CONTEXT Chemical pesticides remain central to agricultural productivity but pose well documented risks to human health and ecosystems. While Integrated Pest Management (IPM) offers a more sustainable alternative, its adoption remains limited, partly due to the complex trade-offs that are not easily quantified and compared. OBJECTIVE This study presents the IPM Self-Assessment Tool (ISAT), a web-based application designed to enable transparent comparison of crop protection scenarios at the field level, integrating environmental, human health, agronomic, and economic indicators. METHODS A new harmonized pesticide risk indicator for Europe (PLEU – Pesticide Load Indicator for Europe) was developed. The PLEU covers 41 hazard metrics across three sub-indicators (Environmental Fate, Ecotoxicity, and Human Health). The PLEU was integrated into the ISAT alongside indicators for greenhouse gas (GHG) emissions and production costs, calculated using life cycle based methods. The tool was developed using data from ten European countries covering eight crops, with scenarios described by regional specialists and validated by farm advisors. RESULTS AND CONCLUSIONS The PLEU was calculated for 502 active ingredients (441 synthetic, 61 biopesticides) across eight pesticide groups. A case study on winter wheat in Germany showed that, relative to conventional practice, an IPM scenario replacing herbicide use by mechanical weeding reduced pesticide risk by up to 75%, but at the cost of GHG emissions comparable to the conventional scenario. The ISAT enables users to identify risk-driving active ingredients and explore lower-risk alternatives, supporting informed decision-making for IPM adoption. SIGNIFICANCE To our knowledge, ISAT is the first freely accessible, web-based tool integrating a harmonized pesticide risk indicator with GHG emissions and economic outcomes within a single, comprehensive field level scenario comparison. It provides a transparent decision-support platform that can help farmers, advisors, and researchers make informed decisions when designing and comparing crop protection strategies across diverse European cropping systems.
CONTEXT Controlled-release fertilizers (CRFs) and conventional fertilizers both influence nitrogen (N) availability in coarse-textured soils, but their effectiveness depends on how fertilizer release or transformation aligns with crop N uptake and weather-driven N-loss processes. However, their agronomic and N-loss trade-offs across contrasting historical weather conditions remain insufficiently quantified in irrigated, coarse-textured soils. OBJECTIVE This study evaluated the ability of CSM-CERES-Maize to reproduce observed crop growth, grain yield, aboveground crop N, and soil NO₃−–N responses under contrasting controlled-release fertilizer (CRF) and split-applied UAN management strategies and examined how these integrated N-management strategies affected simulated N partitioning and agronomic–environmental trade-offs across 36 independent historical weather-year simulations. METHODS The model was calibrated using 2023 field observations and independently evaluated using 2022 observations from irrigated maize, including leaf area index, aboveground biomass, yield, aboveground crop N, and 0–90 cm soil NO₃−–N. CRFs were implemented with DSSAT's product-informed logistic controlled-release function. The evaluated model was applied to 36 independent historical weather-year simulations to derive crop N uptake, NO₃−–N leaching, model-derived gaseous N losses, soil mineral N change, apparent N recovery efficiency and crop N capture relative to total modeled N input. RESULTS AND CONCLUSIONS Model agreement was strongest for grain yield and aboveground crop N, whereas biomass and soil NO₃−–N performance was more variable during independent evaluation. Lower CRF rates generally maintained higher simulated N-use metrics and lower modeled NO₃−–N leaching, whereas higher CRF rates produced greater grain yield and crop N uptake. However, scenario-based simulated NO₃−–N leaching increased with higher CRF rates, from 17.0 ± 19.3 to 49.4 ± 47.7 kg N ha−1. For CONV 269, simulated gaseous N loss was strongly dependent on UAN placement representation, decreasing from 64.05 ± 8.34 kg N ha−1 under the surface-band representation (S0; 0 cm) to 1.10 ± 0.11 kg N ha−1 under the shallow subsurface representation (S2; 2 cm). Thus, the magnitude and ranking of the simulated CONV 269 gaseous-loss response were conditional on the assumed UAN placement representation. SIGNIFICANCE Process-based modeling reveals trade-offs among integrated N-management strategies, showing that CRF performance under the evaluated site-specific conditions reflected the modeled interaction among the assumed fertilizer-release pattern, N rate, application schedule, and hydrologic variability. These findings may inform N-management decisions for irrigated maize grown under site conditions comparable to those evaluated in this study but should not be generalized to all coarse-textured production systems.
Context Developing appropriate irrigation and nitrogen application schedules (INASs) is essential for maize production on sandy soils, where low water-retention and nutrient-holding capacities make crop performance highly sensitive to within-season water and nitrogen management. However, most crop-model-based optimization studies have focused mainly on yield and resource-use efficiency, while grain nutritional quality and the economic qualifications of optimized schedules have received less attention. Objective This study aimed to develop a CERES-Maize–NSGA-III framework to optimize INASs for shallow-buried drip-irrigated maize on sandy soil, while clarifying trade-offs among yield, crop water productivity (WPc), nitrogen physiological efficiency (PEN), and grain nitrogen concentration (GNC), and evaluate the economic performance of selected candidate schedules through partial-budget analysis. Methods CERES-Maize was calibrated and validated using two years of field observations and then coupled with NSGA-III to optimize irrigation timing, irrigation amount, nitrogen application timing, and nitrogen rate. Pareto-optimal schedules were further evaluated through candidate-schedule selection, weather-year stress testing, and partial-budget economic sensitivity analysis. Results and conclusions CERES-Maize reproduced maize growth, yield, seasonal water use, and total nitrogen uptake with acceptable accuracy. The Pareto front revealed clear trade-offs among yield, WPc, PEN, and GNC. The 16 high-yield (HY) candidates selected using the historical farmer-yield benchmark exceeded the corresponding weather-matched farmer yield in all five annual simulations and maintained positive ΔPNR across all tested price–cost combinations. Among these candidates, HY01 showed the strongest combined yield and partial-budget performance, with the highest five-year mean simulated yield (12,003.2 kg/ha) and the highest mean ΔPNR relative to the corresponding farmer-managed schedules (1307.83 yuan/ha) across the tested weather-year and price–cost combinations. In contrast, the distance-to-utopia compromise schedule produced lower yield than the corresponding farmer-managed schedule in each weather year and showed weather- and price–cost-dependent economic performance. Significance The proposed framework provides a biophysical optimization tool for generating candidate INASs and clarifying trade-offs among yield, WPc, PEN, and GNC in sandy-soil maize. Weather variability was evaluated only through ex-post simulations of selected candidates and was not incorporated directly into the optimization. Further field-level, operational, economic, and environmental validation is therefore required before practical implementation.
CONTEXT Cropland systems in river-basin agricultural regions are jointly shaped by hydro-climatic constraints, cropland configuration and human–land development pressure. Existing assessments often separate productive, structural and dynamic properties and rarely localize dominant drivers for differentiated regulation. OBJECTIVE This study developed a framework linking multidimensional cropland ecosystem health assessment, spatialized dominant-driver diagnosis and mechanism-informed zoning in the middle and lower reaches of the Yellow River, China. METHODS Cropland ecosystem health was evaluated from vitality, organizational stability and resilience using environmental, land-use and socioeconomic data harmonized to a 1 km grid for 2000–2011 and 2012–2023. Random Forest and XGBoost quantified driver relationships, while local TreeSHAP contributions, current CH grade and standardized diagnostic scores supported zoning. RESULTS AND CONCLUSIONS Cropland ecosystem health was relatively favourable in Henan and Shandong and lower in the western and northern sections. Between the two periods, 57.39% of grids shifted to lower vitality grades, whereas 93.45% and 64.64% shifted to higher organizational stability and resilience grades, respectively. Integrated health nevertheless declined in 54.62% of the grids. Precipitation remained the leading predictor, accounting for 38.0% and 35.4% of total TreeSHAP importance. The contributions of cropland density and GDP density increased from 10.0% to 16.1% and from 9.2% to 16.5%, respectively. Spatialized dominant-driver diagnosis supported the delineation of five regulation zones: mountain ecological conservation, extensive cropland maintenance, terrain–hydrology constraint regulation, cropland-intensive coordination and development-pressure control. SIGNIFICANCE The framework reveals asynchronous changes within integrated cropland health and links regional driver shifts and local dominant factors with differentiated management priorities.
Understanding how tillage-residue practices regulate water balance is essential for sustaining rain-fed agriculture winter wheat production on the Loess Plateau. This study integrated a three-year field experiment (2020−2023) with a 38-year (1978–2016) WHCNS simulation to compare the effects of no-tillage with surface straw mulching (NT) and rotary tillage with straw incorporation (RT) on soil water dynamics and winter wheat yield. The WHCNS model achieved acceptable performance for soil moisture, leaf area index, and biomass, but failed to accurately capture yield losses triggered by extreme pre-sowing-to-early-seedling-stage waterlogging. Compared with RT, NT suppressed soil evaporation, promoted transpiration and infiltration, and improved water use efficiency, grain yield and inter-annual yield stability. Correlation and covariance-based structural equation modelling (CB-SEM) revealed divergent fallow-rainfall utilization mechanisms between the two systems. Under RT, high bare-soil evaporation during hot July–August largely dissipated early fallow rainfall, so pre-sowing initial soil-water storage (ISW) and yield strongly relied on late-fallow September precipitation. By contrast, surface straw mulching under NT reduced non-productive evaporation, enabling early-season July–August fallow rainfall to effectively recharge soil water; September rainfall still exerted positive effects, yet August rainfall made meaningful contributions to ISW. For both treatments, fallow rainfall regulated grain yield mainly via the mediation chain: fallow rainfall → pre-sowing ISW → water stress → wheat yield. This study provides theoretical insights for adopting NT as a sustainable water-saving management practice for semi-arid rain-fed wheat regions, though simulation-derived findings require further field validation.
Context Small ruminants play a critical role in addressing poverty, household food security, employment, and income generation across South Asia. However, increasing climate induced hazards affect productivity and limit socio-economic contributions of small ruminants. Objective In this context, this study assesses climate induced hazards at current scenarios and projects climatic risk SSP2–4.5 and SSP5–8.5 scenarios during the 2050s and 2080s. Methods The Participatory Climate Risk Assessment approach was used to identify and validate climate hazards, hazard thresholds, and region-specific adaptation options through consultations with 26 experts and stakeholder workshops. These inputs were then combined with high-resolution gridded climate data from CHC, CHIRTS, and CHIRPS, along with CMIP6-based projections, while livestock exposure was estimated using FAO-GLW4 and sub-national small-ruminant population data. These datasets were used to estimate hazard and climate risk at the grid level under the SSP2–4.5 and SSP5–8.5 scenarios for the 2050s and 2080s. Results The results revealed that climate induced temperature–humidity (TH) stress is the most prevalent and expanding hazard for small ruminants followed by cyclones and rainfall deficits. While cold stress is a declining hazard, floods, and extreme rainfall are locally significant. During the 2050s, approximately 27% of goats and 37% of sheep in South Asia are projected to experience high to very high climate risk. Currently high to very high climate risk areas are concentrated in Pakistan, Bangladesh, and eastern and coastal India. It is projected to extend further under SSP2–4.5 and SSP5–8.5 scenarios spatially in above countries. Based on the identified hazards, the main adaptation priorities were better animal housing and cooling, improved feeding and watering practices, stronger disease prevention, the use of climate-resilient breeds, and better access to climate information and risk-transfer services. Further, the findings of this study emphasize the need for developing region-specific, multi-hazard adaptation plans to address hazards and resource limitations rather than pan country plans. Based on suitability, these adaptation options need to be integrated with livestock development and climate policies. Interventions demanding high investments such as housing need to be supported through incentives. In long term, strengthening extension systems and development of climate resilience breeds are considered crucial for enhancing resilience of small ruminants in South Asia. Conclusion The study offers a spatially explicit framework for identifying climate-risk hotspots and guiding location-specific adaptation strategies for small-ruminant production systems under changing climatic conditions. Significance The findings point to the need for region-specific climate policies that focus investment on resilient housing, feed and water management, livestock health services, climate-resilient breeds, climate information systems, and risk-transfer mechanisms in the most vulnerable areas.
Context More than two billion people are deficient in key minerals and vitamins, including zinc, iron, vitamin A, and iodine. At the same time, one-third of the world's soils, from which 95% of our food is produced, are degraded. Current agricultural systems prioritize productivity over nutritional quality, human and planetary health. New agricultural approaches that produce nutritious food while enhancing soil and planetary health are needed. Regenerative agriculture places strong emphasis on soil health, and although the evidence is limited, studies suggest it can enhance the nutritional quality of crops and animals. Objective This paper introduces a new concept of ‘regenerative fortification’ – a soil-centric approach to enhancing the nutritional quality of food through soil chemical, physical, and biological pathways driven by regenerative agricultural practices. Methods A scientific literature review on biofortification and regenerative agricultural approaches was conducted, followed by the introduction of the regenerative fortification approach and a critical discussion comparing it with existing biofortification strategies. Supporting evidence reporting on the effects of various regenerative practices on food nutritional quality was compiled. Results and conclusions There is growing evidence that regenerative practices can enhance the nutritional quality of food. The introduced regenerative fortification approach advances this by positioning soil health as a central determinant of food nutritional quality and rhizosphere soil as the intersection of nutrition. As the efficacy of regenerative fortification may vary across contexts, management, and commodities, it underscores the need for context-specific optimization and a better mechanistic understanding of the underlying effects. A list of key research directions is provided. Significance Regenerative fortification represents a potentially promising, scalable pathway to align agricultural production with global nutrition and climate goals, while reinforcing the foundational role of soil in sustaining both ecosystem and human health.
CONTEXT Adjusting sowing dates is a crucial strategy for enhancing crop yields, improving resource utilization efficiency, and adapting to climate change. However, traditional sowing date optimization primarily relies on field trials and empirical guidance. OBJECTIVE This study aims to develop a digital method to support climate-smart decisions on sowing dates, helping ensure sustainable and secure rice production. METHODS This study developed a rice yield prediction framework by integrating crop model, extreme climate indices, and machine learning algorithms. Using this framework, the study assessed the impact of sowing date shifts on double-cropping rice yields. RESULTS AND CONCLUSION The hybrid modelling approach effectively and accurately predicted rice yields, achieving a normalized root mean square error (NRMSE) of 10.6% in the test set. Furthermore, considering rotational constraints between early and late rice, this study identified the optimal sowing dates (OSDs) and the suitable sowing window to maximize the yield of double-cropping rice in southern China. Compared to actual sowing dates, projected yield improvements under optimal sowing dates were 0.60%–4.59% for early rice, 4.55%–10.86% for late rice, and 4.27%–6.46% for total double-cropping rice yield. The findings revealed a northward delay in suitable sowing periods (SSPs) for early rice, with the ideal period ranging from February 20 to April 25 across different provinces. The SSPs for late rice generally ranged from May 24 to August 5, with the latest suitable dates mainly occurring in lower-latitude areas where thermal resources were sufficient for safe maturity. SIGNIFICANCE This study provided valuable insights into optimizing thermal and light resource utilization for double-cropping rice systems and developing adaptive strategies under climate change.
Context Intercropping, growing multiple plant species simultaneously on the same field, provides clear ecological benefits in experimental settings. However, its implementation in real farms and its impacts on crop diversity and composition at the cropping-system level remain understudied. Objective This study aimed at assessing how intercropping influences cropping-system diversity. Methods Data from the French DEPHY network, > 2000 farms monitored since 2010, were used to track changes in crop diversity and composition in different crop families before, during, and after intercropping was adopted. We analysed 414 cropping systems, both conventional and organic. Results and conclusions Intercropping resulted in a significant increase of 52% in species richness, 22% in Shannon diversity, and 9% in evenness at the cropping-system level, mainly due to an increase of 96% in the proportion of legumes and reduced proportion of 19% of cereals. Farmers intercropped mainly cereals with legumes, representing 90% of mixtures in organic, and 66% in conventional. Of the 414 systems studied, 40% abandoned intercropping, which resulted in a decrease in diversity and the proportion of legumes. In 92% of the species mixtures, some species were lost when intercropping was abandoned, especially legumes and minor crops. Significance These results demonstrate that intercropping can diversify cropping systems effectively under real farming conditions, particularly by reintroducing legumes, but its long-term benefits on diversity are limited by the low persistence of species after intercropping is abandoned. By providing new evidence on how farmers adopt and abandon intercropping over time, this study provides insights for designing policies and advisory strategies that support biodiversity-friendly farming.
CONTEXT Cocoa swollen shoot virus (CSSV) remains the most destructive viral disease affecting cocoa in West Africa, with ecological and economic repercussions. Since the virus is specialized to cocoa and disperse locally through mealybugs vectors, landscape structure could play a crucial role in determining epidemic outcomes. OBJECTIVES This study aimed to assess how landscape structure influences CSSV spread and economic returns, to identify landscape structures that reconcile epidemiological control with profitability. METHODS The spatially and temporally explicit landsepi model was parameterized for the CSSV pathosys tem using literature-derived data and calibrated through Approximate Bayesian Computation based on field observations. The model was then used to compare landscape mosaics varying in cocoa (host) and coffee (non-host) proportions and spatial aggregation, under contrasting disease pressure, economic, and yield scenarios over a 20-year period. RESULTS AND CONCLUSIONS Epidemiologically, long-term cocoa survival improved in fragmented landscapes, with optimal outcomes below 40% cocoa under low disease pressure and below 20% under high pressure. However, these compositions reduced total system income. Economically, cocoa-dominated landscapes remained profitable under low disease pressure. Under high disease pressure, profitability persisted only in the short (4–5 years) to medium term (12–13 years), depending on cocoa–coffee gains differences. Over longer periods, profitability declined unless profits were accumulated, indicating that short-term benefits only partly offset long-term losses. SIGNIFICANCE Collective, landscape-scale diversification emerges as a promising strategy to contain CSSV while sustaining cocoa production. By jointly considering disease pressure and landscape structure, stakeholders can design context specific landscape mosaic that reconcile epidemic control with economic profitability.
CONTEXT Tropical agriculture faces increasing challenges in sustaining production and rural livelihoods while addressing climate change, biodiversity loss, soil degradation, and resource limitations. Multi-strata agroforestry systems integrating perennial overstory crops with high-value understory species offer a promising pathway for climate-smart intensification and sustainable tropical agriculture. OBJECTIVE This review synthesizes evidence from multi-strata agroforestry systems combining major overstory crops, including Hevea brasiliensis, Cocos nucifera, and Areca catechu, with high-value understory crops such as spices, beverage, and medicinal plants. We develop a trait-based framework linking overstory characteristics, ecological interactions, management strategies, and ecosystem multifunctionality. METHODS A comprehensive literature synthesis was conducted using Web of Science Core Collection, Scopus, and Google Scholar, focusing on productivity, ecosystem services, resource-use efficiency, management practices, and socioeconomic outcomes. The evidence was organized around overstory–understory interactions, climate resilience, ecosystem services, productivity, and adoption challenges. RESULTS AND CONCLUSIONS Multi-strata agroforestry systems enhance carbon (C) storage, biodiversity, soil functions, and land-use efficiency compared with monocultures, with land equivalent ratios (LER) frequently exceeding 1.0. Reported evidence indicates that agroforestry systems can increase C storage by 20–50%, improve soil organic carbon (SOC) stocks by 10–20% compared with monocultures, and reduce midday air and soil temperatures by approximately 3–5 °C and 2–4 °C, respectively. High-value understory crops further enhance productivity and income diversification, with LER values commonly ranging from 1.2 to 1.8 and exceeding 2.0 in some shade-adapted crops. However, outcomes remain context-dependent due to competition for light, water, and nutrients, as well as potential pest and disease risks. Optimized performance depends on complementary species selection, canopy regulation, and adaptive soil fertility management. Key knowledge gaps remain in long-term productivity, C sequestration dynamics, resource competition thresholds, and socioeconomic barriers. SIGNIFICANCE Diversified overstory–understory agroforestry systems represent a nature-based solution for developing resilient tropical landscapes by integrating agricultural productivity, biodiversity conservation, ecosystem services, and climate change mitigation.
CONTEXT Land-use intensification reshapes biodiversity and soil nutrient cycling in mountain agroecosystems. The well-documented positive correlation between nutrient availability and plant diversity is largely supported by evidence from monocropping systems, while complex rural settlement mosaics integrating residential landscaping and small-scale agriculture remain understudied. Existing research rarely accounts for divergent village development types and regional ecological zoning, leaving a key theoretical gap: intensive human management may break the inherent soil resource constraints on plant communities, yet empirical evidence confirming this decoupling effect remains scarce. Closing this knowledge gap underpins the formulation of sustainable mountain rural management strategies that reconcile nutrient utilization efficiency with biodiversity conservation. OBJECTIVE This study aims to quantify the dual responses of plant diversity and soil nutrient pools to rural land-use intensification across distinct development modes, and to systematically assess the direct regulatory capacity of soil properties on plant community assembly in mountainous landscapes. METHODS We conducted field surveys across 38 districts in Chongqing, Southwestern China, comparing plant communities and soil physicochemical properties between human-modified village habitats and adjacent natural environments. Plant α-diversity indices and soil variables (including total and available phosphorus, nitrogen, and stoichiometric ratios) were analyzed using multivariate statistics and structural equation modeling (SEM) to disentangle direct and indirect drivers of plant diversity. By applying this integrated analytical framework, we compared plant diversity and soil properties between habitat types and identified the pathways through which land-use intensification influences plant communities. RESULTS AND CONCLUSIONS A total of 302 plant species were recorded. Human-modified habitats exhibited significantly higher plant α-diversity than natural environments (p < 0.05), primarily driven by species introduction and management practices. Land-use intensification substantially increased soil nutrient availability, particularly phosphorus, and alleviated phosphorus limitation. Although phosphorus availability was strongly associated with plant community differentiation, SEM results showed that plant diversity was primarily driven by climatic factors and village typology, with no significant direct effects of soil variables detected in our analysis under the studied conditions. These findings indicate a decoupling between plant diversity and soil nutrient dynamics under intensive land use, where soil phosphorus reflects management intensity rather than acting as a direct ecological driver. SIGNIFICANCE This study demonstrates that land-use intensification alters plant community assembly primarily through climatic and anthropogenic pathways rather than direct soil nutrient regulation. These findings improve our understanding of biodiversity responses in heterogeneous mountain agroecosystems and provide a scientific basis for developing sustainable rural management strategies that balance biodiversity conservation with agricultural development.
CONTEXT Artificial intelligence is becoming an important interface through which farmers, advisors and policymakers access agricultural knowledge, while misinformation increasingly circulates through the same digital channels. OBJECTIVE This Perspective develops the concept of agri-food knowledge governance to explain how AI-mediated advice and misinformation create shared challenges of knowledge validation, contextual relevance and accountability. METHODS The paper conceptually synthesises literature on agricultural knowledge and innovation systems, digital agriculture governance, agri-food misinformation and the epistemology of testimony. RESULTS AND CONCLUSIONS The synthesis identifies six governance functions: provenance and traceability, context fidelity, human epistemic intermediation, plural authority and data rights, resilience and literacy, and accountability and redress. These functions shift attention beyond technical accuracy toward locally valid, contestable and accountable agricultural advice. They become especially important as AI systems move from generating recommendations to executing decisions. SIGNIFICANCE Agri-food knowledge governance extends existing agricultural knowledge-system approaches by explicitly addressing epistemic authority, contextual validity and accountability. It provides a framework for evaluating and governing AI-enabled advisory systems while preserving plural and locally grounded sources of agricultural knowledge.
