Next Article in Journal
Writing Is Coding for Sustainable Futures: Reimagining Poetic Expression Through Human–AI Dialogues in Environmental Storytelling and Digital Cultural Heritage
Previous Article in Journal
The Impact of Mergers and Acquisitions on Firm Environmental Performance: Empirical Evidence from China
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A KPI-Based Framework for Evaluating Sustainable Agricultural Practices in Southern Angola

by
Eduardo E. Eliseu
1,2,
Tânia M. Lima
2,3 and
Pedro D. Gaspar
2,3,*
1
Department of Earth and Marine Sciences, University of Namibe, Farol de Noronha, Moçâmedes CP 274, Namibe, Angola
2
Department of Electromechanical Engineering, University of Beira Interior, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
3
C-MAST—Center for Mechanical and Aerospace Science and Technologies, Calçada Fonte do Lameiro, 6201-001 Covilhã, Portugal
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(15), 7019; https://doi.org/10.3390/su17157019
Submission received: 31 May 2025 / Revised: 18 July 2025 / Accepted: 31 July 2025 / Published: 1 August 2025

Abstract

Agricultural production in southern Angola faces challenges due to unsustainable practices, including inefficient use of water, fertilizers, and machinery, resulting in low yields and environmental degradation. Therefore, clear and measurable indicators are needed to guide farmers toward more sustainable practices. The scientific literature insufficiently addresses this issue, leaving a significant gap in the evaluation of key performance indicators (KPIs) that can guide good agricultural practices (GAPs) adapted to the context of southern Angola, with the goal of promoting a more resilient and sustainable agricultural sector. So, the objective of this study is to identify and assess KPIs capable of supporting the selection of GAPs suitable for maize, potato, and tomato cultivation in the context of southern Angolan agriculture. A systematic literature review (SLR) was conducted, screening 2720 articles and selecting 14 studies that met defined inclusion criteria. Five KPIs were identified as the most relevant: gross margin, net profit, water use efficiency, nitrogen use efficiency, and machine energy. These indicators were analyzed and standardized to evaluate their contribution to sustainability across different GAPs. Results show that organic fertilizers are the most sustainable option for maize, drip irrigation for potatoes, and crop rotation for tomatoes in southern Angola because of their efficiency in low-resource environments. A clear, simple, and effective representation of the KPIs was developed to be useful in communicating to farmers and policy makers on the selection of the best GAPs in the cultivation of different crops. The study proposes a validated KPI-based methodology for assessing sustainable agricultural practices in developing regions such as southern Angola, aiming to lead to greater self-sufficiency and economic stability in this sector.

1. Introduction

Intensifying sustainable agricultural production should be the main strategic objective for the coming decades [1]. Crop production with unsustainable practices and poor development in the agricultural sector led Angola to join the Capacity Development for Agricultural Innovation Systems (CDAIS) project, which aims to make agricultural innovation systems more effective and sustainable [2].
Most Angolan farmers depend on the rains to grow a variety of crops. The drought, mainly in the southern region, has caused a sharp decline in agricultural production at a national level, and as a result, has increased the need for imports, mainly of maize [3].
There is a lack of knowledge among farmers in Angola regarding the key performance indicators (KPIs) for selecting good agricultural practices (GAPs). Of the 2,289,644 family farm holdings (FFH), approximately 228,964 (10%) use inorganic fertilizers, and 526,618 (23%) use agricultural practices with organic fertilizers in the cultivation of crop varieties. At the provincial level, the provinces with the highest proportions of FFH using inorganic fertilizers are Huambo with around 29%, Luanda with around 28% involved in agricultural production, and Kwanza South with around 18% [4].
The sustainability of GAPs is generally assessed using agro-environmental indicators [5]. With the growing awareness of environmental problems in recent decades, nitrogen use efficiency and water use efficiency are examples of agro-environmental indicators that have been developed to assess the adverse effects of cropping and farming systems, such as agrochemicals and water pollution by nitrates, phosphates, and pesticides [6].
An important challenge for the research community is to identify clear, scientifically based indicators that are accessible and capable of summarizing multiple dimensions of sustainability to support decision-making, preferably in ways that allow a direct comparison of policy alternatives [1].
Different environmental, economic, social, and technological performance indicators are to be found in the literature and applied in the agricultural sector to quantify the production of different crops in different parts of the world [7,8,9].
In the set of articles found, there was one article that investigated the use of KPIs to study the sustainability of farms in Poland, but it did not carry out an SLR to evaluate the different KPI methods. This shortcoming led us to evaluate the main KPIs capable of guiding GAPs applied to various crops [10].
This literature review examines the environmental, technological, economic, and social performance indicators used in agricultural systems in different countries to enhance sustainability, resilience, and competitiveness. This literature review has three objectives:
  • To provide scientific evidence on the impact of KPIs on the selection of GAPs in the agricultural sector and to promote farmers’ income from agricultural production in southern Angola.
  • To contribute to the scientific knowledge of agricultural science professors and researchers who discuss the promotion of the agricultural sector through the institutionalization of KPIs and GAPs in southern Angola.
  • To identify the environmental, technological, economic, and social KPIs that have made it possible to quantify different sustainable agricultural practices in various countries and to replicate them for the agricultural practices currently developed in southern Angola.
Thus, this study aims to contribute to the ongoing international debate on sustainability metrics in agriculture by addressing a critical gap: the operationalization of GAPs through measurable, context-specific KPIs in southern Angola, and that can be adapted to low- and middle-income countries. Recent global initiatives, such as the FAO’s SAFA Guidelines and the SDG framework, emphasize the need for measurable indicators, but their application does not focus on the adaptation to local agricultural, socio-economic, and environmental contexts. Existing studies in the scientific literature present generic sustainability indicators or focus on high-income countries with robust data systems. On the other hand, this work proposes a replicable framework that integrates an SLR, standardization procedures, and a composite sustainability index tailored to the semi-subsistence farming systems in southern Angola. The use of crop-specific KPIs combined with a practical decision-support perspective distinguishes this approach from prior works and provides a tool for the basis of data-informed policy design and farmer-level decision-making in resource-constrained settings.

2. Methods

To systematically analyze the information related to the KPIs applied in agricultural GAPs, a series of inclusion and exclusion criteria (see Table 1) was designed for data extraction, aimed at specific information on the main environmental, technological, economic, and social indicators applied in the agricultural production of different crops.
The objective of our research study led us to include all articles that dealt with KPIs applied to GAPs in the development of agricultural activities. When including articles, restrictions were applied based on the date of publication, i.e., all documents written before the selected date were considered invalid (excluded). Articles referring to studies that addressed the use of KPIs in the health sector, agricultural waste, environmental conservation, agricultural management practices, and agricultural economics were also excluded.
Quantitative and qualitative analysis of previous articles with case studies performed in different regions enabled the identification of themes, content, and patterns in the different KPIs applied to different GAPs. A thematic synthesis framework guided the analysis to categorize these codes into overarching themes, allowing for the identification of commonly adopted KPIs and GAPs in different crop cultures and contexts. A quantitative method was employed to highlight the prevalence of KPI-driven GAPs (e.g., net profit when growing maize with drip irrigation). This integrated qualitative and quantitative approach facilitated a thorough synthesis of KPIs and GAPs, offering a clear summary of the key KPIs that could be adapted to the context of southern Angola’s existing GAPs.
An SLR article provides a wide range of knowledge for different users of the analysis. In these documents, the information provided should be explicit, accurate and specific regarding the purpose and methods employed in developing the SLR (the way each of the studies was identified and selected), and as well as the main findings, including the study characteristics, contributors, and outcomes of the meta-analyses. To perform this SLR, Science Direct served as the primary database. The literature search focused on KPIs related to GAPs in regions with comparable crop profiles. While this study concentrates on southern Angola, research from other regions was also included to enhance diversity and enable comparative analysis. Abstracts of the chosen articles were reviewed to assess their relevance and appropriateness for the study. The bibliographical references of the selected publications were used as a source and a search for articles with similar and relevant themes. The terms included in the search were: “Agriculture” and “Key Performance” and “Indicators”. We used “Good Practices” AND “Agricultural” AND “Key AND Performance Indicator” OR “Agricultural”. The Boolean operators “AND” and “OR” were used to combine the above search terms and obtain the targeted search results. The articles included in the SLR presented GAPs and KPIs applied in the agricultural sector in different countries around the world. The articles met the following inclusion criteria: (1) the article discussed and focused on GAPs, (2) the article was published between 2019 and 2024, (3) the article described GAPs, (4) the article described KPIs, (5) studies related to environmental, technological, economic, and social KPIs in the agricultural sector, (6) studies and scenarios of GAPs and KPIs in the agricultural sector written in English (Appendix A).
The selection of articles in the first phase of our research followed the inclusion and exclusion criteria. Articles that did not meet the selection criteria were excluded as invalid for the research. The initial search with search strings retrieved 2720 articles from the years 2019 to 2024. From these, 367 review articles and 1989 research articles were evaluated, resulting in a total exclusion of 2356 articles. We excluded 31 encyclopedias, 233 book chapters, 30 conference abstracts, 2 case reports, 1 conference briefing, 1 discussion, 9 editorials, 18 mini-reviews, 1 practice guideline, 10 short communications, 1 software publication, and 27 others. The articles with titles of applied energy, twenty nine (29), marine policy, twenty six (26), computers and industrial engineering, nineteen (19), technological forecasting and social change, nineteen (19), cities, seventeen (17), resources, conservation and recycling, seventeen (17), construction and building materials, fifteen (15), Journal of Business Research, fifteen (15), International Journal of Production Economics, fifteen (15), energy conversion and management, fourteen (14), expert systems with applications, fourteen (14), and resource policy, fourteen (14).
Figure 1 shows the SLR process, including identification, screening, and eligibility stages, based on the PRISMA methodology. The selection process began with a review of the titles of 2720 articles and continued with a review of the titles and abstracts. A total of 2663 documents were excluded because they were outside the scope of the topic. The screening process produced 14 documents eligible for full-text review. Additionally, the references of these documents were manually examined to identify any relevant studies not retrieved in the initial search. This study assesses KPIs and GAPs across different countries. The selected articles represent multiple regions, with eight studies from Asia, two from Europe, one from Africa, and three from the Americas.

3. Good Agricultural Practices in Angola

In the agricultural sector, GAPs are important in achieving the objectives of sustainable agriculture and the Sustainable Development Goals, but there is a lack of generalizable knowledge about the reasons why farmers apply sustainable agricultural practices. It is important to know the main motivations that have led farmers to adopt GAPs in the agricultural production chain [11].
Agriculture in Angola is mostly rainfed. The practice of drip irrigation, which is of great importance for increasing productivity and agricultural production, both in quantitative and qualitative terms, is still very limited in the country. During the “cacimbo” or dry season, agricultural activity is very limited since it is a dry period. As such, farming households that practice agricultural production during this period resort to the use of irrigation to grow crops, dedicating themselves mainly to growing vegetables to increase food production. Currently, the main agricultural practices developed in Angola to diversify the economy, mitigate the import of basic food products, and promote exports are as follows [12]:
  • Drip irrigation: This GAP involves lateral drip tape irrigation with a spacing significantly greater than 20 cm, in comparison with the intermittent irrigation control treatment, which uses a fixed five-day irrigation interval [13,14,15]. It is a GAP that conserves water in regions facing scarcity, as all the irrigation water is directed precisely to the plant and meets the plant’s needs [13];
  • Crop rotation: Crop rotation involves growing leguminous and non-leguminous crops on the same plot of agricultural land. For example, different crops grown by farmers alter the microbial activity and decomposition rates of organic material in the cultivated area, consequently significantly affecting the levels of the soil’s organic carbon fractions [14]. Crop rotation is a good GAP that can help mitigate the negative impacts that can be achieved by some crops that are not resistant to a long period of water absence, thus contributing to the sustainability of agricultural systems. By applying this practice, farmers contribute to soil improvement and conservation, but it can also influence soil organic carbon fractions and arsenic content [15];
  • Inorganic fertilizers: The application of fertilizers (e.g., nitrogen and potassium) depends on the type of crop and the availability of water for irrigation to each hectare of agricultural land, e.g., 270 kg of nitrogen are applied to a hectare of herbaceous crops (artificially irrigated), while 110 kg are applied to the same plot of land for woody crops [16];
  • Organic fertilizers: Once organic matter has reached the right level of growth, farmers collect it, such as grass, tree branches, and fruit scraps, and deposit it in the growing areas to fertilize the soil. The organic matter contained in organic fertilizers improves the structure and nutrient composition of the soil and gives the soil a high water and nutrient retention capacity. Microbial activity is stimulated by the presence of organic fertilizers in the soil, which improves the processes of decomposition of crop residues, plant growth, and the general cycle of nutrients, contributing to the strengthening of agricultural sustainability and respect for the environment [17,18]. Organic fertilizers are the most economical, and policies should be created to support farmers to adhere to organic GAP, contributing to environmental protection, and the sustainability of alternative development projects [19,20].
Table 2 shows the indicators (colored squares) and the numbers corresponding to the year, numbering, and title of the article shown in Appendix A. The numbers with an asterisk are the articles searched manually. It includes the selected KPIs grouped under economic, environmental, and technological dimensions. These indicators were derived from the SLR and serve as measurable variables to evaluate the sustainability of each GAP.

4. Scoring Specific and Measurable Main Agricultural KPIs

Several indicators need to be calculated at different spatial scales to assess their contribution in agriculture to the sustainability issues of the selected agricultural practices. The properties of these indicators must be addressed before the indicators are selected and applied [21].
From the point of view of agricultural sustainability, economic (financial strength, farmer’s income, annual net income, profit for the producer caused by irrigation, gross margin, net profit and payments for environmental measures), environmental (soil conservation, primary production of inputs, efficiency in the use of energy, and efficiency in the use of nitrogen), social (percentage of irrigated agricultural areas, land tenure, uniformity of water distribution, efficiency of fertilizer application, and uniformity of fertilizer distribution) and technological (agricultural machinery and machine energy) factors, it is possible to quantify the performance of drip irrigation practices, crop rotation with inorganic fertilizers and organic fertilizers. Performance indicators are used to provide an assessment of progress towards an established goal or objective of a given agricultural practice [22]. Furthermore, the indicator shows a high sensitivity to changes in the fuel consumption of machinery and irrigation resources in the agricultural sector [23]. The description of each of the KPIs and the GAP is shown in Table 3.
Economic KPIs are tools used by farmers to assess the economic situation of an agricultural business in a region or country for macroeconomic control and to reduce the accuracy of economic indicators to create potential wealth [24]. Farmers can make a valuable assessment of production, as well as learn about emerging economic trends and the situation of companies in the agricultural sector with the help of economic KPIs [25].
Companies in the agricultural sector need to evaluate their environmental performance, measure the circular economy, quantify the carbon index, and calculate the ratio of ecological management in recent years. Teachers and researchers have applied strategies to create environmental KPIs that make it possible to assess sustainability and thus reward managers and workers in the agricultural sector [26].
Social KPIs also provide farmers with recommendations on sustainable practices to implement in their agricultural fields. In addition, they can serve as a tool to audit and guide family and corporate farmers to comply with external certifications, to be internationally competitive, and, at the same time, they can be used by public organizations to assess degrees of sustainability in the agricultural sector [27].
Agricultural technology is crucial to optimizing farming practices and making them more sustainable [28]. Technological indicators assess the performance and impact of different agricultural practices in low- and middle-income countries and help define policies and investment priorities to increase agricultural growth and productivity [29].
Table 4, Table 5, Table 6 and Table 7 show the specific and measurable KPIs for maize, potato, and tomato crops. Table 4 includes the parameters for drip irrigation, while Table 5 does so for crop rotation. Table 6 shows the specific and measurable KPIs for inorganic fertilizers, and Table 7 for organic fertilizers. The selected crops (maize, tomato, and potato) were chosen based on their combined agronomic relevance, dietary importance, and regional production potential in Southern Angola. These crops represent distinct agronomic systems (cereal and vegetables) and are widely cultivated by FFH in the region under study.
The KPI scoring criteria presented in Table 4, Table 5, Table 6 and Table 7 were assigned according to the properties proposed by the United Nations (UN), which are closely related to their identification, definition, and calculation. These properties include being specific (focused on a clear aspect) and measurable (capable of being presented as a reliable numerical value). This raw data was collected from secondary sources for each selected KPI across different GAPs [30,31,32].
Table 3. KPIs applied to the agricultural sector.
Table 3. KPIs applied to the agricultural sector.
KPIDescription
EconomicFinancial strengthWith this indicator, it is possible to assess the annual income of suppliers of products from the field so that they can adhere to payment by check. It also assesses the investments made in large land holdings so that sub-suppliers can be provided with product storage facilities to avoid difficulties when selling [33].
Farmer’s incomeThe quantification of the yield of each hectare of land is carried out by subtracting the modelled fixed costs of production and the total variables from the total income, including subsidies for organic farming [34].
Gross marginIt allows you to quantify the gross benefit (the total market value of production) minus the total costs paid (cost of inputs purchased and labor hired) [35].
Net profitNet profit (NP) is quantified from the marginal profit (MP) for an irrigated olive grove, considering the current cost of irrigation water for the olive grove and a high cost of water [36].
Payments for environmental and climate measuresPayments related to the area for agro-environment–climate measures (AECM) and the type of production, in organic and conventional farming [37].
Profit for the producer caused by irrigationAllows the product of the economic productivity of irrigation water to be quantified in cubic meters of irrigation water applied to the field [38].
EnvironmentalSoil conservationThis indicator was calculated by adding up the areas covered by native grasslands, swamps, woodlands, or native forests and dividing by the area of the cadastral unit [39].
Primary production of inputsThe amount of energy generated by the agricultural system in the form of the production of different products per unit of energy produced during farming by farmers [10].
Energy use efficiencyThis is calculated by applying the crop yield in combination with its coefficient and energy equivalent. This indicator allows farmers to assess the degree of sustainability of different products in the cultivation system of crop varieties, i.e., from an energy point of view, the actual ratio of the energy input of each component to the total energy output for each treatment [40].
Water use efficiencyThis KPI allows farmers to quantify the ratio between crop yield and evapotranspiration for each season [41].
Nitrogen use efficiencyNitrogen use efficiency is quantified based on total N absorption in the fertilized treatment, total N absorption in the unfertilized treatment, and the rate of N fertilizer application [42].
SocialPercentage of agricultural area irrigatedThe ratio between the area of irrigated land and the total area of land cultivated by farmers [43].
Land tenureThis is access to state land and how this land is owned or traded. Land tenure refers to the rights and origins of land and its functions, duties, laws, responsibilities, transition, ownership, and security [44].
TechnologicalAgricultural machineryIt is relatively more efficient and economical than labor and has a positive impact on farmers’ agricultural income [9].
Machine energyDuring their manufacture, agricultural machinery involves indirect energy. This indicator quantifies the sum of the total weight of the machine, the specific number of working hours for each run, and the number of applications in field operation divided by the useful life of the machine [45].
Table 4. Specific and measurable KPIs in drip irrigation for maize, potato, and tomato crops.
Table 4. Specific and measurable KPIs in drip irrigation for maize, potato, and tomato crops.
KPISpecific (S)Measurable (M)
EconomicFinancial strengthNot availableNot available
Farmer’s incomeNot availableNot available
Profit for the producer caused by irrigationNot availableNot available
Gross marginDrip irrigationThe cultivation gross margin varies for
maize from 638.5 to 1151.7 EUR/ha [46,47]
potato from 1396.19 to 2605.90 EUR/ha [48]
tomato from 2609.19 to 7104.78 EUR/ha [48,49]
Net profitDrip irrigationThe growing net profit varies for
maize from 279.6 to 4944 EUR/ha [50,51]
potato from 1706.65 to 2933.95 EUR/ha [52]
tomato from 1652.27 to 6352.18 EUR/ha [48,53]
Payments for environmental and climate measuresNot availableNot available
EnvironmentalSoil conservationNot availableNot available
Primary production with internal inputsNot availableNot available
Energy use efficiencyNot availableNot available
Water use efficiencyDrip irrigationWater use efficiency in cultivation varies for
maize from 23.70 to 49% [41]
potato from 40 to 46.1% [54,55]
tomato from 7.8 to 21.4 [56,57]
Nitrogen use efficiencyDrip irrigationNitrogen use efficiency in cultivation varies for
maize from 66.2 to 90% [58,59]
potato from 48.60 to 81.67% [60]
tomato from 50 to 73% [61]
SocialPercentage of agricultural area irrigatedNot availableNot available
Land tenure Not available
TechnologicalAgricultural machineryNot availableNot available
Machine energyDrip irrigation Machine energy use in cultivation varies for
maize from 15,340 to 21,146 MJ/ha [62]
potato from 64.8 to 6880 MJ/ha [63,64]
tomato from 728.7 to 11,791.7 MJ/ha [65,66]
Table 5. Specific and measurable KPIs in crop rotation for maize, potato, and tomato.
Table 5. Specific and measurable KPIs in crop rotation for maize, potato, and tomato.
KPISpecific (S)Measurable (M)
EconomicFinancial strengthNot availableNot available
Farmer’s incomeNot availableNot available
Profit for the producer caused by irrigationNot availableNot available
Gross marginCrop rotation The gross margin in cultivation varies for
maize from 1000 to 6000 EUR/ha [67]
potato from 1000 to 5056 EUR/ha [68]
tomato from 500 to 3000 EUR/ha [69]
Net profitCrop rotation The growing net profit varies for
maize from 122.63 to 379.77 EUR/ha [70,71]
potato from 20 to 8100 EUR/ha [72,73]
tomato from 484.40 to 7185.2 EUR/ha [74]
Payments for environmental and climate measuresNot availableNot available
EnvironmentalSoil conservationNot availableNot available
Primary production with internal inputsNot availableNot available
Energy use efficiencyNot availableNot available
Water use efficiencyCrop rotation Water use efficiency in cultivation varies for
maize from 8 to 18% [75]
potato from 12.5 to 35.3% [76,77]
tomato from 15.5 to 23.4% [78]
Nitrogen use efficiencyCrop rotation Nitrogen use efficiency in cultivation varies for
maize from 46 to 85% [79]
potato from 6.14 to 50% [80,81]
tomato from 12.5 to 64% [82,83]
SocialPercentage of agricultural area irrigatedNot availableNot available
Land tenureNot availableNot available
TechnologicalAgricultural machineryNot availableNot available
Machine energyCrop rotationMachine energy in maize cultivations varies for
maize from 1000 to 8000 MJ/ha [84]
potato from 1120 to 2780 MJ/ha [85,86]
tomato from 826.70 to 1067.56 MJ/ha [87]
Table 6. Specific and measurable KPIs with inorganic fertilizers in maize, potato, and tomato crops.
Table 6. Specific and measurable KPIs with inorganic fertilizers in maize, potato, and tomato crops.
KPISpecific (S)Measurable (M)
EconomicFinancial strengthNot availableNot available
Farmer’s incomeNot availableNot available
Profit for the producer caused by irrigationNot availableNot available
Gross marginInorganic fertilizersThe gross margin in cultivation varies for
maize from 706 to 1187 EUR/ha [88]
potato from 412 to 1537 EUR/ha [89]
tomato from 1419.26 to 8332.87 EUR/ha [90,91].
Net profitInorganic fertilizersNet profit cultivation varies for
maize from 1220.56 to 1853.01 EUR/ha [92]
potato from 621.38 to 2414.26 EUR/ha [93]
tomato from 2218.38 to 3904.15 EUR/ha [94].
Payments for environmental and climate measuresNot availableNot available
EnvironmentalSoil conservationNot availableNot available
Primary production with internal inputsNot availableNot available
Energy use efficiencyNot availableNot available
Water use efficiencyInorganic fertilizersWater use efficiency in cultivation varies for
maize from 60 to 80% [95]
potato from 5.9 to 47.8% [96]
tomato from 22.63 to 62% [97,98]
Nitrogen use efficiencyInorganic fertilizersNitrogen use efficiency in cultivation varies for
maize from 42 to 68% [82]
potato from 48.60 to 81.67% [99]
tomato from 15 to 69% [100]
SocialPercentage of agricultural area irrigatedNot availableNot available
Land tenureNot availableNot available
TechnologicalAgricultural machineryNot availableNot available
Machine energyInorganic fertilizersMachine energy in cultivations varies for
maize from 589.38 to 1739.5 MJ/ha [45]
potato from 866 to 950 [101]
tomato from 142.69 to 530.3 MJ/ha [102,103]
Table 7. Specific and measurable KPIs for growing maize, potatoes, and tomatoes with organic fertilizers.
Table 7. Specific and measurable KPIs for growing maize, potatoes, and tomatoes with organic fertilizers.
KPISpecific (S)Measurable (M)
EconomicFinancial strengthNot availableNot available
Farmer’s incomeNot availableNot available
Profit for the producer caused by irrigationNot availableNot available
Gross marginOrganic fertilizersThe cultivation gross margin varies for
maize from 24.97 to 139.21 EUR/ha
potato from 2850 to 6288 EUR/ha [104]
tomato from 355.57 to 3640.28 EUR/ha [105]
Net profitOrganic fertilizersNet profit from cultivation varies for
maize from 812.81 to 3500.97 EUR/ha [50]
potato from 830.2 to 7211.96 EUR/ha [106,107]
tomato from 6339.29 to 9248.59 EUR/ha [108].
Payments for environmental and climate measuresNot availableNot available
EnvironmentalSoil conservationNot availableNot available
Primary production with internal inputsNot availableNot available
Energy use efficiencyNot availableNot available
Water use efficiencyOrganic fertilizersWater use efficiency in cultivation varies for
maize from 9.9 to 58.4% [50]
potato from 31 to 77% [109,110]
tomato from 25 to 40% [111]
Nitrogen use efficiencyOrganic fertilizersNitrogen use efficiency in cultivation varies for
maize from 20 to 45% [112]
potato from 33 to 44.1% [113,114]
tomato from 10 to 69% [115]
SocialPercentage of agricultural area irrigatedNot availableNot available
Land tenureNot availableNot available
TechnologicalAgricultural machineryNot availableNot available
Machine energyOrganic fertilizersMachine energy in cultivation varies for
maize from 237.60 to 6217.4 MJ/ha [116,117]
potato from 1582.61 to 3206.48 MJ/ha [116,118]
tomato from 835 to 13,006 MJ/ha [119]
Table 2 includes all candidate KPIs identified from the literature review, while Table 3, Table 4, Table 5, Table 6 and Table 7 present the refined set of indicators selected based on data availability, measurability, and relevance to the studied GAPs. Only five indicators (gross margin, net profit, water use efficiency, nitrogen use, and machine energy) were retained and used for standardization. These five indicators were chosen due to: (1) availability of consistent data from reliable sources, (2) quantitative comparability across practices, and (3) relevance to sustainability dimensions (economic, environmental, technological).
Therefore, the most suitable KPIs for which S, M, and total (T) data can be found are economic (gross margin and net profit), environmental (water use efficiency and nitrogen use efficiency), and technological (machine energy), according to the data presented in Table 8. Social KPIs, such as percentage of agricultural area irrigated and land tenure, initially considered during the KPI selection process were excluded from the final model due to limited up-to-date data availability and lack of quantitative comparability. These constraints limited their inclusion. The maximum weight of each KPI in Table 8 is one (1) when the KPI has two KPI properties (specific and measurable). Some of the KPIs in Table 4 are not included in Table 8 (for example, payments for environmental and climate measures) because they are not specific (S) to a particular culture, like southern Angola, and measurable (M). Moreover, weights in Table 8 were assigned equally to all KPIs in all GAPs. This decision reflects an initial assumption of equal importance that can be changed using stakeholders input or empirical weighting methods.

5. Mathematical Formulation

In many scientific papers, motivated by a variety of circumstances, the process of standardization has been used. In assessing the sustainability of a GAP, the main motivation for standardization is to transform the measurements of KPIs applied to different GAPs in the cultivation of crop varieties, normally obtained in different units, into an appropriate and common unit of measurement, to compare them or prepare them for inclusion in an aggregate sustainability score [120,121].
Five specific indicators were selected first, as shown in Table 8, and then an index was formulated to quantify the sustainability of each KPI. It is then necessary to relate them in such a way that they can be presented or expressed in a single quantifiable value.
The analysis of the results in Table 4, Table 5, Table 6 and Table 7 allows us to emphasize the heterogeneity of data sources and potential comparability issues across countries and crops. While all data were drawn from peer-reviewed publications and reputable institutional reports, regional differences in measurement methods or reporting may introduce variation. To moderate this, we used normalized ranges and excluded outliers to improve consistency. Equation (1) makes it possible to relate them using an association function for each of these KPIs. The procedure is carried out for the minimum and maximum values of the economic, environmental, and technological KPIs to obtain a range of values for the overall sustainability index.
For each indicator:
-
Select a maximum, max(xi), and a minimum, min(xi), for each indicator separately for the value ranges of these values (minimum and maximum).
-
Evaluate the increase and decrease in Sij as xij grows. The indicator directionality allows us to classify each KPI as either a benefit (positive indicator, where higher values are desirable) or a cost (negative indicator, where lower values are preferable).
The increase in the membership function Sij increases with the indicator xi, their relationship is expressed by the following:
            S i j = x i j min x i max x i min x i ×   100 ,   when   x i j ,   j   is   a   positive   indicator m a x ( x i ) x i j m a x ( x i ) m i n ( x i ) ×   100 ,   when   x i j ,   j   is   a   negative   indicator                                                  
In Equation (1), Sij and xij represent, respectively, the standardized value and the original value for indicator i (i = 1, 2, …, n) in reference to sample j (j = 1, 2, …, m), min(xi) and max(xi) are the maximum and minimum values among all the samples for indicator i. The overall sustainability index (S) is the sum of the various indicators, considering the weight (0.5) that each one has in Equation (1) of the index. Considering m indicators for selecting a sustainable agricultural practice, the final mathematical expression is given by Equation (2):
                                                                                S s ; 0.5 = i = 1 m 0.5 · S i j                                                                                                                                                            
The data from the economic, environmental and technological KPIs in Table 4 and the S and M conditions in Table 8 allowed us to develop Table 9, Table 10, Table 11 and Table 12 of maximum (max(xi)), minimum (min(xi)) values of KPIs for the practices of drip irrigation, crop rotation, organic fertilizers and inorganic fertilizers for the maize, potato and tomato crops.
In the process of standardization, the maximum, minimum, and value to test the metrics for gross margin, net profit, water use efficiency, nitrogen use efficiency, and machine energy are required. The manual search of case studies allowed us to obtain the value to test the metrics from Table 9, Table 10, Table 11 and Table 12 to identify which is the best practice that farmers should apply in the cultivation of maize, potato, and tomato for more sustainable, economical, and environmentally friendly agriculture.
The values to test the metrics shown in Table 9, Table 10, Table 11 and Table 12 are based on standardized indicators using equal weights and data from secondary sources. Whilst this ensures comparability, it may not reflect local priorities or conditions. Future work must include a sensitivity analysis to test how different assumptions affect the results. The framework supports the comparison between GAPs. The scalability may be limited by constraints related to digital literacy, market access, extension service capacity, and infrastructure. So, its scalability from southern Angola to the rest of the country or other countries with similar agricultural landscapes requires local adaptation and validation to ensure its relevance and effectiveness in real-world conditions.

6. Application of the Graphical Tool Developed to Case Studies

Based on the results of the mathematical formulation, where the maximum and minimum values for the overall sustainability index were obtained for each test scenario, the most relevant indicators were defined, as well as the mix of energy carrier systems that should be considered in the electricity supply portfolio.
To support the practical application of the proposed sustainability assessment framework, a visual tool was developed to assist local stakeholders—particularly farmers, technicians, and extension agents—in comparing GAPs based on standardized KPI values and the resulting sustainability index. This analogue dashboard is presented in the form of a triangular graph representing the sustainability performance of GAPs applied to cultivation. Each vertex of the triangle represents one sustainability dimension (economic, environmental, and technological). The plotted points correspond to specific GAP scenarios, with their position indicating the balance achieved between the three dimensions. The closer a point is to a given vertex, the greater its relative contribution in that category. Thus, the suitability of each GAP to the sustainability dimensions is given by the larger area that is covered. This graphical tool enables side-by-side interpretation of the performance of each GAP across economic, environmental, and technological dimensions.
Given the limited digital infrastructure and literacy in the region under study, the tool was deliberately designed as non-digital, with the objective to be an easy-to-use support instrument, suitable for field-level use and farmer-training sessions. By interpreting technical results into a simplified and accessible format, the visual dashboard increases the usability of the assessment outcomes and promotes more informed decision-making in real agricultural settings.
The following sections discuss the results obtained from the case studies using the proposed framework.

6.1. Evaluation of the Standardized KPIs for the Maize Crop

The minimum and maximum values from Table 9, Table 10, Table 11 and Table 12 of the GAPs of drip irrigation, crop rotation, inorganic fertilizers and organic fertilizers in maize cultivation when considering the case studies as the metric to test for all the economic, environmental and technological KPIs normalized using Equation (1), are shown in Figure 2. This figure, as the following ones for other case studies, shows the conceptual framework adopted for KPI selection and sustainability assessment. It integrates the three dimensions of sustainability (economic, environmental, technological) evaluated in this research work into a unified evaluation process. This visual representation, besides providing an easy and expeditious way to evaluate sustainability in countries like Angola, supports the transparency and reproducibility of the indicator selection process. The following considerations should be highlighted:
(1)
The high gross margin required to grow maize with GAPs of drip irrigation, crop rotation and the use of inorganic fertilizers lowers its sustainability indices.
(2)
The low gross margin required to grow maize with GAPs that use organic fertilizers contributes to the sustainability index compared to GAPs that use drip irrigation, crop rotation, and inorganic fertilizers.
(3)
Maize cultivation with GAPs that use organic fertilizers and drip irrigation produces higher profits, which increases its indicator value.
(4)
The low net profit values in maize cultivation with inorganic fertilizers and crop rotation contribute to its low sustainability index.
(5)
Sustainability in maize production with organic and inorganic fertilizer GAPs in maize cultivation is based on water use efficiency compared to drip irrigation and crop rotation GAPs.
(6)
The low amount of nitrogen supplied via fertilizers in the maize crop in the crop rotation and drip irrigation GAPs contributes to low sustainability.
(7)
The overall sustainability indices for maize cultivation systems with drip irrigation, inorganic fertilizer use, and crop rotation are based on water use efficiency, compared to other GAPs.
(8)
The reduced machine energy requirements in maize cultivation with GAPs that use organic and inorganic fertilizers, and drip irrigation, also influence its overall sustainability index concerning the crop rotation GAP.

6.2. Evaluation of Standardized KPIs for Potato Cultivation

The minimum and maximum values from Table 9, Table 10, Table 11 and Table 12 of the GAPs of drip irrigation, crop rotation, inorganic fertilizers, and organic fertilizers in potato cultivation when considering the case studies as the metric to test for all the economic, environmental, and technological KPIs normalized using Equation (1), are shown in Figure 3. The following considerations should be highlighted:
(1)
The high gross margin required for potato cultivation with GAPs of crop rotation and use of organic fertilizers lowers its sustainability indices.
(2)
The low gross margin required for potato cultivation with GAPs in the application of drip irrigation and inorganic fertilizers contributes to the sustainability index compared to other GAPs.
(3)
Potato cultivation with GAPs in the use of drip irrigation, and inorganic fertilizers produces higher profits, benefiting its sustainability indicator value compared to other GAPs.
(4)
The sustainability of potato production with the organic fertilizer GAP is based on water use efficiency compared to the GAPs of drip irrigation, crop rotation, and inorganic fertilizers.
(5)
The high amount of nitrogen supplied via fertilizers concerning drip irrigation GAPs and the utilization of inorganic fertilizers which promotes their overall sustainability indices.
(6)
The lower sustainability index of the crop rotation and inorganic fertilizer GAPs in potato production has low nitrogen use efficiency compared to the other GAPs.

6.3. Evaluation of Standardized KPIs for Tomato Cultivation

The minimum and maximum values from Table 9, Table 10, Table 11 and Table 12 of the GAPs of drip irrigation, crop rotation, inorganic fertilizers and organic fertilizers in tomato cultivation when considering the case studies as the metric to test for all the economic, environmental and technological KPIs normalized using Equation (1), are shown in Figure 4. The following considerations should be highlighted:
(1)
Growing tomatoes with GAPs using crop rotation and organic fertilizers produces higher profits, which benefits their indicator value.
(2)
The capital costs required to grow tomatoes with GAPs using inorganic fertilizers, and drip irrigation penalize their sustainability indices, conditioning farmers to not adhere to these GAPs and opt for more sustainable GAPs.
(3)
The value of water use efficiency in tomato cultivation in rotation with other crops is one of the indicators that promotes the sustainability of this practice, and increases the overall index in relation to the drip irrigation GAP.
(4)
The amount of nitrogen supplied via fertilizers (nitrogen use efficiency) in tomato cultivation in the drip irrigation, crop rotation and organic fertilizer GAPs contributes to high sustainability.
(5)
Low machine energy use in tomato cultivation with GAPs in the use of organic and inorganic fertilizers, and crop rotation influences its overall sustainability index in relation to the drip irrigation GAP.

7. Discussion

The perception of and adherence to available and effective KPIs applied in companies in the agricultural sector can lead to the sustainable development of GAPs and promote positive results in the cultivation of different crops by farmers, such as more efficient production. In this study, it is possible to identify and evaluate the main KPIs, their quality, and how they can be applied to GAPs. Fourteen scientific articles were selected with a variety of KPIs to measure the sustainability of GAP in different countries around the world. The results revealed that the majority of KPIs were developed in drip irrigation practices, crop rotation, inorganic fertilizers, and organic fertilizers. The KPIs found are applied to GAPs in regions with maize, potato, and tomato crops, and it may be possible to adapt them to a specific context and location. Furthermore, normalization was the most used method to validate the economic (gross margin and net profit), environmental (water use efficiency and nitrogen use efficiency), and technological (machine energy) KPIs selected for each of the GAPs and crops.
Several studies presented a set of KPIs for factors including economic (financial strength, farmer’s income, gross margin, net profit and profit for the producer caused by irrigation), environmental (soil conservation, primary production to internal inputs, efficiency in the use of energy, efficiency in the use of water and efficiency in the use of nitrogen), social (percentage of irrigated agricultural areas and land tenure), and technological (agricultural machinery and machine power). However, they did not have an instruction guide to help farmers interpret the results and carry out practical tests, which would be a fundamental guiding element for farmers to monitor and assess whether GAPs are sustainable. Thus, it is necessary to validate the KPIs. This is essential as it facilitates monitoring, standardized assessment of sustainability, and improved agricultural production.
Previous systematic literature reviews have updated a comprehensive list of 133 key sustainable performance indicators (economic, social, and environmental) that can be used to measure the sustainability performance of GAPs [27]. The KPIs developed in various studies help promoters in the agricultural sector to quantify the results of agricultural production. They make it possible to quantify a set of GAPs applied to crop varieties, to help assess productivity progress, target strategic objectives, and lead us to identify the main areas in the agricultural production chain where improvements are needed to support decision-making [175].
The studies included in this review were carried out on different farms and covered different regions of the world, providing a broad perspective of GAPs based on KPIs. Indicators should be adapted to the local context when they are developed and tested in a specific context or country, as each region has unique agricultural systems and information infrastructures [175].
The solution to the problems relating to the lack of water for irrigation in areas with cyclical droughts, the need for water, and the limited water resources that condition and limit the cultivation of potatoes lies in the application of the drip irrigation GAP. This GAP allows water to reach the stem and root of the plant directly, thus reducing water waste and increasing productivity [176]. Growing maize and potatoes using organic fertilizers can increase soil organic carbon levels and improve the biological and physical functioning of the soil [177]. Organic fertilizers also keep the soil healthy and increase plants’ resistance to adverse environmental conditions, as well as improving their properties [178]. Organic farms perform significantly better economically, with organic farms showing the highest growth rate in profitability [179].
Growing maize and potatoes in rotation with other crops increases productivity and mitigates the pressure from weeds, pathogens, and insects [180]. Several studies have shown that the annual crop rotation scheme offers a favorable balance across the sustainability dimensions in the case of tomato production, significantly increasing soil quality, tuber yield, and reducing the water footprint [181].
The use of inorganic fertilizers in agriculture significantly increases the yield and growth of tomatoes [182,183].
The economic (gross margin and net profit), environmental (water use efficiency and nitrogen use efficiency), and technological (machine energy) KPIs were applied to drip irrigation practices, crop rotation, inorganic fertilizers, and organic fertilizers in maize, potato and tomato crops like those grown in Angola. Although these KPIs are applied to GAPs in countries with different socio-economic, technological and social conditions to Angola, they can be replicated for the southern Angolan context as they are applied to crops like those grown in southern Angola. The application of these KPIs in GAPs in the cultivation of maize, potatoes, and tomatoes will allow farmers to quantify (i) the gross margin, i.e., the crop yield, the selling price of the crop harvest, the by-product yield and the selling price of the by-product [46]; (ii) net profit, i.e., the profit from growing different crops to determine the best agricultural practice [51]; (iii) water use efficiency: the ratio between marketable yield and the total evapotranspiration that depends on irrigation efficiency, that is, the volume of water applied per hectare, allowing comparison of irrigation effectiveness across different practices in terms of productivity per unit of water used [169]; (iv) nitrogen use efficiency, i.e., optimizing the total absorption of N in fertilizer integration on land to improve crop yields in agricultural production [146]; and (v) machine energy, i.e., the direct energy used during various agricultural activities, such as creating beds, planting crops, harvesting, threshing and transportation pertaining to the cultivation of different crops [40].
So, the initial objectives of this study were fully accomplished and, in several aspects, exceeded. The research provided scientific evidence on the role of KPIs in selecting GAPs. It offers a structured and replicable methodology, including standardized indicators and a sustainability index, which can support the widespread use of KPIs in agricultural planning and education. Furthermore, it identified and validated a comprehensive set of environmental, technological, economic, and social KPIs, adapting them to the southern Angolan context with practical application to key crops. A mathematical framework for KPI standardization and sustainability assessment is proposed with a simple and clear visual representation that helps and enhances decision-making for farmers.
It should be noted that solutions based on economic, environmental, and technological KPIs have been widely seen as an opportunity for selecting better GAPs in corn, potato, and tomato cultivation in developed and developing countries. In recent years, KPIs applied in the agricultural sector were presented in readable dashboards and have been rapidly adopted by several countries around the world, with governments and policymakers increasingly recognizing their potential as visual tools to quickly represent the performance of an agricultural enterprise. These dashboards present the latest KPIs on a single screen in a concise, easy-to-understand format [184].
To increase robust productivity, agricultural sustainability, and select better agricultural GAPs in the cultivation of corn, potatoes, and tomatoes, southern Angola needs to opt for the following KPIs:
  • Gross margin: This allows farmers to relate total revenue as a percentage to determine the productivity of one euro (EUR 1) of revenue generated [185]. For example, the revenue, production costs, and services provided for one hectare of maize grown with organic fertilizers is EUR 139.21 [104].
  • Net profit: This quantifies the company’s income after paying the workers’ wages, and the cost of buying seeds, farmyard manure, weed control, and pesticides. For example, a cost of EUR 85.936 for corn production using organic fertilizers on a one-hectare plot of land generates a total return of EUR 3520.6 and a net profit of EUR 2661.25 [50].
  • Water use efficiency: This calculates the sum of the total water applied by irrigation, the water used in the profile, and rainfall during the growing season [186]. For example, total irrigation for growing maize using organic fertilizers generates a water use efficiency of 58.4% [50].
  • Nitrogen use efficiency: This allows the yield per unit of nutrient absorbed to be quantified. The average value of nitrogen absorption in maize cultivation with organic fertilizers generates a nitrogen use efficiency of 40.9% [170].
  • Machine energy: This allows the machine’s energy on each hectare during cultivation operations, the success of the tool’s and machine’s work to be quantified [187]. It allows the calculation of the total diesel used during various agricultural activities, such as creating seedbeds, planting crops, harvesting, threshing, and transportation [45]. For example, it was possible to quantify the machine energy value of 871 MJ/ha in the tomato and rice rotation in Marvdasht in Iran [87].
GAPs using irrigation systems (sprinkling, gravity, ditch or furrow, flooding, and bucket or watering cans) and the use of chemical fertilizers are the most common in potato and tomato cultivation in various provinces of southern Angola [4]. Although these GAPs are applied in developed countries with modern technology, it is necessary for southern Angola to apply the sustainable and ecologically correct GAPs guided by the environmental, economic and technological KPIs for sustainable agriculture and to take a significant step forward in the agricultural sector: The practice of using organic fertilizers in maize cultivation can reduce the adverse effects on the environment, maintaining its natural cycles in the recovery process and improving the quality of food. This practice is economically viable when farmers can get a higher price for their product [188]. The practice of drip irrigation in potato cultivation is often preferred over other irrigation methods because of the high efficiency of water application due to reduced losses, surface evaporation, and deep percolation [189]. This method of irrigation allows water to be applied directly to the plant, as the low amount of water can meet all the plant’s needs [13]. The practice of crop rotation in tomato cultivation guarantees long-term yield stability and maintains soil fertility. Rotating tomato cultivation compared to monoculture can reduce the intensive use of pesticides and synthetic fertilizers and mitigate greenhouse gas emissions [190].
It is intended that our analysis may provide valuable insights into the understudied aspects of KPIs and GAPs in several African countries. First of all, research relating to KPIs applied to GAPs in developed and developing countries is still incipient and has received little attention from researchers, academics, governments, and decision-makers in the agricultural sector to increase economic development and productivity. Secondly, sustainability-oriented GAPs in southern Angola based on KPIs is an important research topic. Finally, the KPIs for the sustainable agricultural practices of crop rotation and organic fertilizers (maize cultivation), drip irrigation and organic fertilizers (potato cultivation) and crop rotation and inorganic fertilizers (tomato cultivation) applied in other countries need to be reproduced in southern Angola for sustainable agriculture.
It is pertinent to perform more in-depth investigations of similar studies for a complete appraisal of the overall impact of the KPIs applied to good agricultural practices in maize, potato and tomato cultivation in companies in the agricultural sector, with greater attention to the possible adverse effects that the present GAPs pose as obstacles to the progress of agricultural production, preventing their adoption. By understanding the impact of the application of KPIs on the development of GAPs, it is possible to highlight areas with potential for improvement to boost sustainable agricultural development in southern Angola.
Nevertheless, the academic community, political decision-makers, and the civil society in southern Angola need to apply the KPIs to the GAPs of drip irrigation, crop rotation, organic and inorganic fertilizers in the cultivation of maize, potatoes, and tomatoes to promote sustainable agricultural development.
This article has some limitations. Although the SLR was very exhausting, it has allowed us to retrieve some articles with very similar case studies that were not found because they were not indexed in the databases where the research was carried out or because they were released on the websites of organizations, governmental institutions, or academic societies.
The manual search process consisted of analyzing and verifying reference lists during the full-text selection stage, along with employing a manual search code in Google Scholar to address these issues.
Furthermore, research performed in different areas where KPIs applied to the agricultural sector or GAPs are grouped may not have been retrieved. Furthermore, the application of KPIs to different GAPs might have been underestimated in certain aspects. The majority of the KPIs were applied to GAPs with crops and technologies that do not match the realities of southern Angola.
The SLR allowed us to identify several studies that propose indicator-based tools for evaluating sustainable agriculture, although most of them rely on complex data inputs or multi-criteria methods that are difficult to operationalize in low-resource settings. In contrast, the proposed framework emphasizes simplicity, transparency, and applicability with minimal data requirements, which enhances its feasibility in FFH systems like those in Southern Angola. Moreover, our approach tailors KPI selection and standardization to crop-specific and regional contexts. This local adaptation represents a distinctive contribution to the literature on sustainability evaluation in African agricultural systems.

8. Conclusions

In the literature analyzed in this research, there is potential to use economic, environmental, and technological KPIs to monitor and evaluate the quality and sustainability of GAPs. These identified KPIs can be used as a database to monitor and evaluate GAPs and decision-makers in the agricultural sector, small and large farmers opting for economic, environmental and technological KPIs to quantify the practice of drip irrigation, crop rotation, inorganic fertilizers and organic fertilizers in the cultivation of maize, potatoes, and tomatoes. The results of this study show that most of the KPIs in the included articles were developed to quantify gross margin, net profit, nitrogen use efficiency, water use efficiency, and machine energy in sustainable agricultural practices. Therefore, the majority of KPIs do not present evidence to be validated because they were not specific and did not present a specific value that would make it possible to quantify a particular indicator in a sustainable GAP.
In this study, the values of the economic, environmental and technological KPIs reflect the most appropriate and sustainable GAPs for growing maize, potatoes and tomatoes. The use of organic fertilizers is the best practice for growing maize. This practice requires lower production costs and provides higher net profit (a gross margin of 139 EUR/ha has a net profit of 3500 EUR/ha) and higher water use efficiency of around 58%. Growing maize on one hectare requires only 6217 MJ of machine energy. As an example, the use of organic manure improves the growth performance of young coffee under low water levels while the application of inorganic fertilizers results in more growth at higher water levels [191].
In potato cultivation, the practice of drip irrigation is the most sustainable with a very low cost of production compared to other GAPs; for example, potato production on one hectare of land has a cost of production of EUR 2605 and a net profit of EUR 2933.91. The drip irrigation GAP allows a nitrogen use efficiency of 81% and a machine energy of 6880 MJ. In Tanzania, growing cabbage using drip irrigation is sustainable and has generated positive profits. What is more, drip irrigation is restricted to ground-level crops such as carrots, onions, and French beans, and this practice would significantly improve the yields of these crops [192].
The high and low values of gross margin (3000 EUR/ha) and machine energy (871 MJ/ha), respectively, allow the crop rotation GAP to be the best sustainable practice in tomato cultivation. In this practice, the yield is very high and significant (in one hectare of tomatoes, there is around EUR 7185.2 of net profit) and it enables a nitrogen use efficiency of 61%. In South Africa, two levels of tillage, no-till and conventional tillage were introduced, with two crop rotations, sweet sorghum–pasture vetch–sweet sorghum and sweet sorghum–fallow–sweet sorghum. No-till increased the concentration in the soil of total nitrogen, total organic nitrogen, magnesium, and sodium by 3.19% to 45% compared to conventional tillage [193], contributing to improved nutrient availability for subsequent crop cycles.
The application of the proposed KPI-based framework in southern Angola faces several limitations. Regional variations in data availability and quality can limit the accuracy and comparability of results, while economic limitations may restrict the adoption of certain GAPs. In addition, factors such as local farming traditions, literacy, and risk perceptions, can pose barriers to the acceptance of the recommended GAP. The dataset used in this study includes information sourced from countries with agronomic and economic conditions that, while similar in some respects, differ from the specific context of Angola. This introduces limitations in terms of representativeness and may affect the direct applicability of the results. Thus, field-level validation, stakeholders’ engagement, and phased implementation strategies adapted to local realities are required to overcome these limitations.
Academics and researchers are encouraged to conduct further studies on KPIs for GAP in developing countries that share similar climatic conditions, soil types, energy resources, human resources, technology levels. Comparative analysis with KPIs for GAPs used in developed countries are needed to identify and understand the barriers that delay the application of KPIs for quantifying the sustainability of GAP in developing countries. Additionally, future studies should focus on strengthening the empirical foundation of KPI sets, developing robust mathematical formulations, and rigorously testing their applicability and effectiveness within GAP systems.

Author Contributions

Conceptualization, E.E.E., P.D.G. and T.M.L.; methodology, E.E.E., P.D.G. and T.M.L.; software, E.E.E.; validation, P.D.G. and T.M.L.; formal analysis, P.D.G. and T.M.L.; investigation, E.E.E.; data curation, E.E.E.; writing—original draft preparation, E.E.E., P.D.G. and T.M.L.; writing—review and editing, P.D.G. and T.M.L.; supervision, P.D.G. and T.M.L.; project administration, P.D.G.; funding acquisition, P.D.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Fundação para a Ciência e Tecnologia, grant number UIDB/00151/2020.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to privacy restrictions.

Acknowledgments

The first author gratefully acknowledges the support of a PhD scholarship from the National Institute of Management and Scholarships of Angola, University of Namibe -Angola. The authors would like to express their gratitude to FCT–Fundação para a Ciência e a Tecnologia, I.P. and Centre for Mechanical and Aerospace Science and Technologies (C-MAST) for their support in the form of funding, under the project UIDB/00151/2020 (https://doi.org/10.54499/UIDB/00151/2020 accessed on 18 July 2025; https://doi.org/10.54499/UIDP/00151/2020 accessed on 18 July 2025).

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. List of included studies.
Table A1. List of included studies.
Serial
Number
YearTitle of the PaperType of
Paper
SourceSource CountryReference
12024Group decision-making model for selection of performance indicators for sustainable supplier evaluation in agro-food supply chain.JournalInternational Journal of Production EconomicsChina[33]
22024A comprehensive analysis of the environmental performance of the Uruguayan agricultural sector.Journal Ecological IndicatorsUruguay[39]
32019Performance indicators used to study the sustainability of farms. Case study from Poland.Journal Ecological IndicatorsPoland[10]
42023Agroecosystem multifunctionality of apple orchards in relation to agricultural management and landscape context.Journal Ecological IndicatorsBelgium[34]
52024Maximizing potato tuber yields and nitrogen use efficiency in semi-arid environments by precision fertilizer depth application.Journal European Journal of AgronomyChina[42]
62024Exploring the poverty-reduction benefits of agricultural machinery socialization services in China: implications for the sustainable development of farmers.Journal HeliyonChina[9]
72019Comparative assessment of irrigation systems’ performance: case study in the Triffa agricultural district, NE Morocco.Journal Agricultural Water ManagementMorocco[43]
8 2022Optimal irrigation levels can improve maize growth, yield, and water use efficiency under drip irrigation in Northwest China,Journal WaterVietnam, Thailand, China, Indonesia, Myanmar, and Sri Lanka [41]
92021Water productivity and net profit of high-density olive orchards in San Juan, Argentina,JournalAgricultural Water ManagementArgentina[36]
102022Intensification of rice–pasture rotations with annual crops reduces the stability of sustainability across productivity, economic, and environmental indicators.JournalAgricultural SystemsUruguay[35]
112024Impact of ET and biomass model choices on economic irrigation water productivity in water-scarce basins.JournalAgricultural Water ManagementLebanon[38]
122023Enhancing energy efficiency and reducing carbon footprint in organic soybean production through no-tillage and rye cover crop integration.Journal Journal of Cleaner ProductionJapan[40]
132022Agricultural land tenure system in Iran: an overview.JournalLand Use PolicyIran[44]
142024Exploring the poverty-reduction benefits of agricultural machinery socialization services in China: implications for the sustainable development of farmers.JournalHeliyonChina [9]

References

  1. Pisante, M.; Stagnari, F.; Grant, C.A. Agricultural Innovations for Sustainable Crop Production Intensification. Ital. J. Agron. 2012, 7, e40. [Google Scholar] [CrossRef]
  2. FAO. Angola e FAO Parceria Para a Resiliencia e o Desenvolvimento Rural Sustentável; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2018; Available online: https://openknowledge.fao.org/server/api/core/bitstreams/cd3bd7c7-9e30-48ee-b5db-b1c5ab5c51da/content (accessed on 4 July 2024).
  3. FAO. The Republic of Angola. Drought Expected to Significantly Reduce Cereal Production and Pasture Availability, with Severe Consequences for Food Security in 2021; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2021; Available online: https://openknowledge.fao.org/server/api/core/bitstreams/5c197934-219c-4f56-9c7b-d1305fb629f2/content (accessed on 14 October 2024).
  4. INE. Relatório dos Resultados das Explorações Agropecuária e Aquícolas Familiares, Relatório dos Resultados das Explorações Agropecuária; Instituto Nacional de Estatística (INE): Luanda, Angola, 2022; Available online: https://www.ine.gov.ao/Arquivos/arquivosCarregados//Carregados/Publicacao_638096614135388083.pdf (accessed on 15 October 2024).
  5. Bélanger, V.; Vanasse, A.; Parent, D.; Allard, G.; Pellerin, D. Development of agri-environmental indicators to assess dairy farm sustainability in Quebec, Eastern Canada. Ecol. Indic. 2012, 23, 421–430. [Google Scholar] [CrossRef]
  6. Binder, C.R.; Feola, G.; Steinberger, J.K. Considering the normative, systemic and procedural dimensions in indicator-based sustainability assessments in agriculture. Environ. Impact Assess. Rev. 2010, 30, 71–81. [Google Scholar] [CrossRef]
  7. FAO. Proportion of Agricultural Area Produtive and Sustainable e Agriculture; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2017; Available online: https://www.fao.org/fileadmin/templates/es/SDG/SDG_2.4.1_Methodological_concept_note.pdf (accessed on 15 October 2024).
  8. FAO. Compendium of Agricultural—Environmental Indicators; Ballayan, D., DeSantis, G., Narain, P., Barre, M., Gordon, A., Eds.; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2003; Available online: https://www.fao.org/fileadmin/templates/ess/documents/other_statistics/compendium/agr_env_indic.pdf (accessed on 15 February 2025).
  9. Qiu, H.; Feng, M.; Zhang, X.; Song, Z.; Luo, M.; Wang, J.; Ye, F. Exploring the poverty-reduction benefits of agricultural machinery socialization services in China: Implications for the sustainable development of farmers. Heliyon 2024, 10, e32636. [Google Scholar] [CrossRef]
  10. Lewandowska, C.A.; Piernik, A.; Nienartowicz, A. Performance indicators used to study the sustainability of farms. Case Study from Poland. Ecol. Indic. 2019, 99, 51–60. [Google Scholar] [CrossRef]
  11. Swart, R.; Cristãs, A.; Davis, J.T.; Verburg, P.H. Meta-analyses reveal the importance of socio-psychological factors for farmers’ adoption of sustainable agricultural practices. One Earth 2023, 6, 1771–1783. [Google Scholar] [CrossRef]
  12. INE. Relatório dos Resultados das Explorações Agropecuária e Aquícolas Empresariais. Relatório de Quadros—Volume V(C); Food and Agriculture Organization of the United Nations (FAO): Luanda, Angola, 2022; Available online: https://www.ine.gov.ao/Arquivos/arquivosCarregados//Carregados/Publicacao_638096713703719191.pdf (accessed on 4 July 2024).
  13. Adhikari, J.; Thapa, R. Determinants of the adoption of different good agricultural practices (GAP) in the command area of PMAMP apple zone in Nepal: The case of Mustang district. Heliyon 2023, 9, e17822. [Google Scholar] [CrossRef]
  14. Slijper, T.; Tensi, A.F.; Ang, F.; Ali, B.M. Investigating the relationship between knowledge and the adoption of sustainable agricultural practices: The case of Dutch arable farmers. J. Clean. Prod. 2023, 1471, 138011. [Google Scholar] [CrossRef]
  15. Kama, R.; Ele, J.; Nabi, F.; Aidara, M.; Faye, B.; Diatta, S.; Ma, C.; Li, H. Crop rotation and green manure type enhance organic carbon fractions and reduce soil arsenic content. Agric. Ecosyst. Environ. 2025, 378, 109287. [Google Scholar] [CrossRef]
  16. Pérez-Martín, M.A.; Arora, M.; Monreal, T.E. Defining the maximum nitrogen surplus in water management plans to recover nitrate polluted aquifers in Spain. J. Environ. Manag. 2024, 356, 120770. [Google Scholar] [CrossRef]
  17. FAO. Training Manual for Organic Agriculture; Scialabba, N., Gomez, I., Thivant, L., Eds.; Food and Agriculture Organization of the United Nations (FAO): Rome, Italy, 2015; Available online: https://www.fao.org/fileadmin/templates/nr/sustainability_pathways/docs/Compilation_techniques_organic_agriculture_rev.pdf (accessed on 14 July 2024).
  18. UN; UNEP. Organic Agriculture and Food Security in Africa; United Nations (UN): New York, NY, USA; United Nations Environment Programme (UNEP): Geneva, Switzerland, 2008; Available online: https://www.fao.org/family-farming/detail/en/c/285510/ (accessed on 24 December 2024).
  19. UNODC. Practical Guide Alternative Development and the Environment; United Nations Office on Drugs and Crime (UNODC): Vienna, Austria, 2023; Available online: https://www.unodc.org/documents/alternative-development/Practical_Guide_Report_web.pdf (accessed on 24 December 2024).
  20. FAO. Organic. In Agriculture and Food Security; Niggli, U., Earley, J., Ogorzalek, K., Eds.; Food and Agriculture Organization of the United Nations (FAO): Geneva, Switzerland, 2007; Available online: https://openknowledge.fao.org/server/api/core/bitstreams/0576c3d0-fe53-4804-bf0d-7edd4785d8b5/content (accessed on 1 June 2024).
  21. Chopin, P.; Blazy, J.M.; Guindé, L.; Tournebize, R.; Doré, T. A novel approach for assessing the contribution of agricultural systems to the sustainable development of regions with multi-scale indicators: Application to Guadeloupe. Land Use Policy 2017, 62, 132–142. [Google Scholar] [CrossRef]
  22. FAO. Indicators to Monitor and Evaluate the Sustainnability of Bioeconomy; Food and Agriculture Organization of the Nation (FAO): Rome, Italy, 2019; Available online: https://openknowledge.fao.org/server/api/core/bitstreams/95937318-a0be-40d5-82b2-2277dd98add5/content (accessed on 15 June 2024).
  23. Sinisterra-Solís, N.K.; Sanjuán, N.; Ribal, J.; Estruch, V.; Clemente, G. From farm accountancy data to environmental indicators: Assessing the environmental performance of Spanish agriculture at a regional level. Sci. Total Environ. 2023, 894, 164937. [Google Scholar] [CrossRef] [PubMed]
  24. Zhu, S.; Tian, H.; Wang, C. Economic indicator accuracy and corporate ESG performance. Econ. Lett. 2024, 243, 111907. [Google Scholar] [CrossRef]
  25. Adilov, N.; Alexander, P.J.; Braun, V.; Cunningham, B.M. An economic indicator of the orbital debris environment. J. Spac. Safet. Eng. 2024, 11, 539–545. [Google Scholar] [CrossRef]
  26. Marrucci, L.; Daddi, T.; Iraldo, F. Creating environmental performance indicators to assess corporate sustainability and reward employees. Ecol. Indic. 2024, 158, 111489. [Google Scholar] [CrossRef]
  27. Alonso-Martínez, D.; Jiménez-Parra, B.; Cabeza-García, L. Theoretical framework to foster and assess sustainable agriculture practices: Drivers and key performance indicators. Environ. Indic. Sustain. 2024, 23, 100434. [Google Scholar] [CrossRef]
  28. Shah, W.U.H.; Hao, G.; Yasmeen, R.; Yan, H.; Qi, Y. Impact of agricultural technological innovation on total-factor agricultural water usage efficiency: Evidence from 31 Chinese Provinces. Agric. Water Manag. 2024, 299, 108905. [Google Scholar] [CrossRef]
  29. FAO. Indicators, Agricultural Science and Technology Institutionalization and New National Data Collection Approach; Food and Agriculture Organization of the United Nations (FAO): Johannesburg, South Africa, 2023; Available online: https://openknowledge.fao.org/server/api/core/bitstreams/8056c757-91ae-4e47-851a-a91a422f0f50/content (accessed on 12 October 2024).
  30. Cappiello, C.; Comuzzi, M.; Plebani, P.; Fim, M. Assessing and improving measurability of process performance indicators based on quality of logs. Inform. Syst. 2022, 103, 101874. [Google Scholar] [CrossRef]
  31. UN. Performance Indicators to Monitor and Evaluate the Effectiveness of the Implementation of the Technology Transfer Framework; United Nations (UN): Guimarães, Portugal, 2009; Available online: https://unfccc.int/resource/docs/2009/sb/eng/01.pdf (accessed on 2 October 2024).
  32. UN. Developing the Most Significant and Suitable Smart City Indicators for Smart City Pilot in Knowledge Oasis Muscat (KOM), Sultanate of Oman; United Nations (UN): Guimarães, Portugal, 2019; Available online: https://i.unu.edu/media/egov.unu.edu/event/4886/Presentation-slides.pdf (accessed on 2 October 2024).
  33. Sharma, R.; Kannan, D.; Darbari, J.D.; Jha, P.C. Group Decision Making Model for Selection of Performance Indicators for Sustainable Supplier Evaluation in Agro-Food Supply Chain. Economics 2024, 277, 109353. [Google Scholar] [CrossRef]
  34. Daelemans, R.; Hulsmans, E.; Fockaert, L.; Vranken, L.; Bruyn, L.D.; Honnay, O. Agroecosystem multifunctionality of apple orchards in relation to agricultural management and landscape context. Ecol. Indic. 2023, 154, 110496. [Google Scholar] [CrossRef]
  35. Macedo, I.; Roel, Á.; Velazco, J.I.; Bordagorri, A.; Terra, J.A.; Pittelkow, C.M. Intensification of rice-pasture rotations with annual crops reduces the stability of sustainability across productivity, economic, and environmental indicators. Agric. Syst. 2022, 202, 103488. [Google Scholar] [CrossRef]
  36. Serman, F.V.; Orgaz, F.; Starobinsky, G.; Capraro, F.; Elias, F. Water productivity and net profit of high-density olive orchards in San Juan, Argentina. Agric. Water Manag. 2021, 252, 106878. [Google Scholar] [CrossRef]
  37. Löw, P.; Osterburg, B. Evaluation of nitrogen balances and nitrogen use efficiencies on farm level of the German agricultural sector. Agric. Syst. 2024, 2013, 103796. [Google Scholar] [CrossRef]
  38. Hazimeh, R.; Jaafar, H. Impact of ET and biomass model choices on economic irrigation water productivity in water-scarce basins. Agric. Water Manag. 2024, 292, 108651. [Google Scholar] [CrossRef]
  39. Paruelo, J.M.; Sans, G.C.; Gallego, F.; Baldassini, P.; Staiano, L.; Baeza, S.; Dieguez, H. A comprehensive analysis of the environmental performance of the Uruguayan agricultural sector. Ecol. Indic. 2024, 166, 112385. [Google Scholar] [CrossRef]
  40. Huang, Q.; Gong, Y.; Dewi, R.K.; Li, P.; Wang, X.; Hashimi, R.; Komatsuzaki, M. Enhancing energy efficiency and reducing carbon footprint in organic soybean production through no-tillage and rye cover crop integration. J. Clean. Prod. 2023, 419, 138247. [Google Scholar] [CrossRef]
  41. Liu, M.; Wang, G.; Liang, F.; Li, Q.; Tian, Y.; Jia, H. Optimal Irrigation Levels Can Improve Maize Growth, Yield, and Water Use Efficiency under Drip Irrigation in Northwest China. Water 2022, 14, 3822. [Google Scholar] [CrossRef]
  42. Zhang, N.; Luo, H.; Li, H.; Bao, M.; Liu, E.; Shan, W.; Ren, X.; Jia, Z.; Siddique, K.H.; Zhang, P. Maximizing potato tuber yields and nitrogen use efficiency in semi-arid environments by precision fertilizer depth application. Eur. J. Agron. 2024, 156, 127147. [Google Scholar] [CrossRef]
  43. Alonso, A.; Feltz, N.; Gaspart, F.; Vanclooster, M. Comparative assessment of irrigation systems’ performance: Case study in the Triffa agricultural district, NE Morocco. Agric. Water Manag. 2019, 212, 338–348. [Google Scholar] [CrossRef]
  44. Shirzad, H.; Barati, A.A.; Ehteshammajd, S.; Goli, I.; Siamian, N.; Moghaddam, S.M.; Para, M.; Tan, R.; Janečková, K.; Sklenička, P.; et al. Agricultural land tenure system in Iran: An overview. Land Use Policy 2022, 123, 106375. [Google Scholar] [CrossRef]
  45. Isaak, M.; Yahya, A.; Razif, M.; Mat, N. Mechanization status based on machinery utilization and workers’ workload in sweet corn cultivation in Malaysia. Comput. Electron. Agric. 2020, 169, 105208. [Google Scholar] [CrossRef]
  46. Lima, F.A.; Córcoles, J.I.; Tarjuelo, J.M.; Martínez-Romero, A. Model for management of an on-demand irrigation network based on irrigation scheduling of crops to minimize energy use (Part II): Financial impact of regulated deficit irrigation. Agric. Water Manag. 2019, 2015, 44–54. [Google Scholar] [CrossRef]
  47. López-Mata, E.; Tarjuelo, J.M.; Juan, J.A.; Ballesteros, R.; Domínguez, A. Effect of irrigation uniformity on the profitability of crops. Agric. Water Manag. 2010, 98, 190–198. [Google Scholar] [CrossRef]
  48. Engindeniz, S. Economic analysis of pesticide use on processing tomato growing: A case study for Turkey. Crop Protec. 2006, 15, 534–541. [Google Scholar] [CrossRef]
  49. Oulmane, A.; Chebil, A.; Frija, A.; Benmehaia, A. Water-Saving Technologies and Total Factor Productivity Growth in Small Horticultural Farms in Algeria. Agric. Res. 2020, 9, 585–591. [Google Scholar] [CrossRef]
  50. El-Wahed, M.A.; Ali, E. Effect of irrigation systems, amounts of irrigation water and mulching on corn yield, water use efficiency and net profit. Agric. Water Manag. 2013, 120, 64–71. [Google Scholar] [CrossRef]
  51. Moursy, M.; ElFetyany, M.; Meleha, A.M.I.; El-Bialy, M.A. Productivity and profitability of modern irrigation methods through the application of on-farm drip irrigation on some crops in the Northern Nile Delta of Egypt. Alex. Eng. J. 2023, 62, 349–356. [Google Scholar] [CrossRef]
  52. Tang, J.; Xiao, D.; Wang, J.; Fang, Q.; Zhang, J.; Bai, H. Optimizing water and nitrogen managements for potato production in the agro-pastoral ecotone in North China. Agric. Water Manag. 2021, 253, 106945. [Google Scholar] [CrossRef]
  53. Dou, Z.; Feng, H.; Zhang, H.; Abdelghany, A.E.; Zhang, F.; Li, Z.; Fan, J. Silicon application mitigated the adverse effects of salt stress and deficit irrigation on drip-irrigated greenhouse tomato. Agric. Water Manag. 2023, 289, 108526. [Google Scholar] [CrossRef]
  54. Badr, M.A.; Hussein, S.D.A.; El-Tohamy, W.A.; Gruda, N. Efficiency of Subsurface Drip Irrigation for Potato Production Under Different Dry Stress Conditions. Gesun. Pflan. 2010, 62, 63–70. [Google Scholar] [CrossRef]
  55. Wang, X.; Yang, B.; Jiang, L.; Zhao, S.; Liu, M.; Xu, X.; Jiang, R.; Zhang, J.; Duan, Y.; He, P.; et al. Organic substitution regime with optimized irrigation improves potato water and nitrogen use efficiency by regulating soil chemical properties rather than microflora structure. Field Crops Res. 2024, 316, 109512. [Google Scholar] [CrossRef]
  56. Luo, H.; Li, F. Tomato yield, quality and water use efficiency under different drip fertigation strategies. Sci. Hortic. 2018, 135, 181–188. [Google Scholar] [CrossRef]
  57. Shabbir, A.; Mao, H.; Ullah, I.; Buttar, N.A.; Ajmal, M.; Lakhiar, I.A. Effects of Drip Irrigation Emitter Density with Various Irrigation Levels on Physiological Parameters, Root, Yield, and Quality of Cherry Tomato. Agronomy 2020, 10, 1685. [Google Scholar] [CrossRef]
  58. Sandhu, O.; Gupta, R.; Pense, H.; Jat, M.; Sidhu, H.; Singh, Y. Drip irrigation and nitrogen management for improving crop yields, nitrogen use efficiency and water productivity of maize-wheat system on permanent beds in north-west India. Agric. Water Manag. 2019, 219, 19–26. [Google Scholar] [CrossRef]
  59. Gupta, N.; Singh, Y.; Jat, H.S.; Singh, L.K.; Choudhary, K.M.; Sidhu, H.S.; Gathala, M.K.; Jat, M.L. Precise irrigation water and nitrogen management improve water and nitrogen use efficiencies under conservation agriculture in the maize-wheat systems. Sci. Rep. 2023, 13, 12060. [Google Scholar] [CrossRef]
  60. Shrestha, B.; Darapuneni, M.; Stringam, B.L.; Lombard, K.; Djaman, K. Irrigation Water and Nitrogen Fertilizer Management in Potato (Solanum tuberosum L.): A Review. Agronomy 2023, 13, 2566. [Google Scholar] [CrossRef]
  61. Zotarelli, L.; Dukes, M.; Scholberg, J.M.S.; Muñoz-Carpena, R.; Icerman, J. Tomato nitrogen accumulation and fertilizer use efficiency on a sandy soil, as affected by nitrogen rate and irrigation scheduling. Agric. Water Manag. 2009, 96, 1247–1258. [Google Scholar] [CrossRef]
  62. Jackson, T.M.; Khan, S.; Hafeez, M. A comparative analysis of water application and energy consumption at the irrigated field level. Agric. Water Manag. 2010, 97, 1477–1485. [Google Scholar] [CrossRef]
  63. Chandel, R.; Raj, R.; Kaur, A.; Singh, K.; Kataria, S.K. Energy and yield optimization of field and vegetable crops in heavy crop residue for Indian conditions-climate smart techniques for food security. Energy 2024, 287, 129555. [Google Scholar] [CrossRef]
  64. Brar, A.; Buttar, G.; Thind, H.; Singh, K. Improvement of water productivity, economics and energetics of potato through straw mulching and irrigation scheduling in Indian Punjab. Potato Res. 2019, 62, 465–484. [Google Scholar] [CrossRef]
  65. Nita, K.; Soni, P.; Shivakoti, G.P. Comparative energy input–output and financial analyses of greenhouse and open field vegetables production in West Java, Indonesia. Energy 2013, 53, 83–92. [Google Scholar] [CrossRef]
  66. Çetin, B.; Vardar, A. An economic analysis of energy requirements and input costs for tomato production in Turkey. Renew. Energy 2008, 33, 428–433. [Google Scholar] [CrossRef]
  67. González-García, S.; Almeida, F.; Moreira, M.T.; Brandão, M. Evaluating the environmental profiles of winter wheat rotation systems under different management strategies. Sci. Total Environ. 2021, 770, 145270. [Google Scholar] [CrossRef]
  68. Silva, J.V.; Reidsma, Y.; Ittersum, M.K. Yield gaps in Dutch arable farming systems: Analysis at crop and crop rotation level. Agric. Syst. 2017, 158, 78–92. [Google Scholar] [CrossRef]
  69. Nabahungu, N.; Visser, S. Contribution of wetland agriculture to farmers’ livelihood in Rwanda. Ecol. Indic. 2011, 71, 4–12. [Google Scholar] [CrossRef]
  70. Boansi, D.; Owusu, V.; Donkor, E. Impact of integrated soil fertility management on maize yield, yield gap and income in northern Ghana. Sustain. Futures 2024, 7, 100185. [Google Scholar] [CrossRef]
  71. Garbelini, L.G.; Debiasi, H.; Junior, A.A.B.; Franchini, J.C.; Coelho, A.E.; Telles, T.S. Santo. Diversified crop rotations increase the yield and economic efficiency of grain production systems. Eur. J. Agron. 2022, 137, 126528. [Google Scholar] [CrossRef]
  72. Kik, M.; Claassen, G.; Ros, G.; Meuwissen, M.; Smit, A. FARManalytics—A bio-economic model to optimize the economic value of sustainable soil management on arable farms. Eur. J. Agron. 2024, 157, 127192. [Google Scholar] [CrossRef]
  73. Nasrallah, A.; Belhouchette, H.; Baghdadi, N.; Mhawej, M.; Darwish, T.; Darwich, S.; Faour, G. Performance of wheat-based cropping systems and economic risk of low relative productivity assessment in a sub-dry Mediterranean environment. Eur. J. Agron. 2020, 133, 125968. [Google Scholar] [CrossRef]
  74. Xu, J.; Min, J.; Haijun, S.; Singh, B.P.; Wang, H.; Shi, W. Biostimulants decreased nitrogen leaching and NH3 volatilization but increased N2O emission from plastic-shed greenhouse vegetable soil. Environ. Sci. Pollut. 2021, 29, 6093–6102. [Google Scholar] [CrossRef] [PubMed]
  75. Peng, Y.; Li, Z.; Sun, T.; Zhang, F.; Wu, Q.; Du, M.; Sheng, T. Modeling long-term water use and economic returns to optimize alfalfa-corn rotation in the corn belt of northeast China. Field Crops Res. 2022, 276, 108379. [Google Scholar] [CrossRef]
  76. Xiao, G.; Zhang, Q.; Yao, Y.; Yang, S.; Wang, R.; Xiong, Y.; Sun, Z. Effects of temperature increase on water use and crop yields in a pea–spring wheat–potato rotation. Agric. Water Manag. 2007, 91, 86–91. [Google Scholar] [CrossRef]
  77. Zhang, J.; Yang, J.; An, P.; Ren, W.; Pan, Z.; Dong, Z.; Han, G.; Pan, Y.; Pan, S.; Tian, H. Enhancing soil drought induced by climate change and agricultural practices: Observational and experimental evidence from the semiarid area of northern China. Agric. For. Meteorol. 2017, 243, 74–83. [Google Scholar] [CrossRef]
  78. Yu, T.; Mahe, L.; Li, Y.; Wei, X.; Deng, X.; Zhang, D.; Zhang, D. Benefits of Crop Rotation on Climate Resilience and Its Prospects in China. Agronomy 2022, 12, 436. [Google Scholar] [CrossRef]
  79. Zhu, X.; Ros, G.H.; Xu, M.; Cai, Z.; Sun, N.; Duan, Y.; de Vries, W.H. Long-term impacts of mineral and organic fertilizer inputs on nitrogen use efficiency for different cropping systems and site conditions in Southern China. Eur. J. Agron. 2023, 146, 126797. [Google Scholar] [CrossRef]
  80. Guitarra, H.I.; Karanja, N.N.; Gachene, C.K.; Kamau, S.; Sharma, K.; Schulte-Geldermann, E. Nitrogen and phosphorous uptake by potato (Solanum tuberosum L.) and their use efficiency under potato-legume intercropping systems. Field Crops Res. 2018, 222, 78–84. [Google Scholar] [CrossRef]
  81. Liang, K.; Jiang, Y.; Nyiraneza, J.; Fuller, K.; Murnaghan, D.; Meng, F. Nitrogen dynamics and leaching potential under conventional and alternative potato rotations in Atlantic Canada. Field Crops Res. 2018, 242, 107603. [Google Scholar] [CrossRef]
  82. Valenzuela, H. Optimizing the Nitrogen Use Efficiency in Vegetable Crops. Nitrogen 2024, 5, 106–146. [Google Scholar] [CrossRef]
  83. Jalpa, L.; Mylavarapu, R.S.; Hochmuth, G.J.; Wright, A.L.; Santen, E. Apparent Recovery and Efficiency of Nitrogen Fertilization in Tomato Grown on Sandy Soils. HortTechnology 2020, 30, 204–211. [Google Scholar] [CrossRef]
  84. Rathke, G.W.; Wienhold, B.J.; Wilhelm, W.W.; Diepenbrock, W. Tillage and rotation effect on corn–soybean energy balances in eastern Nebraska. Soil Tillage Res. 2007, 97, 60–70. [Google Scholar] [CrossRef]
  85. Khakbazan, M.; Mohr, R.M.; Huang, J.; Xie, R.; Volkmar, K.M.; Tomasiewicz, D.J.; Moulin, A.P.; Derksen, D.A.; Irvine, B.R.; McLaren, D.L.; et al. Effects of crop rotation on the energy use efficiency of potatoes irrigated with cereals, canola and alfalfa over a 14-year period in Manitoba, Canada. Soil Tillage Res. 2019, 195, 104357. [Google Scholar] [CrossRef]
  86. Hülsbergen, K.J.; Feil, B.; Biermann, S.; Rathke, G.W.; Kalk, W.D.; Diepenbrock, W. A method of energy balancing in crop production and its application in a long-term fertilizer trial. Agric. Ecosyst. Environ. 2001, 86, 303–321. [Google Scholar] [CrossRef]
  87. Houshyar, E.; Dalgaard, T.; Tarazkar, M.H.; Jørgensen, U. Energy input for tomato production what economy says, and what is good for the environment. J. Clean. Prod. 2015, 89, 99–109. [Google Scholar] [CrossRef]
  88. 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. Crop Prot. 2009, 9, 1096–1113. [Google Scholar] [CrossRef]
  89. Dupuis, B.; Nkuriyingoma, P.; Ballmer, T. Economic Impact of Potato Virus Y (PVY) in Europe. Potato Res. 2023, 67, 55–72. [Google Scholar] [CrossRef]
  90. Brown, S.; Kennedy, G. A case study of cash cropping in Nepal: Poverty alleviation or inequity? Agric. Hum. 2005, 22, 105–116. [Google Scholar] [CrossRef]
  91. Schreinemachers, P.; Wu, M.; Uddin, D.; Ahmad, S.; Hanson, P. Farmer training in off-season vegetables: Effects on income and pesticide use in Bangladesh. Food Policy 2016, 61, 132–140. [Google Scholar] [CrossRef]
  92. Zhang, T.; Zou, Y.; Kisekka, I.; Biswas, A.; Cai, H. Comparison of different irrigation methods to synergistically improve maize’s yield, water productivity and economic benefits in an arid irrigation area. Agric. Water Manag. 2021, 243, 106497. [Google Scholar] [CrossRef]
  93. Kilonzi, J.M.; Githui, D.; Pwaipwai, P.; Kawira, C.; Otieno, S.; Kelele, J.; Ng’ang’a, N.; Nyongesa, M.; Mafurah, J.; Kibe, A. Effects of Seed Tuber Size of Potato Varieties on Fungicide Spray Regime, Weed Infestation and Net Farm Income in Potato Production. Potato Res. 2024, 68, 23–47. [Google Scholar] [CrossRef]
  94. Mao, X.; Gu, J.; Wang, F.; Wang, K.; Liu, R.; Hong, Y.; Wang, Y.; Han, F. Yield, Quality, and Nitrogen Leaching of Open-Field Tomato in Response to Different Nitrogen Application Measures in Northwestern China. Plants 2024, 13, 924. [Google Scholar] [CrossRef] [PubMed]
  95. Faloye, O.T.; Ajayi, A.E.; Babalola, T.; Omotehinse, A.O.; Adeyeri, O.E.; Adabembe, B.A.; Ogunrinde, A.T.; Okunola, A.; Fashina, A. Modelling Crop Evapotranspiration and Water Use Efficiency of Maize Using Artificial Neural Network and Linear Regression Models in Biochar and Inorganic Fertilizer-Amended Soil under Varying Water Applications. Water 2023, 15, 2294. [Google Scholar] [CrossRef]
  96. Niu, Y.; Zhang, K.; Khan, K.S.; Fudjoe, S.K.; Li, L.; Wang, L.; Luo, Z. Deficit Irrigation as an Effective Way to Increase Potato Water Use Efficiency in Northern China: A Meta-Analysis. Agronomy 2024, 14, 1533. [Google Scholar] [CrossRef]
  97. Khater, E.G.; Bahnasawy, A.H.; Shams, A.E.S.; Hassaan, M.S.; Hassaan, Y.A. Utilization of effluent fish farms in tomato cultivation. Ecol. Eng. 2015, 83, 199–207. [Google Scholar] [CrossRef]
  98. Daoud, B.; Pawelzik, E.; Naumann, M. Different potassium fertilization levels influence water-use efficiency, yield, and fruit quality attributes of cocktail tomato—A comparative study of deficient-to-excessive supply. Sci. Hortic. 2020, 2072, 109562. [Google Scholar] [CrossRef]
  99. Wang, C.; Zang, H.; Liu, J.; Shi, X.; Li, S.; Chen, F.; Chu, Q. Optimum nitrogen rate to maintain sustainable potato production and improve nitrogen use efficiency at a regional scale in China. A meta-analysis. Agron. Sustain. Dev. 2020, 40, 37. [Google Scholar] [CrossRef]
  100. Badr, M.; Abou-Hussein, S.; El-Tohamy, W. Tomato yield, nitrogen uptake and water use efficiency as affected by planting geometry and level of nitrogen in an arid region. Agric. Water Manag. 2016, 169, 90–97. [Google Scholar] [CrossRef]
  101. Khoshnevisan, B.; Rafiee, S.; Omid, M.; Mousazadeh, H.; Rajaeifar, M.A. Application of artificial neural networks for prediction of output energy and GHG emissions in potato production in Iran. Agric. Syst. 2014, 123, 120–127. [Google Scholar] [CrossRef]
  102. Amirhossein, A.; Ghahderijani, M.; Borghaee, A.; Bakhoda, H. Energy use and environmental impacts analysis of greenhouse crops production using life cycle assessment approach: A case study of cucumber and tomato from Tehran province, Iran. Energy Rep. 2023, 9, 988–999. [Google Scholar] [CrossRef]
  103. Ozkan, B.; Ceylan, R.F.; Kizilay, H. Energy inputs and crop yield relationships in greenhouse winter crop tomato production. Renew. Energy 2011, 36, 3217–3221. [Google Scholar] [CrossRef]
  104. Berentsen, P.; Asseldonk, M. An empirical analysis of risk in conventional and organic arable farming in The Netherlands. Eur. J. Agron. 2016, 79, 100–106. [Google Scholar] [CrossRef]
  105. Maruf, S.A.; Ahmed, J.U.; Khan, J.A. Prospect of off-seasonal vegetable production in Bangladesh: A socioeconomic diagnosis. Qual Quant 2021, 56, 3441–3463. [Google Scholar] [CrossRef]
  106. Timpanaro, G.; Timpanaro, G.; Branca, F.; Cammarata, M.; Falcone, G.; Scuderi, A. Life Cycle Assessment to Highlight the Environmental Burdens of Early Potato Production. Agronomy 2021, 11, 879. [Google Scholar] [CrossRef]
  107. Bajracharya, M.; Sapkota, M. Profitability and productivity of potato (Solanum tuberosum) in Baglung district, Nepal. Agric. Food Secur. 2017, 6, 47. Available online: https://link.springer.com/article/10.1186/s40066-017-0125-5 (accessed on 2 October 2024). [CrossRef]
  108. Wang, X.; Zhao, M.; Liu, B.; Zou, C.; Sun, Y.; Wu, G.; Zhang, Q.; Jin, G.; Jin, Z.; Chadwick, D.; et al. Integrated systematic approach increase greenhouse tomato yield and reduce environmental losses. J. Environ. Manag. 2020, 266, 110569. [Google Scholar] [CrossRef]
  109. El-Beltagi, H.S.; Basit, A.; Mohamed, H.I.; Ali, I.; Ullah, S.; Kamel, E.A.; Ramadã, A.A.; Alkhateed, A.A.; Ghazzawy, H.S. Mulching as a sustainable water and soil saving practice in agriculture: A review. Agronomy 2022, 12, 1881. [Google Scholar] [CrossRef]
  110. Liu, L.; Li, F.R.; Zhou, L.M.; Zhang, R.H.; Jia, Y.; Lin, S.L.; Wang, L.J.; Siddique, K.H.M.; Li, F. Effect of organic manure and fertilizer on soil water and crop yields in newly-built terraces with loess soils in a semi-arid environment. Agric. Water Manag. 2013, 117, 123–132. [Google Scholar] [CrossRef]
  111. Alliaume, F.; Rossing, W.; Tittonell, P.; Jorge, G.; Dogliotti, S. Reduced tillage and cover crops improve water capture and reduce erosion of fine textured soils in raised bed tomato systems. Agric. Ecosyst. Environ. 2014, 183, 127–137. [Google Scholar] [CrossRef]
  112. Duan, Y.; Xu, M.; Wang, B.; Yang, X.; Huan, S.; Gao, S. Long-Term Evaluation of Manure Application on Maize Yield and Nitrogen Use Efficiency in China. Nutri. Manag. Soil Plant Analy. 2011, 75, 1562–1573. [Google Scholar] [CrossRef]
  113. Musyoka, M.W.; Adamtey, N.; Muriuki, A.W.; Cadisch, G. Effect of organic and conventional farming systems on nitrogen use efficiency of potato, maize and vegetables in the Central highlands of Kenya. Eur. J. Agron. 2017, 86, 24–36. [Google Scholar] [CrossRef]
  114. Drakopoulos, D.; Scholberg, J.M.; Lantinga, E.A.; Tittonell, P.A. Influence of reduced tillage and fertilization regime on crop performance and nitrogen utilization of organic potato. Organ. Agric. 2015, 6, 75–87. [Google Scholar] [CrossRef]
  115. Campiglia, E.; Mancinelli, R.; Radicetti, E. Influence of no-tillage and organic mulching on tomato (Solanum lycopersicum L.) production and nitrogen use in the mediterranean environment of central Italy. Sci. Hortic. 2011, 130, 588–598. [Google Scholar] [CrossRef]
  116. Pimentel, D. Economics and Energetics of Organic and Conventional Farming. J. Agric. Environ. Ethi. 1993, 6, 53–60. [Google Scholar] [CrossRef]
  117. Ranguwal, S.; Sidana, B.K.; Singh, J.; Sachdeva, J.; Kumar, S.; Sharma, R.K.; Dhillon, J. Quantifying the energy use efficiency and greenhouse gas emissions in Punjab (India) agriculture. Energy Nexus. 2023, 11, 100238. [Google Scholar] [CrossRef]
  118. Mohammadi, A.; Tabatabaeefar, A.; Shahin, S.; Rafiee, S.; Keyhani, A. Energy use and economical analysis of potato production in Iran a case study: Ardabil province. Energy Convers. Manag. 2008, 49, 3566–3570. [Google Scholar] [CrossRef]
  119. Liang, L.; Ridoutt, B.G.; Wu, W.; Lal, R.; Wang, L.; Wang, Y.; Li, C.; Zhao, G. A multi-indicator assessment of peri-urban agricultural production in Beijing, China. Ecol. Indic. 2019, 97, 350–362. [Google Scholar] [CrossRef]
  120. Pollesch, N.; Dale, V. Normalization in sustainability assessment: Methods and implications. Econ. Ecol. 2016, 130, 195–208. [Google Scholar] [CrossRef]
  121. Zhang, L.; Liang, T.; Wei, X.; Wang, H. An improved indicator standardization method for multi-indicator composite evaluation: A case study in the evaluation of ecological civilization construction in China. Environ. Impact Assess. Rev. 2024, 108, 107600. [Google Scholar] [CrossRef]
  122. Maisiri, N.; Senzanje, A.; Rockstrom, J.; Twomlow, S. On farm evaluation of the effect of low cost drip irrigation on water and crop productivity compared to conventional surface irrigation system. Phys. Chem. Earth 2005, 30, 783–791. [Google Scholar] [CrossRef]
  123. Pascual-Seva, N.; Bautista, A.S.; López-Galarza, S.; Maroto, J.V.; Pascual, B. Response of nutsedge (Cyperus esculentus L. var sativus Boeck.) tuber production to drip irrigation based on volumetric soil water content. Irrig. Sci. 2014, 33, 31–42. [Google Scholar] [CrossRef]
  124. Elzaki, R.M.; Elrasheed, M.; Elmulthum, N.A. Optimal crop combination under soaring oil and energy prices in the kingdom of Saudi Arabia. Socio-Econo. Plan. Sci. 2022, 83, 101367. [Google Scholar] [CrossRef]
  125. Chauhdary, J.N.; Bakhsh, A.; Engel, B.A.; Ragab, R. Improving corn production by adopting efficient fertigation practices: Experimental and modeling approach. Agric. Water Manag. 2019, 221, 449–461. [Google Scholar] [CrossRef]
  126. Akram, M.M.; Asif, M.; Rasheed, S.; Rafique, M.A. Effect of Drip and Furrow Irrigation on Yield, Water Productivity and Economics of Potato (Solanum tuberosum L.) Grown under Semiarid Conditions. Sci. Lett. 2020, 8, 48–54. [Google Scholar] [CrossRef]
  127. Abduwaiti, A.; Liu, X.; Yan, C.; Xue, Y.; Jin, T.; Wu, H.; He, P.; Bao, Z.; Liu, Q. Testing Biodegradable Films as Alternatives to Plastic-Film Mulching for Enhancing the Yield and Economic Benefits of Processed Tomato in Xinjiang Region. Sustainability 2021, 13, 3093. [Google Scholar] [CrossRef]
  128. Li, H.; Mei, X.; Nangia, V.; Guo, R.; Liu, Y.; Hao, W.; Wan, J. Effects of different nitrogen fertilizers on the yield, water- and nitrogen-use efficiencies of drip-fertigated wheat and maize in the North China Plain. Agric. Water Manag. 2021, 243, 106474. [Google Scholar] [CrossRef]
  129. Qin, J.; Ramírez, D.A.; Xie, K.; Li, W.; Yactayo, W.; Jin, L.; Quiroz, R. Is Partial Root-Zone Drying More Appropriate than Drip Irrigation to Save Water in China? A Preliminary Comparative Analysis for Potato Cultivation. Potato Res. 2018, 61, 391–406. [Google Scholar] [CrossRef]
  130. Rasool, G.; Guo, X.; Wang, Z.; Ali, M.U.; Chen, S.; Zhang, S.; Wu, Q.; Ullah, M.S. Coupling fertigation and buried straw layer improves fertilizer use efficiency, fruit yield, and quality of greenhouse tomato. Agric. Water Manag. 2020, 239, 106239. [Google Scholar] [CrossRef]
  131. Darwish, T.; Atallah, T.; Hajhasan, S. Nitrogen and water use efficiency of fertigated processing potato. Agric. Water Manag. 2006, 85, 95–104. [Google Scholar] [CrossRef]
  132. Li, H.; Mei, X.; Wang, J.; Huang, F.; Hao, W.; Li, B. Drip fertigation significantly increased crop yield, water productivity and nitrogen use efficiency with respect to traditional irrigation and fertilization practices: A meta-analysis in China. Agric Water Manag. 2021, 244, 106534. [Google Scholar] [CrossRef]
  133. Jackson, T.; Hanjra, M.; Khan, S.; Hafeez, M. Building a climate resilient farm: A risk based approach for understanding water, energy and emissions in irrigated agriculture. Agric. Syst. 2011, 104, 729–774. [Google Scholar] [CrossRef]
  134. Tabar, I.B.; Keyhani, A.; Rafiee, S. Energy balance in Iran’s agronomy (1990–2006). Renew. Sustain. Energy Rev. 2010, 14, 849–855. [Google Scholar] [CrossRef]
  135. Canakci, M.; Akinci, I. Energy use pattern analyses of greenhouse vegetable production. Energy 2006, 31, 1243–1256. [Google Scholar] [CrossRef]
  136. Rebolledo-Leiva, R.; Almeida-García, F.; Pereira-Lorenzo, S.; Ruíz-Nogueira, B.; Moreira, M.T.; González-García, S. Determining the environmental and economic implications of lupin cultivation in wheat-based organic rotation systems in Galicia, Spain. Sci. Total Environ. 2022, 845, 157342. [Google Scholar] [CrossRef] [PubMed]
  137. Dogliotti, S.; Rossing, W.; Ittersum, M. Rotat, a tool for systematically generating crop rotations. Eur. J. Agron. 2003, 19, 239–250. [Google Scholar] [CrossRef]
  138. Brosseau, A.; Saito, K.; Oort, P.A.; Diagne, M.; Valbuena, D.; Groot, J.C. Exploring opportunities for diversification of smallholders’ rice-based farming systems in the Senegal River Valley. Agric. Syst. 2021, 193, 103211. [Google Scholar] [CrossRef]
  139. Bonner, I.J.; Cafferty, K.G.; Muth, D.J.; Tomer, M.D.; James, D.E.; Porter, S.A.; Karlen, D.L. Opportunities for Energy Crop Production Based on Subfield Scale Distribution of Profitability. Energies 2014, 7, 6509–6526. [Google Scholar] [CrossRef]
  140. Munnaf, M.; Haesaert, G.; Meirvenne, S.V.; Mouazen, M. Map-based site-specific seeding of consumption potato production using high-resolution soil and crop data fusion. Comput. Electron. Agric. 2020, 178, 105752. [Google Scholar] [CrossRef]
  141. Nordey, T.; Basset-Mens, C.; Bon, C.D.; Martin, T.; Déletré, E.; Simon, S.; Parrot, L.; Despretz, H.; Huat, J.; Biard, Y.; et al. Protected cultivation of vegetable crops in sub-Saharan Africa: Limits and prospects for smallholders. A review. Agron. Sustain. Dev. 2017, 37, 53. [Google Scholar] [CrossRef]
  142. Sun, T.; Li, Z. Alfalfa-corn rotation and row placement affects yield, water use, and economic returns in Northeast China. Field Crops Res. 2019, 241, 107558. [Google Scholar] [CrossRef]
  143. Wang, S.; Xiong, J.; Yang, B.; Yang, X.; Du, T.; Steenhuis, T.S.; Siddique, K.H.; Kang, S. Diversified crop rotations reduce groundwater use and enhance system resilience. Agric. Water Manag. 2023, 276, 108067. [Google Scholar] [CrossRef]
  144. Ponsioen, T.C.; Hengsdijk, H.; Wolf, J.; van Ittersum, M.K.; Rötter, R.P.; Son, T.; Laborte, A.G. TechnoGIN, a tool for exploring and evaluating resource use efficiency of cropping systems in East and Southeast Asia. Agric. Syst. 2006, 87, 80–100. [Google Scholar] [CrossRef]
  145. Limon-Ortega, A.; Govaerts, B.; Sayre, K.D. Straw management, crop rotation, and nitrogen source effect on wheat grain yield and nitrogen use efficiency. Eur. J. Agron. 2008, 29, 21–28. [Google Scholar] [CrossRef]
  146. Shen, W.; Lin, X.; Shi, W.; Min, J.; Gao, G.; Zhang, H.; Yin, Y.; He, X. Higher rates of nitrogen fertilization decrease soil enzyme activities, microbial functional diversity and nitrification capacity in a Chinese polytunnel greenhouse vegetable land. Plant Soil 2010, 337, 137–150. Available online: https://link.springer.com/article/10.1007/s11104-010-0511-2 (accessed on 2 October 2024). [CrossRef]
  147. Alluvione, F.; Moretti, B.; Sacco, D.; Grignani, C. EUE (energy use efficiency) of cropping systems for a sustainable agriculture. Energy 2011, 36, 4468–4481. [Google Scholar] [CrossRef]
  148. Soni, P.; Sinha, R.; Perret, S.R. Energy use and efficiency in selected rice-based cropping systems of the Middle-Indo Gangetic Plains in India. Energy Rep. 2018, 4, 554–564. [Google Scholar] [CrossRef]
  149. Canali, S.; Campanelli, G.; Ciaccia, C.; Leteo, F.; Testani, E.; Montemurro, F. Conservation tillage strategy based on the roller crimper technology for weed control in Mediterranean vegetable organic cropping systems. Eur. J. Agron. 2013, 50, 11–18. [Google Scholar] [CrossRef]
  150. Nascimento, N.; Schwartz, R.; Lima, F.; Lima, F.; López-Mata, E.; Domínguez, A.; Izquiel, A.; Tarjuelo, J.; Martínez-Romero, A. Effects of irrigation uniformity on yield response and production economics of maize in a semiarid zone. Agric. Water Manag. 2019, 211, 178–189. [Google Scholar] [CrossRef]
  151. Muchara, B.; Ortmann, G.; Mudhara, M.; Wale, E. Irrigation water value for potato farmers in the Mooi River Irrigation Scheme of KwaZulu-Natal, South Africa: A residual value approach. Agric. Water Manag. 2016, 164, 243–252. [Google Scholar] [CrossRef]
  152. Patthanaissaranukool, W.; Polprasert, S.; Neamhom, T. Carbon smart agriculture: Lower carbon emissions and higher economic benefits of maize production in Thailand. Intern. J. Environ. Sci. Technol. 2022, 20, 6003–6014. Available online: https://link.springer.com/article/10.1007/s13762-022-04355-w (accessed on 2 October 2024). [CrossRef]
  153. Li, C.; Xiong, Y.; Qu, Z.; Xu, X.; Huang, Q.; Huang, G. Impact of biochar addition on soil properties and water-fertilizer productivity of tomato in semi-arid region of Inner Mongolia, China. Geoderma 2018, 331, 100–108. [Google Scholar] [CrossRef]
  154. Kanga, S.; Liang, Z.; Hu, W.; Zhang, J. Water use efficiency of controlled alternate irrigation on root-divided maize plants. Agric. Water Manag. 1998, 38, 69–76. [Google Scholar] [CrossRef]
  155. Zhang, Y.L.; Wang, F.X.; Shock, C.C.; Yang, K.J.; Kang, S.Z.; Qin, J.T.; Li, S.E. Influence of different plastic film mulches and wetted soil percentages on potato grown under drip irrigation. Agric Water Manag. 2017, 180, 160–171. [Google Scholar] [CrossRef]
  156. Guo, L.; Yu, H.; Niu, W.; Kharbach, M. Biochar Promotes Nitrogen Transformation and Tomato Yield by Regulating Nitrogen-Related Microorganisms in Tomato Cultivation Soil. Agronomy 2021, 11, 381. [Google Scholar] [CrossRef]
  157. Ladha, J.K.; Pathak, H.; Krupnik, T.J.; Six, J.; van Kessel, C. Efficiency of Fertilizer Nitrogen in Cereal Production: Retrospects and Prospects. Adv. Agron. 2005, 87, 85–156. [Google Scholar] [CrossRef]
  158. Hailu, G.; Nigussie, D.; Ali, M.; Derbew, B. Nitrogen and Phosphorus Use Efficiency in Improved Potato (Solanum tuberosum L.) Cultivars in Southern Ethiopia. Am. J. Potato Res. 2016, 94, 617–631. Available online: https://link.springer.com/article/10.1007/s12230-017-9600-6 (accessed on 2 October 2024). [CrossRef]
  159. Sánchez-Rodríguez, E.; Rubio-Wilhelmi, M.; Blasco, B.; Constán-Aguilar, C.; Romero, L.; Ruiz, J.M. Variation in the use efficiency of N under moderate water deficit in tomato plants (Solanum lycopersicum) differing in their tolerance to drought. Acta Phys. Plantar 2011, 33, 1861–1865. Available online: https://link.springer.com/article/10.1007/s11738-011-0729-5 (accessed on 2 October 2024). [CrossRef]
  160. Canakci, M.; Topakci, M.; Akinci, I.; Ozmerzi, A. Energy use pattern of some field crops and vegetable production: Case study for Antalya Region, Turkey. Energy Convers. Manag. 2005, 46, 655–666. [Google Scholar] [CrossRef]
  161. Pishgar-Komleh, S.; Ghahderijani, M.; Sefeedpari, P. Energy consumption and CO2 emissions analysis of potato production based on different farm size levels in Iran. J. Clean. Prod. 2012, 33, 183–191. [Google Scholar] [CrossRef]
  162. Heidari, M.; Omid, S. Energy use patterns and econometric models of major greenhouse vegetable productions in Iran. Energy 2011, 36, 220–225. [Google Scholar] [CrossRef]
  163. Adamtey, N.; Musyoka, M.W.; Zundel, C.; Cobo, J.G.; Karanja, E.; Fiaboe, K.K.; Muriuki, A.; Mucheru-Muna, M.; Vanlauwe, B.; Berset, E.; et al. Productivity, profitability and partial nutrient balance in maize-based conventional and organic farming systems in Kenya. Agric. Ecosyst. Environ. 2016, 235, 61–79. [Google Scholar] [CrossRef]
  164. Kanellopoulos, A.; Berentsen, P.; Ittersum, M.K.; Lansink, A.O. A method to select alternative agricultural activities for future-oriented land use studies. Eur. J. Agron. 2012, 40, 75–85. [Google Scholar] [CrossRef]
  165. Pacini, C.; Wossink, A.; Giesen, G.; Vazzana, C.; Huirne, R. Evaluation of sustainability of organic, integrated and conventional farming systems: A farm and field-scale analysis. Agric. Ecosyst. Env. 2003, 95, 273–288. [Google Scholar] [CrossRef]
  166. Wu, G.; Mingjiong, Z.; Liu, B.; Wang, X.; Yuan, M.; Wang, J.; Chen, X.; Wang, X.; Yixiang, S. Environmental impact and mitigation potentials in Greenhouse tomatoes production system in Yangtze River Delta. Plant Soil 2024, 509, 289–300. [Google Scholar] [CrossRef]
  167. Fandika, I.R.; Kadyampakeni, D.; Bottomani, C.; Kakhiwa, H. Comparative response of varied irrigated maize to organic and inorganic fertilizer application. Phys. Chem. Earth 2007, 32, 1107–1116. [Google Scholar] [CrossRef]
  168. Woldeselassie, A.; Dechassa, N.; Alemayehu, Y.; Tana, T.; Bedadi, B. Nitrogen, Phosphorus and Water Use Efficiency of Potato Under Irrigation and Fertilizer Regimes, Eastern Ethiopia. Am. J. Potato Res. 2023, 100, 413–432. Available online: https://link.springer.com/article/10.1007/s12230-023-09919-1 (accessed on 2 October 2024). [CrossRef]
  169. Liu, H.; Li, H.; Ning, H.; Zhang, X.; Li, S.; Pang, J.; Wang, G.; Sun, J. Optimizing irrigation frequency and amount to balance yield, fruit quality and water use efficiency of greenhouse tomato. Agric. Water Manag. 2019, 226, 105787. [Google Scholar] [CrossRef]
  170. Montemurro, F.; Maiorana, M.; Ferri, D.; Convertini, G. Nitrogen indicators, uptake and utilization efficiency in a maize and barley rotation cropped at different levels and sources of N fertilization. Field Crops Res. 2006, 99, 114–124. [Google Scholar] [CrossRef]
  171. Swain, E.Y.; Rempelos, L.; Orr, C.H.; Hall, G.; Chapman, R.; Almadni, M.; Stockdale, E.A.; Kidd, J.; Leifert, C.; Cooper, J.M. Optimizing nitrogen use efficiency in wheat and potatoes: Interactions between genotypes and agronomic practices. Euphytica 2014, 199, 119–136. [Google Scholar] [CrossRef]
  172. Schmidt, S.; Krishnan, V.; Gamage, H.; Walsh, M.; Huelsen, T.; Wolf, J.; Wadewitz, P.; Jensen, P.; Das, B.; Robinson, N. Enabling the circular nitrogen economy with organic and organo-mineral fertilisers. Nutr. Cycl. Agroecos. 2024, 130, 33–48. [Google Scholar] [CrossRef]
  173. Deike, S.; Pallutt, B.; Christen, O. Investigations on the energy efficiency of organic and integrated farming with specific emphasis on pesticide use intensity. Eur. J. of Agron. 2008, 28, 461–470. [Google Scholar] [CrossRef]
  174. Zangeneh, Z.; Omid, M.; Akram, A. A comparative study on energy use and cost analysis of potato production under different farming technologies in Hamadan province of Iran. Energy 2010, 35, 2927–2933. [Google Scholar] [CrossRef]
  175. Ferreira de Souza, J.F.; Fernandes, B.D.; Rotta, I.; Visacri, M.B.; Lima, T.M. Key performance indicators for pharmaceutical services: A systematic review. Expl. Res. Clinical. Soci. Pharm. 2024, 14, 100441. [Google Scholar] [CrossRef]
  176. Eliseu, E.; Lima, T.M.; Gaspar, P.D. Sustainable Development Strategies and Good Agricultural Practices for Enhancing Agricultural Productivity: Insights and Applicability in Developing Contexts—The Case of Angola. Sustainability 2024, 16, 9878. [Google Scholar] [CrossRef]
  177. Culas, R.J.; Anwar, M.R.; Maraseni, T.N. A framework for evaluating benefits of organic fertilizer use in agriculture. J. Agric. Food Res. 2025, 19, 101576. [Google Scholar] [CrossRef]
  178. Alzamel, N.; Taha, E.M.; Bakr, A.A.; Loutfy, N. Effect of Organic and Inorganic Fertilizers on Soil Properties, Growth Yield, and Physiochemical Properties of Sunflower Seeds and Oils. Sustainability 2022, 14, 12928. [Google Scholar] [CrossRef]
  179. Durham, T.C.; Mizik, T. Comparative Economics of Conventional, Organic, and Alternative Agricultural Production Systems. Economies 2021, 9, 64. [Google Scholar] [CrossRef]
  180. Zhao, J.; Yang, Y.; Zhang, K.; Jeong, J.; Zeng, Z.; Zang, H. Does crop rotation yield more in China? A meta-analysis. Field Crops Res. 2020, 245, 107659. [Google Scholar] [CrossRef]
  181. Liu, Y.; Li, Z.; Li, Y.; Liu, Z.; Chen, F.; Bi, Z.; Sun, C.; Tang, C.; Yao, P.; Yuan, A.; et al. Impact of extended dryland crop rotation on sustained potato cultivation in Northwestern China. Resou. Conserv. Recy. 2023, 197, 107114. [Google Scholar] [CrossRef]
  182. Hernández, T.; Chocano, C.; Moreno, J.L.; García, C. Towards a more sustainable fertilization: Combined use of compost and inorganic fertilization for tomato cultivation. Agric. Ecosyst. Environ. 2014, 196, 178–184. [Google Scholar] [CrossRef]
  183. Megali, L.; Glauser, G.; Rasmann, S. Fertilization with beneficial microorganisms decreases tomato defenses against insect pests. Agron. Sustain. Dev. 2013, 34, 649–656. Available online: https://link.springer.com/article/10.1007/s13593-013-0187-0 (accessed on 2 October 2024). [CrossRef]
  184. Victor, S.; Farooq, A. Dashboard visualisation for healthcare performance management: Balanced scorecard metrics. Asi. Paci. J. Health Manag. 2021, 16, 1833–3818. [Google Scholar] [CrossRef]
  185. Wongnaa, C.A.; Akuriba, M.A.; Ebenezer, A.; Danquah, K.S.; Ofosu, D.A. Profitability and constraints to urban exotic vegetable production systems in the Kumasi metropolis of Ghana: A recipe for job creation. J. Global Entrep. Res. 2019, 9, 33. [Google Scholar] [CrossRef]
  186. Seciu, A.M.; Oancea, A.; Gaspar, A.; Moldovan, L.; Craciunescu, O.; Stefan, L.; Petrus, V.; Georgescu, F. Water Use Efficiency on Cabbage and Cauliflower Treated with a New Biostimulant Composition. Agri. Agricult. Sci. Proced. 2016, 10, 475–484. [Google Scholar] [CrossRef]
  187. Gezer, I.; Acaro, M.; Haciseferoǧullari, H.H. Use of energy and labour in apricot agriculture in Turkey. Biom. Bioen. 2003, 24, 215–219. [Google Scholar] [CrossRef]
  188. Gamage, A.; Gangahagedara, R.; Gamage, J.; Nepalês, J.; Kodikara, N.; Suraweera, P.; Merah, O. Role of organic farming for achieving sustainability in agriculture. Farm Syst. 2023, 1, 100005. [Google Scholar] [CrossRef]
  189. Rajput, T.; Patel, N. Water and nitrate movement in drip-irrigated onion under fertigation and irrigation treatments. Agric. Water Manag. 2016, 73, 293–311. [Google Scholar] [CrossRef]
  190. Liu, X.; Lehtonen, H.; Purola, T.; Pavlova, Y.; Rötter, R.; Palosuo, T. Dynamic economic modelling of crop rotations with farm management practices under future pest pressure. Agric. Syst. 2016, 144, 65–76. [Google Scholar] [CrossRef]
  191. Chemura, A. The growth response of coffee (Coffea arabica L.) plants to organic manure, inorganic fertilizers and integrated soil fertility management under different irrigation water supply levels. Inter. J. Recy. Orga. Waste Agric. 2014, 3, 59. [Google Scholar] [CrossRef]
  192. Nordey, T.; Ochieng, J.; Ernest, Z.; Mlowe, N.; Mosha, I.; Fernandes, P. Is vegetable cultivation under low tunnels a profitable alternative to pesticide use? The case of cabbage cultivation in northern Tanzania. Crop Prot. 2020, 134, 105169. [Google Scholar] [CrossRef]
  193. Malobane, M.E.; Nciizah, A.D.; Mudau, F.N.; Wakindiki, I.I. Tillage, Crop Rotation and Crop Residue Management Effects on Nutrient Availability in a Sweet Sorghum-Based Cropping System in Marginal Soils of South Africa. Agronomy 2020, 10, 776. [Google Scholar] [CrossRef]
Figure 1. PRISMA flowchart illustrating the identification and screening process.
Figure 1. PRISMA flowchart illustrating the identification and screening process.
Sustainability 17 07019 g001
Figure 2. Result of the normalization of the economic, environmental, and technological KPIs for the GAP of drip irrigation, crop rotation, inorganic fertilizers, and organic fertilizers in maize cultivation (the largest triangle represents the GAP for maize cultivation using organic fertilizer).
Figure 2. Result of the normalization of the economic, environmental, and technological KPIs for the GAP of drip irrigation, crop rotation, inorganic fertilizers, and organic fertilizers in maize cultivation (the largest triangle represents the GAP for maize cultivation using organic fertilizer).
Sustainability 17 07019 g002
Figure 3. Result of the normalization of the economic, environmental, and technological KPIs for the GAP of drip irrigation, crop rotation, inorganic fertilizers, and organic fertilizers in potato cultivation (the largest triangle represents the GAP in potato cultivation with drip irrigation).
Figure 3. Result of the normalization of the economic, environmental, and technological KPIs for the GAP of drip irrigation, crop rotation, inorganic fertilizers, and organic fertilizers in potato cultivation (the largest triangle represents the GAP in potato cultivation with drip irrigation).
Sustainability 17 07019 g003
Figure 4. Results of the normalization of the economic, environmental and technological KPIs for the GAPs of drip irrigation, crop rotation, inorganic fertilizers and organic fertilizers in tomato cultivation (the largest triangle represents the GAP for growing tomatoes using crop rotation).
Figure 4. Results of the normalization of the economic, environmental and technological KPIs for the GAPs of drip irrigation, crop rotation, inorganic fertilizers and organic fertilizers in tomato cultivation (the largest triangle represents the GAP for growing tomatoes using crop rotation).
Sustainability 17 07019 g004
Table 1. Inclusion and exclusion criteria.
Table 1. Inclusion and exclusion criteria.
InclusionExclusion
The document discussed and focused on GAPs;The document does not discuss and focus on GAPs;
Studies performed in the agricultural sector;Studies have not been performed in the agricultural sector;
Studies related to environmental, technological, economic, and social KPIs in the agricultural sector;Studies related to environmental, technological, economic, and social KPIs in other sectors;
Studies performed in developed and underdeveloped countries;Studies performed in developed and underdeveloped countries are not available;
Publication date between 2019 and 2024;Published outside the defined range;
Studies and scenarios of GAPs and KPIs in the agricultural sector are written in English.Studies and scenarios of GAPs and KPIs in the agricultural sector are written in a language other than English.
Table 2. Main agricultural KPIs according to the respective articles.
Table 2. Main agricultural KPIs according to the respective articles.
KPIs1234567891011121314
EconomicFinancial strength
Farmer’s income
Gross margin
Net profit
Profit for the producer caused by irrigation
EnvironmentalSoil conservation
Primary production for internal inputs
Energy use efficiency
Water use efficiency
Nitrogen use efficiency
SocialPercentage of irrigated agricultural areas
Land tenure
TechnologyAgricultural machinery
Machine energy
Table 8. Score of the KPIs applied in the agricultural sector according to the S and M of Table 4, Table 5, Table 6 and Table 7.
Table 8. Score of the KPIs applied in the agricultural sector according to the S and M of Table 4, Table 5, Table 6 and Table 7.
KPIsDrip IrrigationCrop RotationInorganic FertilizersOrganic Fertilizers
SMTSMTSMTSMT
Gross margin0.50.510.50.510.50.510.50.51
Net profit0.50.510.50.510.50.510.50.51
Water use efficiency0.50.510.50.510.50.510.50.51
Nitrogen use efficiency0.50.510.50.510.50.510.50.51
Machine energy0.50.510.50.510.50.510.50.51
Table 9. Maximum (max(xi)), minimum (min(xi)) and test values of the KPI metrics in drip irrigation practice.
Table 9. Maximum (max(xi)), minimum (min(xi)) and test values of the KPI metrics in drip irrigation practice.
KPIsMaizePotatoTomato
m i n ( x i ) m a x ( x i ) m i n ( x i ) m a x ( x i ) m i n ( x i ) m a x ( x i )
Gross   margin   ( E U R /ha)638.51151.71396.192605.902609.197104.78
Value to test the metric1056.68 [122]2368 [123]3287.4 [124]
Net   profit   ( E U R /ha)279.649441706.652933.911652.276352.18
Value to test metric983.77 [125]2765.85 [126]2145.45 [127]
Water use efficiency (%)23.70494046.17.821.4
Value to test metric32.5 [128]45.9 [129]18 [130]
Nitrogen use efficiency (%)66.29048.6081.675073
Value to test metric68.5 [128]61 [131]68 [132].
Machine energy (MJ/ha)15,34021,14664.86880728.711,791.7
Value to test metric16,506 [133]1290 [134]1300.6 [135]
Table 10. Maximum (max(xi)), minimum (min(xi)) and test values of the KPI metrics in crop rotation practice.
Table 10. Maximum (max(xi)), minimum (min(xi)) and test values of the KPI metrics in crop rotation practice.
KPIMaizePotatoTomato
m i n ( x i ) m a x ( x i ) m i n ( x i ) m a x ( x i ) m i n ( x i ) m a x ( x i )
Gross   margin   ( E U R /ha)10006000100050565003000
Value to test the metric2860 [136]5020 [137]2699.35 [138]
Net   profit   ( E U R /ha)112.63379.77208100484.407185.2
Value to test metric260.40 [139]792.36 [140]4571.28 [141]
Water use efficiency (%)81812.535.315.523.4
Value to test metric12 [142]27 [143]20 [144]
Nitrogen use efficiency (%)4685335312.0561
Value to test metric60 [145]34.3 [132]29.3 [146]
Machine energy (MJ/ha)1000800011202780826.701067.56
Value to test metric6600 [147]2130.01 [148]871 [149]
Table 11. Maximum (max(xi)), minimum (min(xi)) and test values of the KPI metrics in the inorganic fertilizer practice.
Table 11. Maximum (max(xi)), minimum (min(xi)) and test values of the KPI metrics in the inorganic fertilizer practice.
KPIMaizePotatoesTomatoes
m i n ( x ) m a x ( x ) m i n ( x ) m a x ( x ) m i n ( x ) m a x ( x )
Gross   margin   ( E U R /ha)706118741215371419.268332.87
Value to test the metric831 [150]1026.80 [151]2699.35 [138]
Net   profit   ( E U R /ha)1220.561853.01621.382414.262218.383904.15
Value to test metric1411 [152]1240.02 [140]2914.19 [153]
Water use efficiency (%)60805.947.8022.6362
Value to test metric65 [154]25 [155]45.33 [156]
Nitrogen use efficiency (%)426848.6081.671524
Value to test metric60 [157]68.25 [158]22 [159]
Machine energy (MJ/ha)589.381739.5866950142.69530.3
Value to test metric1245.6 [160]910 [161]440.66 [162]
Table 12. Maximum (max(xi)), minimum (min(xi)) and test value of the KPI metrics in organic fertilizer practice.
Table 12. Maximum (max(xi)), minimum (min(xi)) and test value of the KPI metrics in organic fertilizer practice.
KPIsMaizePotatoTomato
m i n ( x i ) m a x ( x i ) m i n ( x i ) m a x ( x i ) m i n ( x i ) m a x ( x i )
Gross   margin   ( E U R /ha)24.97139.2128506288355.573640.28
Value to test the metric125 [163]3945 [164]429 [165]
Net   profit   ( E U R /ha)812.813500.97830.27211.966339.299248.59
Value to test metric990.85 [125]1181.0 [140]8901.47 [166]
Water use efficiency (%)9.958.431772540
Value to test metric40 [167]65 [168]29.1 [169]
Nitrogen use efficiency (%)20453344.11069
Value to test metric40.9 [170]39.5 [171]22.6 [172]
Machine energy (MJ/ha)237.606217.41582.613206.488351766
Value to test metric1870 [173]3182.65 [174]871 [149]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Eliseu, E.E.; Lima, T.M.; Gaspar, P.D. A KPI-Based Framework for Evaluating Sustainable Agricultural Practices in Southern Angola. Sustainability 2025, 17, 7019. https://doi.org/10.3390/su17157019

AMA Style

Eliseu EE, Lima TM, Gaspar PD. A KPI-Based Framework for Evaluating Sustainable Agricultural Practices in Southern Angola. Sustainability. 2025; 17(15):7019. https://doi.org/10.3390/su17157019

Chicago/Turabian Style

Eliseu, Eduardo E., Tânia M. Lima, and Pedro D. Gaspar. 2025. "A KPI-Based Framework for Evaluating Sustainable Agricultural Practices in Southern Angola" Sustainability 17, no. 15: 7019. https://doi.org/10.3390/su17157019

APA Style

Eliseu, E. E., Lima, T. M., & Gaspar, P. D. (2025). A KPI-Based Framework for Evaluating Sustainable Agricultural Practices in Southern Angola. Sustainability, 17(15), 7019. https://doi.org/10.3390/su17157019

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop