Search Results (915)

Modern agriculture in developing regions faces significant challenges due to labor scarcity and the health hazards associated with the manual application of chemical treatments. This study presents the design, development, and techno-economic evaluation of an experimental hexacopter unmanned ariel vehicle (UAV) platform specifically tailored for crop protection on fragmented, smallholder farmlands. The research aims to bridge the gap between expensive imported technology and the practical needs of small-scale farmers by providing a cost-effective, locally manufacturable solution. The methodology involved the integration of a modular spraying system and optimized control architecture into a high-stability hexacopter frame. Experimental evaluations focused on flight stability, payload capacity, and spray uniformity using water-sensitive media. The results indicate that the developed platform achieves high coverage efficiency while significantly reducing chemical waste compared to traditional manual methods. Furthermore, the economic analysis suggests that the operational costs are substantially lower than those of comparable imported systems, offering a favorable payback period within a few crop seasons. These findings demonstrate that an indigenous UAV spraying platform can enhance both operational safety and economic feasibility for smallholder agriculture. Full article

Cassava is a critical staple crop for food security and rural livelihoods in Sub-Saharan Africa, yet high-resolution maps of its distribution remain scarce, particularly for smallholder systems. In this study, we generated a 10 m resolution cassava presence map for Uganda (CM24) by fine-tuning a Random Forest classifier on TESSERA foundation model embeddings derived from Sentinel-1 and Sentinel-2 time series. Using field survey data from the Copernicus4GEOGLAM campaign for training and validation, the model achieved excellent discriminative ability (validation AUC = 0.9532, test AUC = 0.9524). Visual validation against high-resolution satellite imagery confirmed good spatial agreement, capturing both large contiguous fields and small fragmented plots. Comparison with two existing global products (CassavaMap and SPAM2020) and two seasons of national survey data conducted by the Uganda Bureau of Statistics showed that CM24 produced national harvested area estimates that fell between the two survey totals, whereas CassavaMap and SPAM2020 systematically overestimated harvested area by factors of two to three. Our results demonstrate that foundation-model embeddings offer a robust and scalable approach for mapping cassava in heterogeneous smallholder landscapes. The resulting CM24 map provides a spatially explicit tool to support disease surveillance, agricultural monitoring, and food security planning in Uganda and beyond. Full article

Agricultural reconversion has become a key strategy for the continuity and competitiveness of primary-sector productive units, confronting scenarios such as low profitability, environmental pressures, and changes in agri-food markets. The objective of this study was to identify the economic, social, and environmental factors that influenced the agricultural reconversion to lemon cultivation (Citrus limon) in production units in the municipality of Calvillo, Aguascalientes, Mexico. During 2023 and 2024, a total of 32 semi-structured interviews were conducted with producers who had undertaken restructuring processes, from which perception variables were constructed across three dimensions: economic, social, and crop management. Through cluster analysis, three distinct profiles were identified based on their perceptions, resource use, and organizational characteristics (p < 0.05). Results indicate that economic factors are the main driver of productive change (low crop profitability p = 0.0035); however, environmental dimension and efficient resource management, particularly water, are crucial to consolidating the conversion. Furthermore, government support, production experience, and membership in organizations influence perceptions of viability and the prospects of the new crop. The findings suggest that agricultural production restructuring does not follow a uniform pattern, but is shaped by structural, perceptual, and contextual factors, highlighting the importance of targeted public policies and integrated strategies for technical and organizational support. Full article

Hungary is experiencing more frequent and severe droughts due to climate change, with 60% of its arable land in the vulnerable Great Hungarian Plain. Drought events in 2012 and 2022 reduced maize yields by more than 50% in some regions. This review synthesizes studies (2000–2025) on remote sensing capabilities, climate change impacts, and farmer adaptation in Hungarian agriculture. Remote sensing technologies (Sentinel, Landsat, MODIS) and indices (NDVI, VCI, LST, TCI) achieve high accuracy (often >80%) in drought detection under validated conditions, yet technical and financial barriers limit uptake among smallholder farmers. Climate projections indicate that a 2 °C temperature rise by 2050 will expand drought-affected areas. Farmer adaptation varies sharply by farm size: large farms (>100 ha) adopt precision agriculture (65% uptake), while smallholders (<10 ha) rely on crop rotation and drought-resistant varieties. Although substantial support is provided through the EU Common Agricultural Policy, institutional fragmentation and weak extension services—which reach only 32% of farmers—undermine its effectiveness. Bridging this gap requires integrating accessible remote sensing tools with targeted smallholder support and reformed extension services. Full article

In sub-Saharan Africa, the sustainability of smallholder farming systems is threatened by climate change. Women farmers are often disproportionately affected. These disproportionate impacts are linked to gender-based inequities like limited decision-making power and resource constraints, which limit women’s adaptive capacity. Previous research has examined inequities in agriculture generally, as well as women farmers’ adaptation to climate change. However, relatively few studies have explicitly focused on the experiences of women who are the primary farmers. Intersectional research is also limited. This paper presents the results of a scoping review to identify how climate change affects women smallholder farmers and how they adapt. The review identified 41 studies between 2014 and 2024. The most frequently identified vulnerability factors were access to credit, social and cultural norms, and land issues (e.g., tenure issues). Few studies took an explicitly intersectional approach. The findings suggest the need for support that targets the challenges faced by women smallholders. More intersectional research is needed to examine how gendered impacts are shaped by other forms of inequality and inhibit sustainable livelihood options. The review revealed a pervasive patriarchal assumption in which dual-headed households are often described as “male-headed”. Revising such discourses can support women’s adaptive agency in the face of future climate challenges. These findings have direct implications for the sustainability of smallholder farming systems and rural livelihoods in the region, emphasizing the need for gender-responsive approaches to sustainable development in sub-Saharan Africa. Full article

Agricultural intensification in Eastern Africa has raised concerns about the transport of pesticides and nutrients from farmland into surface waters, posing risks to ecosystems and human health. This study systematically reviews the peer-reviewed literature published between 2010 and 2024 to assess the extent, patterns, and drivers of agrochemical contamination in rivers, lakes, and reservoirs across the region. Reported pesticide concentrations ranged from <0.01 to 0.55 μg L⁻¹, with several studies indicating exceedances of drinking-water or environmental guideline values, particularly for organophosphate and carbamate compounds. Nutrient enrichment was widespread, with nitrate concentrations of 0.99–5.6 mg L⁻¹ and phosphate levels of 0.16–2.0 mg L⁻¹, frequently linked to eutrophication. Many studies showed strong seasonal variability, with higher concentrations during rainy periods due to increased runoff and erosion. Variability among findings reflected differences in land use, catchment characteristics, sampling design, and analytical approaches. Where evaluated, mitigation measures such as vegetated buffer strips, cover cropping, and improved nutrient management were associated with reductions in agrochemical runoff of approximately 50–80%. Overall, agrochemical contamination is widespread across Eastern African basins and influenced by agricultural practices and hydrological dynamics, highlighting the need for strengthened monitoring, improved stewardship, and broader adoption of mitigation strategies. Full article

The rising global population has heightened food demand, placing pressure on agricultural systems, particularly in water-scarce regions such as South Africa. Smallholder farmers, essential to the sector, face climatic variability and resource constraints, necessitating innovative solutions to enhance sustainability and productivity. This study evaluates unmanned aerial vehicles (UAVs) for generating spatially explicit evapotranspiration (ET) estimates in a small-scale sugarcane field, supporting precision water management. Vegetation indices (VIs) derived from UAV-based multispectral imagery were used to predict actual ET (ET_a) and validated against eddy covariance measurements. Five models were assessed, including Normalised Difference Vegetation Index (NDVI)-based and Enhanced Vegetation Index (EVI)-based approaches. Machine learning was used to relate crop coefficients (K_c) to NDVI, enabling improved estimation. The two-band EVI (EVI2) model achieved the highest accuracy, with an R² of 0.63, an RMSE of 0.67, and an MAE of 0.52. ET-VI approaches, particularly EVI2, require lower data and technical complexity, making them suitable for smallholder systems. However, reducing dependence on in situ data remains essential to improve accessibility of remote sensing approaches for agricultural water management in resource-limited environments. These findings demonstrate the potential of UAV-based ET_a modelling to support field-scale irrigation decision-making while highlighting the need for further refinement to improve operational applicability across diverse smallholder farming contexts and beyond. Full article

In smallholder-dominated agricultural systems, farm expansion is often expected to improve agricultural performance, yet a larger operated area does not necessarily translate into higher returns per unit of land. This issue is particularly relevant in rice farming, where land fragmentation, labor constraints, and uneven access to agricultural services may limit the return-enhancing effect of farm-scale management (FSM). Using 2024 household survey data from 732 rice-farming households in Jiangxi Province, China, this study examines how FSM, service-scale management (SSM), and their organizational matching affect rice-farming returns (RFR). We apply ordinary least squares (OLS) regression models with interaction-term specifications and further conduct mechanism, moderation, and heterogeneity analyses. The results show that FSM alone does not automatically increase per-mu net operating returns, whereas SSM is positively associated with RFR. More importantly, the interaction between FSM and SSM is significantly positive, indicating that farm expansion generates return advantages mainly when supported by agricultural socialized services. Mechanism analysis suggests that this synergistic effect operates partly through higher land consolidation (LC) and more formalized service contractualization (SC), while smart agricultural technology (SAT) further strengthens the return-enhancing effect. Heterogeneity analysis further shows that the effect differs across farmers with different operating scales. These findings suggest that smallholder modernization should not be understood as land expansion alone but as the organizational matching between farm scale and the service-based division of labor. Policy efforts should therefore aim to improve agricultural socialized service systems, promote land consolidation, strengthen service contracts, and integrate smart agricultural technologies into service provision. Full article

Choosing the most suitable tractor is a complex and high-stakes decision where technical performance, financial capability, and sustainability considerations must be balanced. However, tractor selection in existing studies lacks objective, sustainability-oriented evaluation frameworks, leaving farmers vulnerable to potentially poor investments with long-term economic, operational, and environmental impacts. Therefore, this research proposes a software-based Decision Support System (DSS) that incorporates objective multi-criteria decision-making (MCDM) models within a management control perspective focused on sustainability and provides a clear, data-driven method for tractor selection for small farmers. Four popular tractor models in Egypt were selected for evaluation based on three criteria related to sustainability: power (C1), purchase price (C2), and availability of maintenance and spare parts (C3). Subsequently, a DSS was implemented using Python, and five MCDM methods—CRITIC, MEREC, Entropy, Standard Deviation (SD), and TOPSIS—were used to select the tractor that best meets sustainability objectives. The findings indicate that tractor T2, which had the lowest purchase price (USD 12,390) and enough power (60 HP), was the best-rated tractor. The impact of each criterion varied by method: C1 was the most important in the Entropy method (0.3657), while C2 was the most important in the CRITIC (0.5552), MEREC (0.3432), and SD (0.5938) weightings. The proposed DSS improves transparency and supports more informed, evidence-based decisions in agricultural mechanization. Overall, the system offers a practical and scalable tool that helps smallholder farmers and policymakers make sustainable tractor choices, contributing to progress toward SDGs 2, 7, 12, and 13. Full article

This study assesses improved maize variety (IMV) adoption, as well as yield effects among smallholder farmers in Benue State, Nigeria, having implications for sustainable crop management and resource-use efficiency. Benue state is commonly known as the “Food Basket of the Nation,” but the average maize yield remains less than 2 t/ha, compared to 7–10 t/ha when achieved under improved technologies, and it shows a key sustainability challenge for food security and land-use efficiency. With primary cross-sectional survey data from 205 smallholder farmers with 107 adopters and 98 non-adopters, selected across Local Government Area (LGAs) in Benue State, this study adopts Propensity Score Matching (PSM) for controlling selection bias and estimating the Average Treatment Effect on the Treated (ATET). Nearest Neighbour Matching acts as a primary estimator through robustness checks while using Radius and Kernel Matching. However, the logit model shows that IMV is greatly determined by gender, use of fertilizer, formal education, cooperative membership, access to irrigation, and extension contact, highlighting the crucial parts of human capital, complementary inputs, and institutional support in promoting sustainable adoption of technology. Following the control for observable differences across matching, a 0.399 log-unit yield gain was achieved by adopters, which is equivalent to approximately 49% higher output per hectare compared to non-adopters, an effect that is robust throughout alternative matching algorithms, and it surpasses the 38.7% national-level yield increase, indicating a regional sustainability premium in Benue State. The gains in productivity can promote land-use efficiency, decrease pressure for agricultural intensification on vulnerable lands, and enhance the case for integrated crop management. But adoption remains limited by access to quality seeds, complementary inputs, credit, and sustained gender barriers. Improving input supply chains, extension services, and institutional support is therefore crucial for developing productivity, resource-use efficiency, and food security across smallholder farming systems. Full article

Farmer Producer Organizations (FPOs) have gained increasing attention as institutional mechanisms for improving the resilience of smallholder farming systems under changing climatic conditions. This study examines the role of FPOs in promoting the adoption of Climate-Smart Agriculture (CSA) practices, improved irrigation strategies, and sustainable land management in the arid region of Pali district, Rajasthan, India. A comparative assessment was conducted between FPO-associated member and non-member farmers to evaluate differences in climate change perception, adoption behaviour, and adaptive capacity. The study employed a mixed-methods research design using primary data collected from 408 farm households through structured interviews, focus group discussions, and key informant consultations. Descriptive statistics, mean comparison tests and regression analysis were used to examine adoption patterns and identify the major factors influencing farmers’ responses to climate risks. The findings indicate that delayed rainfall, rising temperatures, and increasing drought frequency are widely perceived by farmers as major threats to agricultural production. FPO membership was associated with higher levels of climate-risk awareness and greater reported adoption of CSA practices; however, these findings should be interpreted as associations rather than causal effects. Farmers linked with FPOs reported stronger uptake of improved and stress-tolerant crop varieties, crop diversification, mixed farming systems, agroforestry, soil moisture conservation, rainwater harvesting, improved irrigation methods, and integrated pest management practices. Education, farm size, access to extension services, market linkages, and climate information were also found to significantly influence adoption decisions. The study highlights the important contribution of FPOs in reducing transaction costs, improving access to inputs, technical knowledge, credit and markets, and encouraging collective responses to climate stress. Strengthening FPO governance, expanding extension support, and targeting vulnerable farmer groups can substantially enhance climate resilience and support sustainable agricultural transitions in arid regions. The findings demonstrate that farmer organizations can serve as effective intermediary institutions linking household-level adaptation strategies with broader goals of irrigation efficiency, land management, and rural sustainability. Full article

Agricultural supply chains operating under rural infrastructure constraints face persistent logistical inefficiencies that reduce producer income and weaken territorial sustainability. This paper assesses how collaborative and coordinated distribution architectures reshape economic performance, efficiency, and equity in dispersed networks of cocoa producers in El Carmen de Bolívar, Colombia. The unified optimization framework compares three regimes: decentralized non-collaborative individual shipments, collaborative consolidation based on distribution centers, and coordinated distribution with time-window synchronization. The findings show a reduction in average logistics costs from $0.688/kg in decentralized distribution to $0.323/kg with collaborative distribution centers, and even further to $0.282/kg in coordinated distribution, representing an overall reduction of approximately 59%. A sensitivity analysis across 64 accessibility configurations shows that the advantage of coordination increases as time rigidity increases. These structural improvements translate into a 13.97% increase in total producer utility, raising average utility from $278 to $317 per producer. In addition, the distributional assessment based on Lorenz curves and Gini coefficients indicates that inequality remains stable despite gains in welfare. These results demonstrate that spatial consolidation combined with temporal synchronization is a decisive lever for resilient and inclusive rural supply systems. Full article

Environmental degradation caused by unsustainable farming practices has depleted soil resources across sub-Saharan Africa. Conservation agriculture (CA) has been promoted to reverse this damage, yet outcomes remain variable, and the role of long-term training is underexplored. Using propensity score matching with 238 smallholder households across five communities in Ghana, we examine the impacts of long-term CA training. Specifically, we assess whether participation in a training program characterized by repeated engagement and follow-up workshops improves yields, farmer knowledge of soil health, and soil indicators (nitrogen and carbon). Farmers receiving long-term CA training did not exhibit significantly better soil chemical metrics. However, they demonstrated significantly more accurate knowledge of soil health (nitrogen, p < 0.001; carbon, p < 0.05), produced a 10.7% higher maize yield (kg/acre) (p < 0.001), and reported fewer soil problems, including fertilizer runoff, top-soil erosion, and waterlogging, compared to conventional farmers (all p < 0.05). We conclude that long-term CA training enhances farmer knowledge and maize yields, suggesting it is a critical intervention for improving productivity and farm management resilience, even where direct improvements in measured soil metrics are not immediately detectable. These findings highlight the need for training programs to emphasize the full suite of CA principles and for evaluation timeframes of 5–10 years to capture soil regeneration. Full article

Unmanned aerial vehicle (UAV)-based remote sensing combined with artificial intelligence (AI) has emerged as a key enabler of climate-smart agriculture (CSA). However, the extent to which these technologies operationalize CSA’s three pillars, productivity, adaptation, and mitigation, remains unevenly assessed. This study presents a PRISMA-guided systematic review of 59 peer-reviewed studies examining UAV–AI applications in agricultural systems. The synthesis categorizes platform configurations, sensor modalities, analytical architectures, geographic distribution, and data integration strategies, and evaluates their alignment with CSA objectives. Results indicate that productivity-oriented applications, including yield estimation, biomass mapping, and nutrient assessment, are the most mature, while adaptation-focused stress detection is also well established. In contrast, mitigation-oriented applications, such as carbon quantification and greenhouse gas monitoring, remain comparatively underrepresented. The analysis further reveals a growing convergence toward multimodal sensing and cross-scale data integration linking UAV observations with satellite and environmental datasets. However, substantial variability in validation approaches and dataset representativeness limits generalizability and scalability. Advancing UAV–AI contributions to CSA therefore requires methodological standardization, interoperable data governance, and strengthened institutional capacity. Collectively, the findings position UAV–AI systems as emerging components of climate-smart agricultural intelligence infrastructure rather than isolated monitoring tools. Full article

Fall armyworm, Spodoptera frugiperda (J.E. Smith), has emerged as one of the most damaging invasive pests affecting maize production and household food security across sub-Saharan Africa since its first detection in 2016. This systematic review synthesizes empirical evidence published between 2016 and 2025 to assess the agronomic, livelihood, and food security impacts of FAW on smallholder farming systems across Eastern, Southern, Western, and Central Africa. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Population, Intervention, Comparison, Outcome, Time, and Setting (PICOTS) framework, 20 studies (17 empirical and 3 contextual) were identified through comprehensive searches of academic databases and institutional repositories and were included in the final synthesis after methodological screening. The evidence indicates that FAW invasion causes substantial maize yield losses ranging from approximately 20% to 50%, with the greatest reductions reported in rain-fed systems with limited access to pest management technologies. Infestation rates frequently exceeded 50%, particularly during early invasion phases. Beyond agronomic losses, several studies reported reduced household income, constrained food availability, and livelihood disruptions, including increased labor requirements, higher production costs, and reliance on short-term coping strategies. Only a small proportion of studies (n = 4) directly assessed nutrition-related indicators, but the available evidence indicates declines in dietary diversity in severely affected communities. Overall, the agronomic impacts of FAW are consistently documented across regions, whereas the socioeconomic and nutrition outcomes remain comparatively underreported, indicating a significant evidence gap. These findings highlight FAW as both an agronomic and livelihood challenge, underscoring the need for integrated pest management strategies, strengthened extension services, and coordinated policy responses to safeguard food and income security among smallholder farmers in Africa. Full article