Search Results (954)

Improving the water productivity and sustainability of global food supplies and reducing water stress worldwide requires a comprehensive and consistent assessment of water use in global food production systems, including livestock production and supply chains. Presented here is a systematic review of relevant livestock water use studies, published over two periods: “Period 1993–2017” and “Period 2018–2024”, assessing consistency in their approaches and identifying opportunities for advancing and harmonizing the assessment of livestock water use worldwide. However, the review highlights that a comprehensive and consistent assessment of livestock water use remains a challenge. The reviewed studies (a total of 317) differ in terms of their accounting of different water flows, setting the system boundaries, and quantification of water productivity and impact metrics. This makes it difficult to compare potential water productivity and environmental impacts of livestock production systems at different scales and locations. Case studies are required to further develop and implement a robust and consistent methodological approach, based on locally calibrated models and databases, of different livestock production systems in different agroclimatic conditions. Also, further communication and training are required to help build the capability to apply a comprehensive and consistent assessment of livestock water use locally and globally. The adoption of a scientifically robust and practically applicable methodological framework will support researchers, policy managers, farmers, and business leaders in sound decision-making to improve the productivity and sustainability of water use in livestock production systems locally and globally. Full article

Pomegranate (Punica granatum) fruit size estimation plays a crucial role in orchard management decision-making, especially for fruit quality assessment and yield prediction. Currently, fruit sizing for pomegranates is performed manually using calipers to measure equatorial and polar diameters. These methods rely on human judgment for sample selection, they are labor-intensive, and prone to errors. In this work, a novel framework for automated on-tree detection and sizing of pomegranate fruits by a farmer robot equipped with a consumer-grade RGB-D sensing device is presented. The proposed system features a multi-stage transfer learning approach to segment fruits in RGB images. Segmentation results from each image are projected on the co-located depth image; then, a fruit clustering and modeling algorithm using visual and depth information is implemented for fruit size estimation. Field tests carried out in a commercial orchard are presented for 96 pomegranate fruit samples, showing that the proposed approach allows for accurate fruit size estimation with an average discrepancy with respect to caliper measures of about 1.0 cm on both the polar and equatorial diameter. Full article

Soil moisture content is an important determinant of crop productivity, especially in agricultural systems that are dependent on rainfall. Climate variability has introduced water management challenges for smallholder farmers in Southern Africa. The emergence of unmanned aerial vehicle (UAV)-borne remote sensing offers modern solutions for monitoring soil moisture, plant health and overall crop productivity in real-time. This study evaluated the utility of UAV-acquired data in conjunction with random forest regression in predicting soil moisture content and chlorophyll across different growth stages of taro. The estimation models achieved R² values up to 0.90 with rRMSE as low as 1.25%, demonstrating the robust performance of random forest in concert with different spectral datasets in estimating soil moisture and chlorophyll. Correlation analysis confirmed the association between these two variables, with the strongest correlation observed during the vegetative stage (r = 0.81, p < 0.05) and the weakest during the late vegetative stage (r = 0.78, p < 0.05). The results showed that UAV bands were crucial in predicting soil moisture and chlorophyll across all stages. These results demonstrate the utility of remote sensing, particularly UAV-borne sensors, in monitoring crop productivity in smallholder farms. By employing UAV-borne sensors, farmers can improve on-farm water management and make better and more informed decisions. Full article

Advancements in unmanned aerial vehicle (UAV) multispectral systems offer robust technical support for the precise and efficient estimation of japonica rice yield in cold regions within the framework of precision agriculture. These innovations also present a viable alternative to conventional yield estimation methods. However, recent research suggests that reliance solely on vegetation indices (VIs) may result in inaccurate yield estimations due to variations in crop cultivars, growth stages, and environmental conditions. This study investigated six fertilization gradient experiments involving two conventional japonica rice varieties (KY131, SJ22) and two hybrid japonica rice varieties (CY31, TLY619) at Yanjiagang Farm in Heilongjiang Province during 2023. By integrating UAV multispectral data with machine learning techniques, this research aimed to derive critical phenotypic parameters of rice and estimate yield. This study was conducted in two phases: In the first phase, models for assessing phenotypic traits such as leaf area index (LAI), canopy cover (CC), plant height (PH), and above-ground biomass (AGB) were developed using remote sensing spectral indices and machine learning algorithms, including Random Forest (RF), XGBoost, Support Vector Regression (SVR), and Backpropagation Neural Network (BPNN). In the second phase, plot yields for hybrid rice and conventional rice were predicted using key phenotypic parameters at critical growth stages through linear (Multiple Linear Regression, MLR) and nonlinear regression models (RF). The findings revealed that (1) Phenotypic traits at critical growth stages exhibited a strong correlation with rice yield, with correlation coefficients for LAI and CC exceeding 0.85 and (2) the accuracy of phenotypic trait evaluation using multispectral data was high, demonstrating practical applicability in production settings. Remarkably, the R² for CC based on the RF algorithm exceeded 0.9, while R² values for PH and AGB using the RF algorithm and for LAI using the XGBoost algorithm all surpassed 0.8. (3) Yield estimation performance was optimal at the heading (HD) stage, with the RF model achieving superior accuracy (R² = 0.86, RMSE = 0.59 t/ha) compared to other growth stages. These results underscore the immense potential of combining UAV multispectral data with machine learning techniques to enhance the accuracy of yield estimation for cold-region japonica rice. This innovative approach significantly supports optimized decision-making for farmers in precision agriculture and holds substantial practical value for rice yield estimation and the sustainable advancement of rice production. Full article

The paper presents AgriMicro, a modern Farm Management Information System (FMIS) designed to help farmers monitor and optimize corn crops from sowing to harvest, by leveraging cloud technologies and machine learning algorithms. The platform is built on a modular architecture composed of multiple components implemented through microservices such as the weather and soil service, recommendation and alert engine, field service, and crop service—which continuously communicate to centralize field data and provide real-time insights. Through the ongoing exchange of data between these services, different information pieces about soil conditions, crop health, and agricultural operations are processed and analyzed, resulting in predictions of crop evolution and practical recommendations for future interventions (e.g., fertilization or irrigation). This integrated FMIS transforms collected data into concrete actions, supporting farmers and agricultural consultants in making informed decisions, improving field productivity, and ensuring more efficient resource use. Its microservice-based architecture provides scalability, modularity, and straightforward integration with other information systems. The objectives of this study are threefold. First, to specify and design a modular FMIS architecture based on microservices and cloud computing, ensuring scalability, interoperability and adaptability to different farm contexts. Second, to prototype and integrate initial components and Internet of Things (IoT)-based data collection with machine learning models, specifically Random Forest and XGBoost, to provide maize yield forecasting as a proof of concept. Model performance was evaluated using standard predictive accuracy metrics, including the coefficient of determination (R²) and the root mean square error (RMSE), confirming the reliability of the forecasting pipeline and validated against official harvest data (average maize yield) from the Romanian National Institute of Statistics (INS) for 2024. These results confirm the reliability of the forecasting pipeline under controlled conditions; however, in real-world practice, broader regional and inter-annual variability typically results in considerably higher errors, often on the order of 10–20%. Third, to present a Romania based case study which illustrates the end-to-end workflow and outlines an implementation roadmap toward full deployment. As this is a design-oriented study currently under development, several services remain at the planning or early prototyping stage, and comprehensive system level benchmarks are deferred to future work. Full article

Australian agriculture managers deal with climates that are characterised by high variability and unpredictability. A simple framework for decision-making is used to structure weather-related inquiries using recent and long-term climate data to better inform decisions based on current conditions and future expectations. This paper describes the rationale, design philosophy, and development journey of Australian CliMate (CliMate), a contemporary climate analysis tool built to consolidate and modernise the functionality of earlier computer-based decision support tools (DSTs). CliMate aimed to be simple, transparent, and user-driven, supporting tactical and strategic agricultural decisions. Ten core analyses were included from previous DSTs. With over 20,000 registered users and widespread adoption among farmers, consultants, and other professionals over a decade, CliMate demonstrates the enduring demand for accessible, mobile climate analysis tools. We reflect on lessons learned in the development process, advocating for minimalism, iteration with users, and integration of transparent data sources. This experience underscores the necessity for long-term support and evaluation to sustain the value of agricultural DSTs. Full article

Coffee demand continues to rise, while producing countries face increasing challenges and yield losses due to climate change. In response, farmers are adopting agricultural practices capable of boosting productivity. However, these practices increase intercrop variability, making coffee mapping more challenging. In this study, a novel approach is proposed to identify coffee cultivation considering four phenological stages: planting (PL), producing (PR), skeleton pruning (SK), and renovation with stumping (ST). A hierarchical classification framework was designed to isolate coffee pixels and identify their respective stages in one of Brazil’s most important coffee-producing regions. A dense time series of multispectral bands, spectral indices, and texture metrics derived from Harmonized Landsat Sentinel-2 (HLS) imagery, with an average revisit time of ~3 days, was employed. This data was combined with an ensemble learning approach based on decision-tree algorithms, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The results achieved unprecedented sensitivity and specificity for coffee plantation detection with RF, consistently exceeding 95%. The classification of coffee phenological stages showed balanced accuracies of 77% (ST) and from 93% to 95% for the other classes. These findings are promising and provide a scalable framework to monitor climate-resilient coffee management practices. Full article

This study examines the role of farm forestry Farmer Field Schools (FFSs) in strengthening climate resilience in mixed crop–livestock systems in dryland Kenya. Based on interviews and focus group discussions in Embu and Taita Taveta, this study finds that FFS participation enhanced tree cultivation, market monitoring, and group-based learning, with greater involvement of women in decision-making. While FFS households showed stronger motivation for continued learning and experimentation, it has not consistently translated into statistically significant improvements in climate resilience outcomes as measured by recent drought and disturbance impacts. Limited water access emerged as a major barrier. The findings suggest that while FFSs foster adaptive learning and farm-level innovation, their contribution to climate resilience requires integration with cross-sectoral strategies, especially water management and institutional support. Full article

The development of technology has influenced changes in agricultural production. Farmers are increasingly using modern devices and machinery that provide valuable information, and to manage this information effectively, it is necessary to use specialized applications. This research aims to evaluate various applications and determine which one is most suitable for small- and medium-sized farmers to adopt in precision agriculture. This research employed expert decision-making to determine the importance of criteria and evaluate applications using linguistic values. Due to the presence of uncertainty in decision-making, an interval type-2 fuzzy (IT2F) set was used, which addresses this problem through the support of a membership function. This approach allows for the display of uncertainty and imprecision using an interval rather than a single exact value. This enables a more flexible and realistic representation of ratings, leading to more confident decision-making. These membership functions are formed in such a way that there is symmetry around the central linguistic value. To use this approach, the SiWeC (simple weight calculation) and CORASO (compromise ranking from alternative solutions) methods were adapted. The results of the IT2F SiWeC method revealed that the most important criteria for experts are data accuracy, efficiency, and simplicity. The results of the IT2F CORASO method displayed that the A3 application delivers the best results, confirmed by additional analyses. This research has indicated that digital tools, in the form of applications, can be effectively used in small- and medium-scale precision agriculture production. Full article

Integrated Pest Management (IPM) is a system that combines ready-made plant protection methods. IPM guidelines apply to all users of plant protection products and require the prioritization of preventative methods. Adherence to IPM principles contributes to the production of healthy and safe food. In Poland, the implementation of IPM into agricultural practice remains a solution to the problem. Furthermore, it is necessary to ensure education and implementation of IPM at the basic or implementation level. The IPM element, particularly emphasized in the 2009/128/EC Directive, is the use of so-called warning systems, tools that address the issue of plant protection application. In this regard, it is necessary to use decision support systems (DSSs). DSSs are digital solutions that integrate meteorological, global, and field data. They include the risk of disease and pest occurrence and the timing of the application. DSSs are not part of the farmer’s experience or presentation but support them in making sound decisions. DSS reduces costs, the side effects of plant protection, and energy consumption. Examples of such solutions in Poland include the eDWIN platform and OPWS, classified, among others, in cereal protection against fungi. The aim of this article is to present the role, capabilities, and limitations of decision support systems in modern agricultural production and their importance in the context of the Green Deal and digital agriculture. Full article

Water scarcity and soil salinization pose significant challenges to sustainable agricultural development in arid and semi-arid regions globally. This study applies Positive Mathematical Programming (PMP) to analyze agricultural water resource management in the Hetao Irrigation District (HID), China. The research constructs a comprehensive multi-stress-factor integrated PMP model to evaluate the compound impacts of water resource constraints, pricing policies, and environmental stress on agricultural production systems. The model incorporates crop-specific salinity tolerance thresholds and simulates farmer decision-making behaviors under various scenarios including water supply reduction (0–100%), water pricing increases (0.2–1.0 CNY/m³), and soil salinity stress (0–10 dS/m). The results reveal that the agricultural system exhibits significant vulnerability characteristics with critical thresholds concentrated in the 60–70% water resource utilization interval. Water pricing policies show limited effectiveness in low-price ranges, with wheat demonstrating the highest price sensitivity (−23.8% elasticity). Crop salinity tolerance analysis indicates that wheat–sunflower rotation systems maintain an 85% planting proportion even under extreme salinity conditions (10 dS/m), significantly outperforming individual crops. The study proposes a hierarchical water resource quota allocation system based on vulnerability thresholds and recommends promoting salt-tolerant rotation systems to enhance agricultural resilience. These findings provide scientific evidence for sustainable water resource management and agricultural adaptation strategies in water-stressed regions, contributing to both theoretical advancement of the PMP methodology and practical policy formulation for irrigation districts facing similar challenges. Full article

To integrate rapidly growing environmental, social, and governance (ESG) investments into agribusiness, it is essential to understand the decision-making mechanisms behind sustainable practices in small- and medium-sized farms. This study examines the role of corporate governance in promoting sustainable practices using structural equation modeling on survey data from 1111 Japanese farms. The results reveal that internal social sustainability practices, such as improving the work environment and employee well-being, are positively associated with corporate governance and, in turn, significantly enhance sales growth, cash flow, and succession prospects. In contrast, external social sustainability practices show a negative correlation with governance, reflecting the influence of socioemotional wealth and reputation-driven decision-making. Environmental sustainability practices correlate only with sustainable corporate governance, suggesting a lack of strategic integration. These findings underscore the importance of corporate governance as a factor in linking sustainable initiatives to economic outcome. Strengthening internal social sustainability through robust corporate governance is therefore critical for farmers aiming to improve performance through sustainability. Moreover, given that family management preferences shape sustainability choices, policymakers must consider both governance and socioemotional factors to effectively support agricultural sustainability. Full article

The “three rights separation” system plays a vital role in enhancing the economic efficiency of rural residential land use, thereby contributing to land revitalization and rural-urban integration. Using survey data from 456 farmers in Yujiang District and Deqing County, this study employs DEA, Tobit, and threshold regression models to analyze the system’s effects. The results show that the system improves economic efficiency by approximately 8.9%, primarily by incentivizing investment and promoting land transfers. A nonlinear threshold effect exists: investment incentives become significant only when idle land exceeds 35 m², consistent with farmers’ economic decision-making. Land transfers enhance efficiency via marginal return equalization, however, economies of scale are not evident, being constrained by legal and coordination factors. The findings highlight the importance of deepening reform implementation, enhancing farmers’ understanding of property rights, adopting differentiated incentives tailored to land size and farmer capacity, and regulating the land transfer market to ensure transparency and fairness. Furthermore, promoting collective or service-based management models can help overcome natural scale limitations, thereby unlocking the system’s full institutional dividends. Full article

Synergizing soil quality improvement and greening for increased yields are essential to ensuring grain security and developing sustainable agriculture, which has become a key issue in agricultural cultivation. This study considers a contract farming supply chain composed of a risk-averse farmer and a risk-neutral firm making green and efficient technology (GET) investments, which refers to the use of technology monitoring to achieve fertilizer reduction and yield increases with yield uncertainty. Based on the CvaR (Conditional value at Risk) criterion, the Stackelberg game method is applied to construct a two-level supply chain model and analyze different cooperation mechanisms. The results show that when the wholesale price is moderate, both sides will choose the cooperative mechanism of cost sharing to invest in technology; the uncertainty of yield and the degree of risk aversion have a negative impact on the agricultural inputs and GET investment, and when yield fluctuates greatly, the farmer invests in GET to make higher utility but lowers profits for the firm and supply chain. This study provides a theoretical basis for GET investment decisions in agricultural supply chains under yield uncertainty and has important practical value for promoting sustainable agricultural development and optimizing supply chain cooperation mechanisms. Full article

China holds the largest apple cultivation area globally, yet yields per hectare remain relatively low. Despite substantial government investment in modern orchard technologies, adoption remains limited among farmers. This study investigates the economic and sociological drivers of technology uptake, focusing on how information sources shape adoption behavior. Based on 382 farmer surveys across major apple-producing provinces, the study examines (1) farmers’ preferences for agricultural information sources, (2) the influence of demographic characteristics on those preferences, and (3) the differential effects of specific sources on the adoption of key technologies, including dwarf rootstocks and virus-free seedlings. Results show that agri-chemical dealers (ACDs) and farmer peers (FPs) are the most commonly used information channels. Access to advice from local experts (EXPs) significantly increases the likelihood of adopting dwarf rootstocks, while information from ACDs promotes the use of virus-free seedlings. In contrast, reliance on personal farming experience is negatively associated with technology uptake. These findings highlight the need to strengthen formal information dissemination systems and better integrate trusted local actors like ACDs and EXPs into agricultural extension. Targeted information delivery can improve adoption efficiency, promote evidence-based decision-making, and support the modernization and sustainability of China’s apple sector. Full article