERATION | INPUT | PRODUCT | AMOUNT | UNIT | FUEL | UNIT |
| Manuring | Manure | Manure | 30 | Ton/ha | 30 | L/ha |
| Harrowing | 1 | 25 | L/ha | |||
| Sowing | Seed | Barone rice seed | 180 | Kg/ha | 10 | L/ha |
| Pre-emergency weed control | Alcance | Clomazone 43 g/L | 2.8 | L/ha | 36 | L/ha |
| Pendimetalin 298 g/L | L/ha | |||||
| Fertilization | NPK 23-0-30 | NPK 23-0-30 | 225 | Kg/ha | 10 | L/ha |
| Post-emergency weed control | Beyond | Imazamox 40 g/L | 1.1 | L/ha | 36 | L/ha |
| DASH | Metil-oleato e metil-palmitato 37.5 g | 1 | L/Ha | |||
| MARINS | MCPA 200 g/L | 1.5 | L/ha | |||
| PERMIT | Halosulfuron metile 750 g/kg | 0.05 | Kg/ha | |||
| Fertilization | Npk 23-0-30 | Npk 23-0-30 | 225 | Kg/ha | 10 | L/ha |
| Fungicide | Amistar | Azoxystrobin puro 250 g/L | 1.0 | L/ha | 36 | L/ha |
| Harvest | 4.5 | Ton/ha | 50 | L/ha | ||
| C—Practices in RO | ||||||
| OPERATION | INPUT | PRODUCT | AMOUNT | UNIT | FUEL | UNIT |
| Plowing | 1 | 70 | L/ha | |||
| Fertilization | Hoof and horn | Nitrogen 14% | 250 | Kg/ha | 10 | L/ha |
| Harrowing | 2 | 25 | L/ha | |||
| Leveling | 1 | 12 | L/ha | |||
| Sowing | Seed | Ryegrass seed | 50 | Kg/ha | 10 | L/ha |
| Fertilization | Potassium sulfate | Potassium 50% | 180 | Kg/ha | 10 | L/ha |
| Sowing | Seed | Carnaroli rice seed | 180 | Kg/ha | 10 | L/ha |
| Fungicide | Thiopron | Sulfur | 5 | L/ha | 36 | L/ha |
| Fungicide | Thiopron | Sulfur | 5 | L/ha | 36 | L/ha |
| Fungicide | Thiopron | Sulfur | 5 | L/ha | 36 | L/ha |
| Harvest | 2 | Ton/ha | 50 | L/ha | ||
| D—Practices in RC | ||||||
| OPERATION | INPUT | PRODUCT | AMOUNT | UNIT | FUEL | UNIT |
| Plowing | 1 | 70 | L/ha | |||
| Leveling | 1 | 12 | L/ha | |||
| Harrowing | 1 | 25 | L/ha | |||
| Fertilization | Diammonium phosphate + Hoof and horn | 18% Nitrogen 46% Phosphorus 14% Nitrogen | 180 330 | Kg/ha Kg/ha | 10 | L/ha |
| Pre-sowing weed control | Stratos | Cycloxydim | 2.2 | L/ha | 36 | L/ha |
| Sowing | Seed | Carnaroli rice seed | 190 | Kg/ha | 10 | L/ha |
| Post-emergency weed control | Aura | Profoxydim | 0.3 | L/ha | 36 | L/ha |
| DASH | Metil-oleato e metil-palmitato 37.5 g | 0.9 | L/Ha | |||
| CLINCHER ONE | Cialofop-buthyl 200 g/L | 1.5 | L/ha | |||
| KARATE | Lambda cyhalothrin 9.48 g | 0.125 | L/ha | |||
| Fungicide | Amistar | Azoxystrobin puro 250 g/L | 1.0 | L/ha | 36 | L/ha |
| Fungicide | Amistar | Azoxystrobin puro 250 g/L | 1.0 | L/ha | 36 | L/ha |
| Harvest | 4.5 | Ton/ha | 50 | L/ha | ||
Table A2.
(A) An Irrigation calendar for the four target farms in 2022: Bereguardo Organic—BO; (B) Irrigation calendar for the four target farms in 2022: Bereguardo Conventional—BC; (C) Irrigation calendar for the four target farms in 2022: Rovasenda Organic—RO; (D) Irrigation calendar for the four target farms in 2022: Rovasenda Conventional—RC.
Table A2.
(A) An Irrigation calendar for the four target farms in 2022: Bereguardo Organic—BO; (B) Irrigation calendar for the four target farms in 2022: Bereguardo Conventional—BC; (C) Irrigation calendar for the four target farms in 2022: Rovasenda Organic—RO; (D) Irrigation calendar for the four target farms in 2022: Rovasenda Conventional—RC.
| A—Water Management in BO | ||||
|---|---|---|---|---|
| OPERATION | START | FINISH | SUBMERSION DAY | DRY DAY |
| Sowing | 30/05/2022 | 30/05/2022 | ||
| Submersion 1 | 20/06/2022 | 23/06/2022 | 3 | |
| Dry 1 | 21/06/2022 | 09/07/2022 | 15 | |
| Submersion 2 | 10/07/2022 | 13/07/2022 | 3 | |
| Dry 2 | 14/07/2022 | 29/07/2022 | 15 | |
| Submersion 3 | 30/07/2022 | 02/08/2022 | 3 | |
| Harvest | 22/10/2022 | 22/10/2022 | 39 | |
| Tot | 10/09/2022 | 10/09/2022 | 9 | 69 |
| B—Water Management on BC | ||||
| OPERATION | START | FINISH | SUBMERSION DAY | DRY DAY |
| Sowing | 15/04/2022 | 15/04/2022 | ||
| Submersion 1 | 12/05/2022 | 15/05/2022 | 3 | |
| Dry 1 | 16/05/2022 | 03/06/2022 | 18 | |
| Submersion 2 | 04/06/2022 | 07/10/2022 | 3 | |
| Dry 2 | 07/06/2022 | 25/06/2022 | 18 | |
| Submersion 3 | 26/6/2022 | 02/07/2022 | 6 | |
| Harvest | 10/09/2022 | 10/09/2022 | 70 | |
| Tot | 12 | 106 | ||
| C—Water Management on RO | ||||
| OPERATION | START | FINISH | SUBMERSION DAY | DRY DAY |
| Sowing | 28/04/2022 | 28/04/2022 | ||
| Submersion 1 | 28/04/2022 | 10/05/2022 | 12 | |
| Dry 1 | 11/05/2022 | 26/05/2022 | 15 | |
| Submersion 2 | 27/05/2022 | 29/07/2022 | 63 | |
| Dry 2 | 30/07/2022 | 06/08/2022 | 7 | |
| Submersion 3 | 07/08/2022 | 06/09/2022 | 30 | |
| Harvest | 03/11/2022 | 03/11/2022 | 58 | |
| Tot | 105 | 80 | ||
| D—Water Management in RC | ||||
| OPERATION | START | FINISH | SUBMERSION DAY | DRY DAY |
| Sowing | 11/04/2022 | 11/04/2022 | ||
| Submersion 1 | 21/05/2022 | 28/05/2022 | 7 | |
| Dry 1 | 28/05/2022 | 09/06/2022 | 12 | |
| Submersion 2 | 10/06/2022 | 14/10/2022 | 126 | |
| Dry 2 | 15/10/2022 | 21/10/2022 | 6 | |
| Harvest | 22/10/2022 | 22/10/2022 | 8 | |
| Tot | 133 | 26 | ||
Table A3.
Additional information about the four target farms.
| Farm | BC | RC | BO | RO |
|---|---|---|---|---|
| Province | Pavia (PV) | Vercelli (VC) | Pavia (PV) | Vercelli (VC) |
| Model | Conventional | Conventional | Organic | Organic |
| Seeding Method | Dry seeding | Water seeding | Dry broadcast after flooding | Broadcast over green mulch |
| Weed Control | Chemical herbicides (pre- and post-emergence) | Chemical herbicides (pre-seeding, post-emergence) | Mechanical weeding + manual/machine pruning | Green mulching (allelopathic effect) |
| Fertilization | NPK fertilizers (chemical) | NPK fertilizers (chemical) | Organic manure | Organic fertilizers |
| Pest Management | Chemical fungicides (Amistar) | Chemical fungicides (Amistar) | Sulfur-based organic treatments | Sulfur-based organic treatments |
| Irrigation | Gravity-fed surface water | Gravity-fed surface water | Gravity-fed surface water | Gravity-fed surface water |
| Water Management | Submersion: June from 4th to 7th, Dry: June from 7th to 25th, Submersion: June from 26th to 29th, Dry: from June 29th to July 9th, Submersion: July from 10th to 13th | Sowing: April 15th, Submersion: May from 12th to 15th, Dry: from May 16th to June 3rd, Submersion: June from 4th yo 7th, Dry: June from 7th to 25th, Submersion: from June 26th to July 2nd | Sowing: May 30th, Submersion: June from 20th to 23rd, Dry: from June 21th to July 9th, Submersion: July from 10th to 13th, Dry: July from 14th to 29th, Submersion: from July 30th toAugust 2nd | Sowing: April 28th, Submersion: from April 28th to May 10th, Dry: May from 10th to 22nd, Submersion: May from 22nd to 25th, Dry: from May 25th to June 7th |
| UAA (ha) | 40 | 55 | 25 | 30 |
| Applied Crop Rotation | Not specified | Not specified | Crop rotation (not specified) | Crop rotation (not specified) |
| Rice Varieties | Baldo | Baldo | Baldo (organic) | Baldo (organic) |
| Average Yield (ton/ha) | 6.5 | 7.2 | 5.2 | 5.5 |
| Main Input Consumption | NPK fertilizers (225 kg/ha × 2), Herbicides (pre- and post-emergence), Fungicides | NPK fertilizers (pre- and post-seeding), Herbicides, Fungicides | Organic manure (30 t/ha), Sulfur-based pest treatments | Organic fertilizers, Sulfur-based treatments |
| Number of Workers | 5 | 6 | 4 | 4 |
Table A4.
OASIS criteria and indicators used in the analysis.
| Criterion | Indicator | Subindicator | Scale |
|---|---|---|---|
| Soil tillage (1.1.1) | The extent of adoption of agroecological measures in soil tillage. Also related to soil compaction. | Specific 1–5 scale thresholds for this criterion are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). | |
| Soil fertility management (1.1.2) | The extent of adoption of agroecological measures in soil fertility management (e.g., use of synthetic fertilizers, organic manures, legumes, cover crops). | 1: no agroecological practices used (completely synthetic fertilizers); 2: rarely used practice and/or only in a small part of the farm (up to 30%); 3: moderate use of practice and/or up to one half of the farmed land; 4: often-used practice and/or in 75% of the farmland, several strategies implemented; 5: several strategies implemented and no (or almost no) use of synthetic fertilizers. | |
| Pest management (1.1.3) | Use of chemical pesticides compared to local IPM recommendations; presence of diverse crop rotation/crops/ecological networks; use of other agroecological pest management practices. | Commercial insecticides and acaricides (5.2.7.5) | Specific 1–5 scale thresholds for the main criterion are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological); For Subindicator: measured using TFI: 1: >7, 2: 5.1–7, 3: 3.1–5, 4: 1.1–3, 5: 0–16. |
| Crop disease management (1.1.4) | Presence of crop diseases; use of fungicides/chemical fertilizers/soil solarization/copper/sulphate compared to IPM; type of soil tillage; extent of agroecological practices; regular monitoring. | Commercial fungicides and bactericides (5.2.7.7) | 1: use >30% of recommended chemical fungicides; copper sulphate >5 times/year; and/or deep/frequent tillage, excessive N, regular soil solarization; no clear prevention; 2: IPM (moderate fungicides); occasional soil solarization or copper sulphate up to 5 times/year; 3: use commercial BCAs, efficient microorganisms, some fungicides; rare solarization or copper sulphate up to 3 times/year; Thresholds for 4 and 5 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator; For Subindicator: measured using TFI: 1: >7, 2: 5.1–7, 3: 3.1–5, 4: 1.1–3, 5: 0–110. |
| Weed management (1.1.5) | Use of herbicides compared to IPM; use of mechanical weeding/flame weeding/bioherbicides; extent of agroecological practices supporting weed management; regular observation. | Commercial herbicides (5.2.7.6) | 1: synthetic herbicide on whole surface, >30% of recommended; 3: frequent mechanical weeding (more than twice/three times per ha/year) or frequent flame weeding or use of bioherbicides; 4: mixed management; 5: no use of herbicides and less than two instances of mechanical weeding per crop per year; well-established use of different weed management-supporting agroecological practices; For Subindicator: measured using TFI: 1: >4.5, 2: 3.1–4.5, 3: 1.6–3, 4: 0.8–1.5, 5: 0–0.7. |
| Soil cover (1.1.6) | Proportion of soil covered (with plants or biological material). | 1: <50% of the time (<6 months); 2: 50–75%; 3: 76–90%; 4: 91–95% (~11 months); 5: >95% (>11.5 months) | |
| Plant reproductive material (1.1.7) | Proportion of farmland using plant reproductive material requiring low amounts of inputs (water, synthetic fertilizers, pesticides). | Plant reproductive material (5.2.7.2) | 5: less than 20% of the plant reproductive material used requires significant amounts of inputs. Thresholds for 1–4 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. For Subindicator: Share of bought seeds and seedlings in total reproductive material used: 1: buys 75–100%, 2: buys up to 75% and/or produces part of seedlings, 3: up to 50% produced and/or produces all seedlings, 4: buys up to 25% and/or produces all seedlings. Threshold for 5 is not explicitly defined in the provided excerpt. |
| Animal welfare (1.2.1) | Extent of adoption of agroecological measures ensuring animal wellbeing and basic needs, based on the “Five Freedoms” concept (e.g., absence of hunger/thirst/malnutrition, absence of fear/distress) | Specific 1–5 scale thresholds are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). | |
| Livestock management (1.2.2) | Extent of adoption of agroecological measures in livestock management (percentage of low-demand animals, production level, animal product to food-competing feedstuff ratio, use of synthetic drugs/preventive natural methods) | Animal feed (5.2.7.9) | 1: farmer raises only highly productive animals OR uses very high amount of drugs OR very low quality concentrate-based feed (e.g., M:G ratio ≤ 2.9); 2: farmer raises highly productive animals; uses only synthetic drugs following recommendations; uses low-diversity feed with concentrates (e.g., M:G ratio 3.0–3.3); 3: farmer has some low-demand animals; uses small amounts of synthetic/some natural drugs; implements appropriate measures for hygiene, spacing, feeding (e.g., M:G ratio 3.4–4.0). Thresholds for 4 and 5 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. For Subindicator: Proportion of forage produced on the farm or received via non-monetary economy: 1: <20% self-sufficiency, 2: 20–40%, 3: 41–60%, 4: 61–80%, 5: >80%. |
| Veterinary drugs (5.2.7.10) | For Subindicator: Mean value of commercial drug treatments per animal per year or continual use of pharmaceutics: 1: continuous treatments, 2: mean value >3, 3: mean value 2–2.9, 4: mean value 1–1.9, 5: average < 1 drug per animal per year. | ||
| Grassland management (1.2.3) | Extent of adoption of agroecological measures in grasslands (stocking management, rotational/extensive grazing, proportion of legumes, amount of fertilizers) | 3: moderate use; 4: high use OR very high use in 60–80% of the used land; 5: very high use in >80% of the land. Thresholds for 1 and 2 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. | |
| Water management (1.3.1) | Extent of implementation of techniques that conserve water and increase irrigation efficiency | Water (5.2.7.1) | 1: no implementation; noticeable inefficient water use; 2: rarely used and/or only in a small part of the farm (up to 30%); Thresholds for 3–5 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. For Subindicator: Approximate consumption of bought water in m3/ha/year: 1: >6000, 2: 3000–6000, 3: 1000–2999, 4: 500–999, 5: <500. |
| Microclimate management (1.3.2) | Extent of use of techniques that enhance favorable microclimate (e.g., ponds, terraces, windbreaks, shading trees) | 1: not used at all; 2: rarely used and/or only in a small part of the farm (up to 10%); 3: moderate use, in up to 30% of the farmed land; 4: often used, 2 or 3 types of techniques, up to 50%; 5: very often used, in >50%, >3 different techniques. | |
| Agroforestry (1.3.3) | High level of adoption of agroforestry (system where trees and crops/livestock are grown together) | 1: not used at all; 2: rarely used and/or only in a small part of the farmed land (<25%); 3: moderate use, in up to one half (25–50%); 4: often used, in more than one half (51–75%); 5: very often used and in all parts (>75%). | |
| Low variable costs (2.1.1) | Yearly expenditures expressed in local currency compared to the regional average expenditures (for the crop/animal in question) per ha per year according to the farm type | Optional specific expenditure categories | 1: expenditure much larger than regional average (>160%); 2: expenditure larger than regional average (121–160%); 3: expenditure at regional average level (81–120%); 4: expenditure lower than regional average (40–80%); 5: expenditure much lower than regional average (<40%) or not used at all. For optional specific expenditure categories: Scales are generally provided under the 5.2.7 non-dependency on commercial inputs section (see below). |
| Low fixed costs (2.1.2) | Fixed costs (buildings and machinery/tools/technology) compared to the regional average expenditures, depreciated over 5 (machinery) or 20 (buildings) years | Optional machinery/tools and buildings | Specific 1–5 scale thresholds are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). For Optional Subindicators: Description of calculation provided but no 1–5 scale. |
| Product quality (2.2.1) | Assessment based on raw materials, practices, processes resulting in higher quality foods (‘healthy’, ‘natural’, ‘safe’, etc.), and communication of these practices (certification, direct communication, PGS). | Specific 1–5 scale thresholds are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). | |
| Product processing (2.2.2) | Proportion of the products sold that are processed by the farmer and/or small-scale and locally | 2: up to 10% processed; 3: 11–30%; 4: 31–60%; 5: >60% processed by farmer/small-scale locally | |
| Short marketing chain (2.2.3) | Assessment based on the number of intermediaries in the supply chain from farm to consumer. | 1: most revenue through long chains (>3 intermediaries) or length unknown; 2: most revenue through long chains (3 intermediaries); 3: most revenue through long–short chains (2 intermediaries); 4: most revenue through short chains and direct sale (0–1 intermediaries); 5: most revenue through direct sales | |
| Local marketing chain (2.2.4) | Assessment based on the geographical distance products travel to the final destination. | 5: most products travel >100 km. Thresholds for 1–4 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. | |
| Diversification of activities (2.2.5) | Number of additional non-farming activities present on the farm (e.g., processing food, farm shop, agritourism) | 1: no additional non-farming activities; 2: slight engagement, minor additional revenue; 3: moderate engagement, some additional revenue; 4: high engagement, important additional revenue OR farmer has primary non-farming job; 5: high diversification, non-farming revenue equals/higher than farming | |
| Income satisfaction (2.3.1) | Farmer’s subjective judgment (satisfaction) with the income coming from farming activities | 1: very low satisfaction; 2: low satisfaction; 3: moderate satisfaction; 4: high satisfaction; 5: very high satisfaction | |
| Income compared to other farmers (2.3.2) | The estimation of the evaluator on how large the profit of the farm is in the context of the region where it operates, compared to other similar farms | 1: significantly lower income; 2: lower income; 3: approximately equal income; 4: higher income; 5: significantly higher income | |
| Working conditions (3.1.1) | Observation and assessment of safety, treatment, and compliance with standards for waged workers (e.g., pesticide-free environment, respectful/equal treatment, safety equipment, training, hours, breaks, housing) | 1: very inhumane and unsafe environment; 2: unsafe environment (e.g., problematic pesticide handling); 3: relatively safe environment, but some problems noticed; 4: safe environment with minor safety issues; 5: very safe working environment | |
| Wages, job stability, social protection (3.1.2) | Compliance with contract requirements, social benefits, wages compared to regional average, reliance on temporary workers | 1: none of the requirements satisfied (highly precarious job, no social protection/wage/contract); 2: low wages and precarious but with a binding contract, some social benefits; 3: clear contracts but with a wage below regional average, reliance on temporary workers; 4: most of the requirements satisfied; 5: all of the requirements satisfied | |
| Gender equity (3.1.3) | Ratio of women in decision-making positions; women’s satisfaction with workplace; gender gap in salary/benefits/working hours; participation/autonomy in decision-making (family farms); land/livestock ownership; political participation | 2: low level of gender equity (e.g., large differences in working hours, salary differences, no women managers, most decisions by men); 3: medium level (some women managers, equal salaries, women feel safe, occasional political participation, hour differences, men make most decisions with consultation/women handle “marginal” matters); 4: high level (small hour differences, women in 1/3 managerial positions, women have authority in non-marginal realms, equal decision-making, good political/social participation); thresholds for 1 and 5 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. | |
| Job creation (3.1.4) | Hired labor (family labor is not included) expressed as full-time equivalents (FTE) per hectare | Workforce (5.2.7.11) | Specific 1–5 scale thresholds for this criterion are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). For Subindicator: Hired labor (family labor not included) expressed as FTE/ha: 1: >1, 2: 0.5–1, 3: 0.01–0.5, 4: 0.001–0.01, 5: 0.25. |
| Employment of people at risk of poverty and social exclusion (3.1.5) | Indicator not explicitly defined in the provided excerpts. | Specific 1–5 scale thresholds are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). | |
| Networks and collectives (3.2.1) | Intensity and continuity of participation in networks, collectives, organizations. | 1: no participation; 2: membership in 1–2, no genuine participation; 3: membership in a few, occasional involvement; 4: membership in a few with some involvement OR membership in one with good involvement, very good cooperation with other farmers; threshold for 5 is not explicitly defined in the provided excerpts. | |
| Social and solidarity economy (3.2.2) | Indicator not explicitly defined in the provided excerpts. | Specific 1–5 scale thresholds are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). | |
| Advocacy and education on agroecology (3.2.3) | Intensity and continuity of involvement in educational projects dealing with agroecology, advocacy activities related to any pillar of agroecology | Specific 1–5 scale thresholds are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). | |
| Transparency (3.2.4) | Deliberate attempt to make available relevant information (positive/negative) accurate, timely, balanced, unequivocal to enhance public reasoning and hold organizations accountable | Specific 1–5 scale thresholds are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). | |
| Traditional seeds and breeds (3.3.1) | Proportion of use and promotion of traditional local seeds and heritage breeds | 1: no use; 2: very slight (e.g., one marginal crop) or one-off use; 3: occasional use; 4: up to 20% traditional, occasional promotion; 5: >20% traditional, strong promotion | |
| Traditional foods (3.3.2) | Extent of involvement in preservation of traditional foods, specifically transformation following traditional processes and recipes | 2: transformation for family needs only; 3: small percentage of products sold transformed traditionally or partially; 4: most of the processed products sold transformed following traditional recipes; 5: all transformed products sold following traditional recipes, strong promotion; threshold for 1 is not explicitly defined in the provided excerpts. | |
| Satisfactory workload levels (3.4.1) | Farmer’s self-assessment of their (and family’s) yearly workload on a scale from 1 (too large) to 5 (very satisfactory) | 1: too large; 2: very large; 3: moderate; 4: satisfactory; 5: very satisfactory | |
| Low stress levels work environment (3.4.2) | Farmer’s self-assessment of the average amount of stress experienced throughout the year on a scale from 1 (extremely stressful) to 5 (not stressful) | 1: extremely stressful; 2: very stressful; 3: moderately stressful; 4: mildly stressful; 5: not stressful | |
| Sufficient time for family and social relationships (3.4.3) | Farmer’s self-assessment of the amount of free time for personal relationships on a scale from 1 (no time) to 5 (sufficient time) | 1: no time; 2: very little time; 3: moderate amount of time; 4: almost enough time; 5: sufficient amount of time | |
| Sufficient time for knowledge and skill acquisition (3.4.4) | Farmer’s self-assessment of the amount of free time for acquiring new knowledge and skills on a scale from 1 (no time) to 5 (sufficient time) | 1: no time; 2: very little time; 3: moderate amount of time; 4: almost enough time; 5: enough time | |
| Finding work meaningful (3.4.5) | Farmer’s self-assessment of the amount of meaning, motivation, and self-realization attributed to their work | 3: I sometimes feel fulfilled/motivated, but often would rather work another job / I find my job somewhat important for the wider community; 4: I mostly feel motivated, it is fulfilling / I find my job is very important; 5: I feel very fulfilled/motivated, would not genuinely consider any other job / I find my job is extremely important; thresholds for 1 and 2 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. | |
| Good level of self-consumption of food products (3.4.6) | Proportion of produce from the farm (including kitchen garden) in the family diet, including products received through non-monetary economy (approximate percentage) | 1: nil or extremely low (<10%); 2: 10–20%; 3: 21–40%; 4: 41–60%; 5: >60% (very high) | |
| Farmer’s perspective on farm’s future (3.5.1) | Farmer’s self-assessment (opinion) on the farm’s viability in the long-term on a scale from 1 (completely pessimistic) to 5 (completely optimistic) | 1: completely pessimistic; 2: pessimistic; 3: neither pessimistic nor optimistic (or no opinion); 4: optimistic; 5: completely optimistic | |
| Young farmer or successor (3.5.2) | Assessment of whether there is genuine possibility that the farm will continue functioning in the long-run considering farmer’s age and whether somebody is willing to take on the farm after the current farmer retires | 1: no chance to have a successor; 2: small possibility; 3: a moderate possibility; 4: good possibility; 5: very high probability OR successor took over in last 5 years OR farmer is young (<50) | |
| Low pollution (4.1.1) | Estimated by the investigator considering sources like type/amount of pesticides/fertilizers, fertilizer application, manure storage, packaging, plastic mulch, waste production/disposal | 1: very important pollution (e.g., due to highly toxic/long half-life pesticides); 2: important pollution; 3: medium pollution; thresholds for 4 and 5 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. | |
| Soil carbon budget optimization (4.1.2) | Use of practices enhancing soil’s carbon sink capacity and minimizing practices turning soil into a GHG source. Indicator considers soil tillage technique, proportion of soil covered, diversity of crop rotations, crop residue management, organic/chemical fertilizer use, type of grazing/stocking | 1: extreme overgrazing AND/OR intensive tillage, no use of cover crops, regular burning of crop residues, no crop rotation, no use of organic fertilizers. Thresholds for 2–5 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. | |
| Soil erosion minimization (4.1.3) | Implementation of techniques reducing soil erosion. Investigator considers inclination, visible signs, tillage, cover crops, soil cover, management techniques, fertility management | 4: slight presence of mudslides or erosion channels, low presumptions about erosion happening; 5: no presence of mudslides or erosion channels, almost no presumptions of erosion happening. Thresholds for 1–3 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. | |
| Soil salinization minimization (4.1.4) | Implementation of techniques reducing secondary salinization. Investigator considers visible signs, type of irrigation water/system, drainage system, farmer’s perspective. | 1: visible soil salinity in most parts, an important problem; 2: problems with soil salinity in many parts; 3: some problems, or only in some parts; 4: rare problems or only restricted to a small part. Threshold for 5 is not explicitly defined in the provided excerpts. | |
| Soil compaction minimization (4.1.5) | Implementation of techniques reducing soil compaction. Investigator observes visible signs, tillage technique/intensity/frequency, amount of traffic | Specific 1–5 scale thresholds are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). | |
| Ecological networks (4.2.1) | Proportion of used agricultural land that is ecological networks | Commercial biological control agents (5.2.7.8) | 1: no presence of EN; 2: up to 2%; 3: 2.1–5%; 4: 5.1–10%; 5: >10%, many efforts for developing ecological infrastructure. For subindicator: Measured using TFI: 1: >2, 2: 1.1–2, 3: 0.5–1, 4: 0.2–0.4, 5: 0–0.110. (Note: This connection implies a link between EN and reliance on commercial BCAs, though the scale is specific to BCA TFI). |
| High-Nature Value farming (HNVf) (4.2.2) | Proportion of farmland where HNVf is present, type of HNVf (specific management for conservation species, mosaic landscape, intensity of management, semi-natural vegetation) | Specific 1–5 scale thresholds are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. There is a diagram showing categories—NOT HNVf, SEMI-NATURAL VEGETATION, and TYPE 3 HNVf—related to intensity of use. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). | |
| Agrobiodiversity (4.2.3) | Mean value calculated from three types of agrobiodiversity: Land use diversity, Diversity of domestic animals, Diversity of crops | Land use diversity | Specific 1–5 scale thresholds for the mean value of the criterion are not explicitly defined. For subindicator Land use diversity: Shannon index: 1: 0–0.3, 2: 0.4–0.7, 3: 0.8–1.1, 4: 1.2–1.5, 5: >1.5. |
| Animal types (species and breeds) diversity | For subindicator Animal diversity: Direct enumeration: 1: 1 type, 2: 2 types, 3: 3 types, 4: 4 types, 5: 5+ types. | ||
| Crop types (species and cultivar) diversity | For subindicator Crop diversity: Shannon index: 1: 0–0.9, 2: 1–1.5, 3: 1.6–2.1, 4: 2.2–2.6, 5: >2.6. | ||
| Stress-tolerant species, breeds, and cultivars (5.1.1) | Proportion of use of cultivars, breeds, and species with stress-tolerant traits, percentage of low-demand animals. | 1: none have stress-tolerant characteristics; 2: up to 25% animal species/breeds AND/OR up to 25% land planted with stress-tolerant crops/cultivars; 3: up to 50% animal species/breeds AND/OR up to 50% land planted; 4: up to 75% animal species/breeds AND/OR up to 75% land planted; 5: (almost) all animal species/breeds AND (almost) all land planted | |
| Diversification of products (5.2.1) | Share of the main product in the total quantity of production | 1: major product share 90–100%; 2: 70–89%; 3: 45–69%; 4: 20–44%; 5: no product > 20% share | |
| Short and local marketing chains (5.2.2) | Mean value of 2.2.3.3 and 2.2.3.495. (Likely refers to a combination of the scales for Short marketing chain (2.2.3) and Local marketing chain (2.2.4)). | Specific 1–5 scale thresholds for the combined mean are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. See scales for 2.2.3 and 2.2.4 separately. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). | |
| Diversification of clients (5.2.3) | Share of each client in the purchase of the production (on average) | 1: one client buys all or nearly all; 2: one client buys 50–90%; 3: one clients buys maximum 30–49%; 4: one client buys maximum 10–29%. Threshold for 5 is not explicitly defined in the provided excerpts. | |
| Revenue distribution (5.2.4) | Spread of EBITDA throughout the year | 5: 80% of the revenue flows are concentrated in 10 months of the year (very stable distribution). Thresholds for 1–4 are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. | |
| Non-dependency on subsidies (5.2.5) | Share of subsidies in gross income | 1: share 70% or higher; 2: 50–70%; 3: 25–49%; 4: 10–24%; 5: lower than 10% | |
| Workforce permanence (5.2.6) | Ability to attract and keep motivated workforce | Specific 1–5 scale thresholds are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. The general scale ranges from 1 (least agroecological) to 5 (most agroecological). Related to Job creation (3.1.4) and Optional workforce (5.2.7.11). | |
| Non-dependency on commercial inputs (5.2.7) | This criterion is assessed via optional specific indicators related to dependence on purchased inputs | Water (5.2.7.1) | Specific 1–5 scale thresholds for the overall criterion are not explicitly defined. They are assessed based on the optional subindicators. For Subindicator Water: Approximate consumption of bought water in m3/ha/year: 1: >6000, 2: 3000–6000, 3: 1000–2999, 4: 500–999, 5: <500. |
| Plant reproductive material (5.2.7.2) | For Subindicator Plant reproductive material: Share of bought seeds and seedlings in total reproductive material used: 1: buys 75–100%; 2: buys up to 75% and/or produces part of seedlings; 3: up to 50% produced and/or produces all seedlings; 4: buys up to 25% and/or produces all seedlings. Threshold for 5 is not explicitly defined. | ||
| Young animals (5.2.7.3) | Indicator and scale thresholds for this optional subindicator are defined by crossing information from different questions and possibly objective quantitative data with qualitative information collected, observed, or estimated by the evaluator. | ||
| Fertilizers (commercial nitrogen) (5.2.7.4) | For Subindicator Fertilizers: Kg/ha of commercial nitrogen: 1: >200 kg/ha, 2: 150–200, 3: 100–149, 4: 50–99, 5: <50. | ||
| Insecticides (5.2.7.5) | For Subindicator Insecticides: Measured using TFI: 1: >7, 2: 5.1–7, 3: 3.1–5, 4: 1.1–3, 5: 0–1 | ||
| Herbicides (5.2.7.6) | For Subindicator Herbicides: Measured using TFI: 1: >4.5, 2: 3.1–4.5, 3: 1.6–3, 4: 0.8–1.5, 5: 0–0.7 | ||
| Fungicides (5.2.7.7) | For Subindicator Fungicides: Measured using TFI: 1: >7, 2: 5.1–7, 3: 3.1–5, 4: 1.1–3, 5: 0–1 | ||
| BCAs (commercial biological control agents) (5.2.7.8) | For Subindicator BCAs: Measured using TFI: 1: >2, 2: 1.1–2, 3: 0.5–1, 4: 0.2–0.4, 5: 0–0.1 | ||
| Animal feed (5.2.7.9) | For Subindicator Animal feed: Proportion of forage self-sufficiency: 1: <20%, 2: 20–40%, 3: 41–60%, 4: 61–80%, 5: >80% | ||
| Veterinary drugs (5.2.7.10) | For Subindicator Veterinary drugs: Mean value of commercial drug treatments per animal per year or continual use: 1: continuous treatments; 2: mean value > 3; 3: mean value 2–2.9; 4: mean value 1–1.9; 5: average < 1 drug per animal per year | ||
| Workforce (5.2.7.11) | For Subindicator Workforce: Hired labor (FTE/ha): 1: >1, 2: 0.5–1, 3: 0.01–0.5, 4: 0.001–0.01, 5: 0 | ||
| Energy (5.2.7.12) | For Subindicator Energy: Energy consumption (kgOE/ha/year): 1: >150, 2: 101–150, 3: 51–100, 4: 11–50, 5: 0–10 |
Table A5.
Indicators describing the difference between 2022 and reference periods.
| Indicator | Year 2022 | Average (Reference Period) | Change 2022 vs. Average | Source |
|---|---|---|---|---|
| Wholesale price of rice (Producer price, €/ton) | € 600.20 | €521.21 (2011–2021) | 13% | FAOSTAT |
| Annual average temperature (°C) | 16.6 °C | 15 °C (2011–2021) | 10% | ISTAT |
| Total annual precipitation (mm) | 409.2 mm | 891.6 mm (2006–2015) | −54% | ISTAT |
| Average diesel price (€/L) | € 1.51 | €0.90 (2015–2025 excluding 2022) | 40% | CCIA Milan |
Table A6.
List of factors and their acronyms as used in the LCA, with units of measurement.
| Acronym | Meaning | Unit of measure (usual) |
|---|---|---|
| ACID | Acidification (terrestrial and freshwater) | mol H+ eq |
| CC | Climate change | kg CO2 eq |
| ETX-FW | Ecotoxicity, freshwater | Comparative Toxic Unit (CTUe) |
| PM | Particulate matter | Disease incidence |
| EU-MAR | Eutrophication, marine | kg N eq |
| EU-FW | Eutrophication, freshwater | kg P eq |
| EU-TERR | Eutrophication, terrestrial | mol N eq |
| HT-C | Human Toxicity—cancer effects | Comparative Toxic Unit (CTUh) |
| HT-NC | Human Toxicity—non-cancer effects | Comparative Toxic Unit (CTUh) |
| IR | Ionizing Radiation | kBq U-235 eq |
| LU | Land use | Pt or dimensionless |
| OD | Ozone depletion | kg CFC-11 eq |
| POF | Photochemical ozone formation | kg NMVOC eq |
| RU-F | Resource use—fossils | MJ |
| RU-MM | Resource use—minerals and metals | kg Sb eq |
| WU | Water use | m3 world eq |
References
- Hashim, N.; Ali, M.M.; Mahadi, M.R.; Abdullah, A.F.; Wayayok, A.; Mohd Kassim, M.S.; Jamaluddin, A. Smart Farming for Sustainable Rice Production: An Insight into Application, Challenge, and Future Prospect. Rice Sci. 2024, 31, 47–61. [Google Scholar] [CrossRef]
- Ahmad Rizal, A.R.; Md Nordin, S.; Abd Rashid, R.; Hassim, N. Decoding the Complexity of Sustainable Rice Farming: A Systematic Review of Critical Determining Factor of Farmers’ Sustainable Practices Adoption. Cogent Food Agric. 2024, 10, 2334994. [Google Scholar] [CrossRef]
- Chivenge, P.; Angeles, O.; Hadi, B.; Acuin, C.; Connor, M.; Stuart, A.; Puskur, R.; Johnson-Beebout, S. Chapter 10—Ecosystem Services in Paddy Rice Systems. In The Role of Ecosystem Services in Sustainable Food Systems; Rusinamhodzi, L., Ed.; Academic Press: Cambridge, MA, USA, 2020; pp. 181–201. ISBN 978-0-12-816436-5. [Google Scholar]
- Surendran, U.; Raja, P.; Jayakumar, M.; Subramoniam, S.R. Use of Efficient Water Saving Techniques for Production of Rice in India under Climate Change Scenario: A Critical Review. J. Clean. Prod. 2021, 309, 127272. [Google Scholar] [CrossRef]
- Mahabubur Rahman, M.; Yamamoto, A. Methane Cycling in Paddy Field: A Global Warming Issue. In Agrometeorology; Swaroop Meena, R., Ed.; IntechOpen: London, UK, 2021; ISBN 978-1-83881-174-7. [Google Scholar]
- Dorairaj, D.; Govender, N.T. Rice and Paddy Industry in Malaysia: Governance and Policies, Research Trends, Technology Adoption and Resilience. Front. Sustain. Food Syst. 2023, 7, 1093605. [Google Scholar] [CrossRef]
- Giuliana, V.; Lucia, M.; Marco, R.; Simone, V. Environmental Life Cycle Assessment of Rice Production in Northern Italy: A Case Study from Vercelli. Int. J. Life Cycle Assess. 2024, 29, 1523–1540. [Google Scholar] [CrossRef]
- Dara Guccione, G.; Pagliarino, E.; Vaccaro, A.; Borri, I.; Borsotto, P. A Participatory Analysis of the Control and Certification System in the Italian Organic Rice Value Chain. Sustainability 2021, 13, 2001. [Google Scholar] [CrossRef]
- Arcieri, M.; Ghinassi, G. Rice Cultivation in Italy under the Threat of Climatic Change: Trends, Technologies and Research Gaps. Irrig. Drain. 2020, 69, 517–530. [Google Scholar] [CrossRef]
- FAO. The White/Wiphala Paper on Indigenous Peoples’ Food Systems; FAO: Rome, Italy, 2021; ISBN 978-92-5-134487-3. [Google Scholar]
- Quiédeville, S.; Bassene, J.-B.; Lançon, F.; Chabrol, D.; Moustier, P. Systemic Sustainability of the French Organic Rice and PGI Einkorn Value Chains: A Preliminary Assessment Based on Network Analysis. Sustainability 2018, 10, 2344. [Google Scholar] [CrossRef]
- Nuntana Udomkit, N.U.; Adrian Winnett, A.W. Fair Trade in Organic Rice: A Case Study from Thailand. Enterp. Dev. Microfinance 2002, 13, 45–53. [Google Scholar] [CrossRef]
- FAO. Organic Agriculture, Environment and Food Security; El-Hage Scialabba, N., Hattam, C., Hage Scialabba, N., Eds.; Environment and Natural Resources Series; FAO: Rome, Italy, 2002; ISBN 978-92-5-104819-1. [Google Scholar]
- Raj, J.; Jat, S.; Kumar, M.; Reema, A.; Yadav, A. The Role of Organic Farming in Sustainable Agriculture. Adv. Res. 2024, 25, 128–136. [Google Scholar] [CrossRef]
- Bacenetti, J.; Fusi, A.; Negri, M.; Bocchi, S.; Fiala, M. Organic Production Systems: Sustainability Assessment of Rice in Italy. Agric. Ecosyst. Environ. 2016, 225, 33–44. [Google Scholar] [CrossRef]
- Bocchi, S.; Ferrero, A.; Porro, A. Proceedings of the Fourth Temperate Rice Conference: 25–28 June 2007, Novara—Italy; SIRFI: Novara, Italy, 2007; ISBN 978-88-95616-01-8. [Google Scholar]
- Blengini, G.A.; Busto, M. The Life Cycle of Rice: LCA of Alternative Agri-Food Chain Management Systems in Vercelli (Italy). J. Environ. Manag. 2009, 90, 1512–1522. [Google Scholar] [CrossRef] [PubMed]
- Hatcho, N.; Matsuno, Y.; Kochi, K.; Nishishita, K. Assessment of Environment-Friendly Rice Farming Through Life Cycle Assessment (LCA). CMU J. Nat. Sci. 2012, 11, 403–408. [Google Scholar]
- Fusi, A.; Bacenetti, J.; González-García, S.; Vercesi, A.; Bocchi, S.; Fiala, M. Environmental Profile of Paddy Rice Cultivation with Different Straw Management. Sci. Total Environ. 2014, 494, 119–128. [Google Scholar] [CrossRef]
- Vaglia, V.; Bacenetti, J.; Orlando, F.; Alali, S.; Bosso, E.; Bocchi, S. The Environmental Impacts of Different Organic Rice Management in Italy Considering Different Productive Scenarios. Sci. Total Environ. 2022, 853, 158365. [Google Scholar] [CrossRef]
- Clark, M.; Tilman, D. Comparative Analysis of Environmental Impacts of Agricultural Production Systems, Agricultural Input Efficiency, and Food Choice. Environ. Res. Lett. 2017, 12, 064016. [Google Scholar] [CrossRef]
- Mehmeti, A.; Abdelhafez, A.A.M.; Ellssel, P.; Todorovic, M.; Calabrese, G. Performance and Sustainability of Organic and Conventional Cotton Farming Systems in Egypt: An Environmental and Energy Assessment. Sustainability 2024, 16, 6637. [Google Scholar] [CrossRef]
- Huang, J.; Tichit, M.; Poulot, M.; Darly, S.; Li, S.; Petit, C.; Aubry, C. Comparative Review of Multifunctionality and Ecosystem Services in Sustainable Agriculture. J. Environ. Manag. 2015, 149, 138–147. [Google Scholar] [CrossRef]
- Van Der Werf, H.M.G.; Knudsen, M.T.; Cederberg, C. Towards Better Representation of Organic Agriculture in Life Cycle Assessment. Nat. Sustain. 2020, 3, 419–425. [Google Scholar] [CrossRef]
- Winter, L.; Lehmann, A.; Finogenova, N.; Finkbeiner, M. Including Biodiversity in Life Cycle Assessment—State of the Art, Gaps and Research Needs. Environ. Impact Assess. Rev. 2017, 67, 88–100. [Google Scholar] [CrossRef]
- European Commission Analysing of Existing Environmental Impact Assessment Methodologies for Use in Life Cycle Assessment, 1st ed.; JRC: Ispra, Italy, 2010.
- Romani, M.; Beltarre, G. Comparison of Different Sowing Types and Nitrogen Fertilization in Organic Rice in Italy; Bocchi, S., Ferrero, A., Porro, A., Eds.; University of Milan: Milan, Italy, 2007. [Google Scholar]
- Hokazono, S.; Hayashi, K. Variability in Environmental Impacts during Conversion from Conventional to Organic Farming: A Comparison among Three Rice Production Systems in Japan. J. Clean. Prod. 2012, 28, 101–112. [Google Scholar] [CrossRef]
- Vinci, G.; Ruggieri, R.; Ruggeri, M.; Prencipe, S.A. Rice Production Chain: Environmental and Social Impact Assessment—A Review. Agriculture 2023, 13, 340. [Google Scholar] [CrossRef]
- Mancini, L.; Valente, A.; Vignola, G.; Mengual, E.; Sala, S. Social Footprint of European Food Production and Consumption. Sustain. Prod. Consum. 2023, 35, 287–299. [Google Scholar] [CrossRef]
- Castoldi, N.; Bechini, L. Integrated Sustainability Assessment of Cropping Systems with Agro-Ecological and Economic Indicators in Northern Italy. Eur. J. Agron. 2010, 32, 59–72. [Google Scholar] [CrossRef]
- Bechini, L.; Castoldi, N. On-Farm Monitoring of Economic and Environmental Performances of Cropping Systems: Results of a 2-Year Study at the Field Scale in Northern Italy. Ecol. Indic. 2009, 9, 1096–1113. [Google Scholar] [CrossRef]
- Tesio, F.; Tabacchi, M.; Cerioli, S.; Follis, F. Sustainable Hybrid Rice Cultivation in Italy: A Review. Agron. Sustain. Dev. 2014, 34, 93–102. [Google Scholar] [CrossRef]
- Boval, M.; Bellon, S.; Alexandre, G. Agroecology and Grassland Intensification in the Caribbean. In Sustainable Agriculture Reviews 14: Agroecology and Global Change; Ozier Lafontaine, H., Lesueur Jannoyer, M., Eds.; Springer: Cham, Switzerland, 2014; Volume 14, pp. 159–184. ISBN 978-3-319-06015-6. [Google Scholar]
- Benoit, M.; Vazeille, K.; Jury, C.; Troquier, C.; Veysset, P.; Prache, S. Combining Beef Cattle and Sheep in an Organic System. II. Benefits for Economic and Environmental Performance. Animal 2023, 17. [Google Scholar] [CrossRef]
- Virginia, A.; Zamora, M.; Barbera, A.; Castro-Franco, M.; Domenech, M.; De Gerónimo, E.; Costa, J. Industrial Agriculture and Agroecological Transition Systems: A Comparative Analysis of Productivity Results, Organic Matter and Glyphosate in Soil. Agric. Syst. 2018, 167, 103–112. [Google Scholar] [CrossRef]
- Gharsallah, O.; Gandolfi, C.; Facchi, A. Methodologies for the Sustainability Assessment of Agricultural Production Systems, with a Focus on Rice: A Review. Sustainability 2021, 13, 11123. [Google Scholar] [CrossRef]
- Wang, Q.; Zhang, Y.; Tian, S.; Yuan, X.; Ma, Q.; Liu, M.; Li, Y.; Liu, J. Evaluation and Optimization of a Circular Economy Model Integrating Planting and Breeding Based on the Coupling of Emergy Analysis and Life Cycle Assessment. Environ. Sci. Pollut. Res. 2021, 28, 62407–62420. [Google Scholar] [CrossRef] [PubMed]
- Dang, H.D. Sustainability of the Rice-Shrimp Farming System in Mekong Delta, Vietnam: A Climate Adaptive Model. JED 2020, 22, 21–45. [Google Scholar] [CrossRef]
- ISTAT. Rice Areas and Production—Overall Data 2022. Available online: https://www.istat.it/dati/banche-dati/ (accessed on 1 July 2025).
- Bonaldo, D.; Bellafiore, D.; Ferrarin, C.; Ferretti, R.; Ricchi, A.; Sangelantoni, L.; Vitelletti, M.L. The Summer 2022 Drought: A Taste of Future Climate for the Po Valley (Italy)? Reg. Environ. Change 2023, 23, 1. [Google Scholar] [CrossRef]
- FAOSTAT. FAOSTAT Rice Production 2024. Available online: https://www.fao.org/faostat/en/#data (accessed on 1 July 2025).
- Camera di Commercio Milano Monza Brianza, Lodi Price Collection 2025. Available online: https://www.milomb.camcom.it/prezzi (accessed on 1 July 2025).
- Škorjanc, K.; Peeters, A.; Wezel, A.; Migliorini, P. OASIS, the Origi Nal Agroecological Survey Indicator System: Methodology and Guidelines for the Assessor; Agroecology Europe: Brussels, Belgium, 2021; ISBN 978-2-9602977-3-7. [Google Scholar]
- International EPD System International EPD System 2023. Available online: https://www.environdec.com/pcr/the-pcr (accessed on 1 July 2025).
- Geß, A.; Viola, I.; Miretti, S.; Macchi, E.; Perona, G.; Battaglini, L.; Baratta, M. A New Approach to LCA Evaluation of Lamb Meat Production in Two Different Breeding Systems in Northern Italy. Front. Vet. Sci. 2020, 7, 651. [Google Scholar] [CrossRef]
- Kathirvel, N. Production Problems Faced by Betel Leaf Farmers’ in Karur District. J. Manag. Sci. 2015, 1, 323–328. [Google Scholar] [CrossRef]
- IPCC. Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories; IPCC: Geneva, Switzerland, 2019. [Google Scholar]
- European Commission; Joint Research Centre. Suggestions for Updating the Organisation Environmental Footprint (OEF) Method; Publications Office: Luxembourg, 2019. [Google Scholar]
- De Laurentiis, V.; Amadei, A.; Sanyé-Mengual, E.; Sala, S. Exploring Alternative Normalization Approaches for Life Cycle Assessment. Int. J. Life Cycle Assess. 2023, 28, 1382–1399. [Google Scholar] [CrossRef]
- Garcia-Herrero, I.; Laso, J.; Margallo, M.; Bala, A.; Gazulla, C.; Fullana-i-Palmer, P.; Vázquez-Rowe, I.; Irabien, A.; Aldaco, R. Incorporating Linear Programing and Life Cycle Thinking into Environmental Sustainability Decision-Making: A Case Study on Anchovy Canning Industry. Clean. Techn Environ. Policy 2017, 19, 1897–1912. [Google Scholar] [CrossRef]
- Kalbar, P.P.; Birkved, M.; Nygaard, S.E.; Hauschild, M. Weighting and Aggregation in Life Cycle Assessment: Do Present. Aggregated Single Scores Provide Correct. Decision Support? J. Ind. Ecol. 2017, 21, 1591–1600. [Google Scholar] [CrossRef]
- Isaac, M.E.; Lin, T.; Caillon, S.; Sebastien, L.; MacDonald, K.; Prudham, S.; Doncieux, A.; Renard, D.; Aumeeruddy-Thomas, Y.; Vincent, L.; et al. Multidimensional Measures of Farmer Well-Being: A Scoping Review. Agron. Sustain. Dev. 2024, 44, 39. [Google Scholar] [CrossRef]
- Orounladji, B.M.; Sib, O.; Berre, D.; Assouma, M.H.; Dabire, D.; Sanogo, S.; Vall, E. Cross-Examination of Agroecology and Viability in Agro-Sylvo-Pastoral Systems in Western Burkina Faso. Agroecol. Sustain. Food Syst. 2024, 48, 581–609. [Google Scholar] [CrossRef]
- Slavickiene, A.; Savickiene, J. Comparative analysis of farm economic viability assessment methodologies. Eur. Sci. J. 2014, 10, 130–150. [Google Scholar]
- OECD. Social Issues in Agriculture in Rural Areas; OECD Food, Agriculture and Fisheries Papers; OECD Publishing: Paris, France, 2024; Volume 212. [Google Scholar]
- Pagliarino, E.; Rolfo, S.; Zoppi, I.M. La Responsabilité Sociale Dans La Culture de Riz Bio En Italie. Nat. Sci. Soc. 2021, 29, 95–102. [Google Scholar] [CrossRef]
- Panpakdee, C.; Simaraks, S.; Sookcharoen, C. Using the Delphi Method to Develop the Social-Ecological Resilience Indicators of Organic Rice Production in Thailand. For. Soc. 2022, 6, 157–174. [Google Scholar] [CrossRef]
- Loaiza, S.; Verchot, L.; Valencia, D.; Guzmán, P.; Amezquita, N.; Garcés, G.; Puentes, O.; Trujillo, C.; Chirinda, N.; Pittelkow, C.M. Evaluating Greenhouse Gas Mitigation through Alternate Wetting and Drying Irrigation in Colombian Rice Production. Agric. Ecosyst. Environ. 2024, 360, 108787. [Google Scholar] [CrossRef]
- Carlson, K.M.; Gerber, J.S.; Mueller, N.D.; Herrero, M.; MacDonald, G.K.; Brauman, K.A.; Havlik, P.; O’Connell, C.S.; Johnson, J.A.; Saatchi, S.; et al. Greenhouse Gas Emissions Intensity of Global Croplands. Nat. Clim. Change 2017, 7, 63–68. [Google Scholar] [CrossRef]
- Meijide, A.; Gruening, C.; Goded, I.; Seufert, G.; Cescatti, A. Water Management Reduces Greenhouse Gas Emissions in a Mediterranean Rice Paddy Field. Agric. Ecosyst. Environ. 2017, 238, 168–178. [Google Scholar] [CrossRef]
- Dahlgreen, J.; Parr, A. Exploring the Impact of Alternate Wetting and Drying and the System of Rice Intensification on Greenhouse Gas Emissions: A Review of Rice Cultivation Practices. Agronomy 2024, 14, 378. [Google Scholar] [CrossRef]
- Pathak, H.; Wassmann, R. Introducing Greenhouse Gas Mitigation as a Development Objective in Rice-Based Agriculture: I. Generation of Technical Coefficients. Agric. Syst. 2007, 94, 807–825. [Google Scholar] [CrossRef]
- Pérez, R.; Argüelles, F.; Laca, A.; Laca, A. Evidencing the Importance of the Functional Unit in Comparative Life Cycle Assessment of Organic Berry Crops. Environ. Sci. Pollut. Res. 2024, 31, 22055–22072. [Google Scholar] [CrossRef]
- Hashemi, F.; Mogensen, L.; Van Der Werf, H.M.G.; Cederberg, C.; Knudsen, M.T. Organic Food Has Lower Environmental Impacts per Area Unit and Similar Climate Impacts per Mass Unit Compared to Conventional. Commun. Earth Environ. 2024, 5, 250. [Google Scholar] [CrossRef]
- Boschiero, M.; De Laurentiis, V.; Caldeira, C.; Sala, S. Comparison of Organic and Conventional Cropping Systems: A Systematic Review of Life Cycle Assessment Studies. Environ. Impact Assess. Rev. 2023, 102, 107187. [Google Scholar] [CrossRef]
- Matthews, H.S.; Matthews, D.; Hendrickson, C.T. Life Cycle Assessment: Quantitative Approaches for Decisions That Matter. Open Access Textbook. 2014. Available online: https://www.lcatextbook.com/ (accessed on 1 July 2025).
- Tittonell, P.; Piñeiro, G.; Garibaldi, L.A.; Dogliotti, S.; Olff, H.; Jobbagy, E.G. Agroecology in Large Scale Farming—A Research Agenda. Front. Sustain. Food Syst. 2020, 4, 584605. [Google Scholar] [CrossRef]
- Pingali, P.L.; Roger, P.A. (Eds.) Impact of Pesticides on Farmer Health and the Rice Environment; Natural resource management and policy; Kluwer Academic Publishers: Boston, MA, USA, 1995; ISBN 978-0-7923-9522-5. [Google Scholar]
- Vermeer, K. Pursuing the Trail of Pesticides and Mercury in Aquatic Birds Reminiscences of a Biologist. Blue Jay 2019, 77, 43–47. [Google Scholar] [CrossRef]
- Bambaradeniya, C.N.B.; Amarasinghe, F.P. Biodiversity Associated with the Rice Field Agroecosystem in Asian Countries: A Brief Review; International Water Management Institute: Colombo, Sri Lanka, 2004; ISBN 978-92-9090-532-5. [Google Scholar]
- Ariyarathna, S.; Nanayakkara, K.; Thushara, S. The Nexus of Farmers’ Sustainable Agriculture Potential and Readiness for More Organic Use in Rice Farming: Insights from Resilience Theory. Sustain. Environ. 2023, 9, 2273619. [Google Scholar] [CrossRef]
- Panpakdee, C. The Social-Ecological Resilience Indicators of Organic Rice Production in Northeastern Thailand. Org. Agric. 2023, 13, 483–501. [Google Scholar] [CrossRef]
- Panpakdee, C.; Limnirankul, B.; Kramol, P. Assessing the Social-Ecological Resilience of Organic Farmers in Chiang Mai Province, Thailand. For. Soc. 2021, 5, 631–649. [Google Scholar] [CrossRef]
- Heckelman, A.; Smukler, S.; Wittman, H. Cultivating Climate Resilience: A Participatory Assessment of Organic and Conventional Rice Systems in the Philippines. Renew. Agric. Food Syst. 2018, 33, 225–237. [Google Scholar] [CrossRef]
Figure 1.
Geographical context of case studies. The locations of the 4 farms assessed are shown on aerial photograph, near Bereguardo (PV) and Vercelli (VC).
Figure 1.
Geographical context of case studies. The locations of the 4 farms assessed are shown on aerial photograph, near Bereguardo (PV) and Vercelli (VC).
Figure 2.
Life Cycle Assessment workflow. The sources of emissions are listed and linked to the LCA input classes, which are assessed for each scenario to produce estimations of output emissions.
Figure 2.
Life Cycle Assessment workflow. The sources of emissions are listed and linked to the LCA input classes, which are assessed for each scenario to produce estimations of output emissions.
Figure 3.
Agroecological performances of the four case studies across the OASIS dimensions.
Figure 4.
Comparative results of the four rice management scenarios (BO, BC, RO, RC) using the EF 3.1 method. LCA impact classification is used. BO (Blue): Bereguardo Organic farm; BC (Orange): Bereguardo Conventional farm; RO (Green): Rovasenda Organic farm; RC (Light Blue): Rovasenda Conventional farm. ACID: Acidification; CC: Climate change; ETX-FW: Ecotoxicity, freshwater; PM: Particulate matter; EU-MAR: Eutrophication, marine; EU-FW: Eutrophication, freshwater; EU-TERR: Eutrophication, terrestrial; HT-C: Human toxicity, cancer; HT-NC: Human toxicity, non-cancer; IR: Ionizing radiation; LU: Land use; OD: Ozone depletion; POF: Photochemical ozone formation; RU-F: Resource use, fossils; RU-MM: Resource use, minerals and metals; WU: Water use.
Figure 4.
Comparative results of the four rice management scenarios (BO, BC, RO, RC) using the EF 3.1 method. LCA impact classification is used. BO (Blue): Bereguardo Organic farm; BC (Orange): Bereguardo Conventional farm; RO (Green): Rovasenda Organic farm; RC (Light Blue): Rovasenda Conventional farm. ACID: Acidification; CC: Climate change; ETX-FW: Ecotoxicity, freshwater; PM: Particulate matter; EU-MAR: Eutrophication, marine; EU-FW: Eutrophication, freshwater; EU-TERR: Eutrophication, terrestrial; HT-C: Human toxicity, cancer; HT-NC: Human toxicity, non-cancer; IR: Ionizing radiation; LU: Land use; OD: Ozone depletion; POF: Photochemical ozone formation; RU-F: Resource use, fossils; RU-MM: Resource use, minerals and metals; WU: Water use.
Figure 5.
Comparative results of gravity analysis within different scenarios: BO is Bereguardo Organic farm, BC is Bereguardo Conventional farm, RO is Rovasenda Organic farm, and RC is ROvasenda Conventional farm.
Figure 5.
Comparative results of gravity analysis within different scenarios: BO is Bereguardo Organic farm, BC is Bereguardo Conventional farm, RO is Rovasenda Organic farm, and RC is ROvasenda Conventional farm.
Figure 6.
Single score results.
Figure 7.
Share of variable costs for each target scenario. RC is Rovasenda Conventional, RO is Rovasenda Organic, BC is Bereguardo Conventional, BO is Bereguardo Organic.
Figure 7.
Share of variable costs for each target scenario. RC is Rovasenda Conventional, RO is Rovasenda Organic, BC is Bereguardo Conventional, BO is Bereguardo Organic.
Figure 8.
Integrated sustainability assessment showing the results of LCA (single score), Gross margin, and OASIS assessment of the four target farms, compared to the average score obtained by the organic and conventional models.
Figure 8.
Integrated sustainability assessment showing the results of LCA (single score), Gross margin, and OASIS assessment of the four target farms, compared to the average score obtained by the organic and conventional models.
Table 1.
Structure of OASIS survey form. The table shows the number of questions for each section of the OASIS survey form.
Table 1.
Structure of OASIS survey form. The table shows the number of questions for each section of the OASIS survey form.
| Subject | Number of Questions |
|---|---|
| Descriptive data about the farm | 20 |
| Crops and management | 110 |
| Costs and revenues | 75 |
| Life quality and gender equity | 30 |
| Labor conditions | 25 |
Table 2.
Emissions for each studied scenario. BO: Bereguardo Organic, BC: Bereguardo Conventional, RO: Rovasenda Organic; RC: Rovasenda Conventional.
Table 2.
Emissions for each studied scenario. BO: Bereguardo Organic, BC: Bereguardo Conventional, RO: Rovasenda Organic; RC: Rovasenda Conventional.
| Emissions | Unit of Measure | BO | BC | RO | RC |
|---|---|---|---|---|---|
| Ammonia volatilized | kg NH3-N/ha | 5.06 | 11.99 | 18.45 | 42.28 |
| Nitrogen monoxide | kg NO-N/ha | 0.00 | 5.16 | 0.00 | 0.45 |
| Direct and indirect emissions of N2O | kg of N2O/ha | 0.04 | 2.52 | 0.13 | 0.91 |
| Direct emissions of CH4 from paddy water | kg of CH4/ha | 47.98 | 29.11 | 289.29 | 493.35 |
| Nitrates leaching and runoff | kg of NO3-/ha | 21.09 | 140.62 | 76.88 | 164.13 |
Table 3.
Results from the OASIS scoring procedure. The overall score for the five dimensions of analysis for RO: Rovasenda Organic, RC: Rovasenda Conventional, BO: Bereguardo Organic, BC: Bereguardo Conventional. A +/– 0.2 threshold centered on the group average score has been used to determine the extent to which each farm performs in comparison with the others.
Table 3.
Results from the OASIS scoring procedure. The overall score for the five dimensions of analysis for RO: Rovasenda Organic, RC: Rovasenda Conventional, BO: Bereguardo Organic, BC: Bereguardo Conventional. A +/– 0.2 threshold centered on the group average score has been used to determine the extent to which each farm performs in comparison with the others.
| Analysis Dimension | BO | BC | RO | RC | Average | SD |
|---|---|---|---|---|---|---|
| Adoption of agroecological practices | 3.15 | 2.50 | 3.45 | 2.15 | 2.81 | 0.59 |
| Economic aspects | 3.63 | 4.01 | 3.72 | 3.99 | 3.84 | 0.19 |
| Sociopolitical aspects | 3.99 | 4.05 | 3.97 | 4.07 | 4.02 | 0.05 |
| Impact on the environment and biodiversity | 3.20 | 3.02 | 3.37 | 2.50 | 3.02 | 0.38 |
| Resilience | 3.74 | 3.67 | 3.72 | 2.83 | 3.49 | 0.44 |
Table 4.
Gross margin results, and Variable costs for each target farm and for the two considered production models. Per hectare.
Table 4.
Gross margin results, and Variable costs for each target farm and for the two considered production models. Per hectare.
| Farm | Gross Margin per Ha | Gross Profit per Ha (Euro) | Gross Income per Ha (Euro) | Total Variable Costs per Ha (Euro) | Diesel Consumption | Seeds | Treatments | Fertilizations | Water Management | Equipment Depreciation | Contractors |
|---|---|---|---|---|---|---|---|---|---|---|---|
| BO | 52.72% | € 1318.01 | € 2500.00 | € 1181.99 | € 297.00 | € 400.00 | € 0.00 | € 150.00 | € 50.00 | € 109.49 | € 175.50 |
| BC | 70.11% | € 2649.99 | € 3780.00 | € 1130.01 | € 267.30 | € 234.00 | € 257.60 | € 210.00 | € 50.00 | € 111.11 | € 0.00 |
| RO | 56.02% | € 1680.56 | € 3000.00 | € 1319.44 | € 330.00 | € 360.00 | € 90.00 | € 200.00 | € 50.00 | € 289.44 | € 0.00 |
| RC | 74.86% | € 4379.42 | € 5850.00 | € 1470.58 | € 350.90 | € 200.00 | € 276.00 | € 120.00 | € 50.00 | € 473.68 | € 0.00 |
| Mean-O | 54.37% | € 1499.29 | € 2750.00 | € 1250.71 | € 313.50 | € 380.00 | € 45.00 | € 175.00 | € 50.00 | € 199.46 | € 87.75 |
| SD-O | 2.33% | € 256.36 | € 353.55 | € 97.19 | € 23.33 | € 28.28 | € 63.64 | € 35.36 | € 0.00 | € 127.24 | € 124.10 |
| Mean-C | 72.48% | € 3514.70 | € 4815.00 | € 1300.30 | € 309.10 | € 217.00 | € 266.80 | € 165.00 | € 50.00 | € 292.40 | € 0.00 |
| SD-C | 3.36% | € 1222.89 | € 1463.71 | € 240.82 | € 59.11 | € 24.04 | € 13.01 | € 63.64 | € 0.00 | € 256.38 | € 0.00 |
| Mean-WHOLE GROUP | 63.43% | ||||||||||
| SD-WHOLE | 10.72% |
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MDPI and ACS Style
Guglielmo, S.; Pietro, D.M.; Andrea, C.; Narote, A.D.; Guidetti, R.; Bocchi, S.; Vaglia, V. Sustainability Assessment of Rice Farming: Insights from Four Italian Farms Under Climate Stress. Agriculture 2025, 15, 1797. https://doi.org/10.3390/agriculture15171797
AMA Style
Guglielmo S, Pietro DM, Andrea C, Narote AD, Guidetti R, Bocchi S, Vaglia V. Sustainability Assessment of Rice Farming: Insights from Four Italian Farms Under Climate Stress. Agriculture. 2025; 15(17):1797. https://doi.org/10.3390/agriculture15171797
Chicago/Turabian StyleGuglielmo, Savoini, De Marinis Pietro, Casson Andrea, Abhishek Dattu Narote, Riccardo Guidetti, Stefano Bocchi, and Valentina Vaglia. 2025. "Sustainability Assessment of Rice Farming: Insights from Four Italian Farms Under Climate Stress" Agriculture 15, no. 17: 1797. https://doi.org/10.3390/agriculture15171797
APA StyleGuglielmo, S., Pietro, D. M., Andrea, C., Narote, A. D., Guidetti, R., Bocchi, S., & Vaglia, V. (2025). Sustainability Assessment of Rice Farming: Insights from Four Italian Farms Under Climate Stress. Agriculture, 15(17), 1797. https://doi.org/10.3390/agriculture15171797
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