Agricultural Systems

Smart Farming is a development that emphasizes the use of information and communication technology in the cyber-physical farm management cycle. New technologies such as the Internet of Things and Cloud Computing are expected to leverage this development and introduce more robots and artificial intelligence in farming. This is encompassed by the phenomenon of Big Data, massive volumes of data with a wide variety that can be captured, analysed and used for decision-making. This review aims to gain insight into the state-of-the-art of Big Data applications in Smart Farming and identify the related socio-economic challenges to be addressed. Following a structured approach, a conceptual framework for analysis was developed that can also be used for future studies on this topic. The review shows that the scope of Big Data applications in Smart Farming goes beyond primary production; it is influencing the entire food supply chain. Big data are being used to provide predictive insights in farming operations, drive real-time operational decisions, and redesign business processes for game-changing business models. Several authors therefore suggest that Big Data will cause major shifts in roles and power relations among different players in current food supply chain networks. The landscape of stakeholders exhibits an interesting game between powerful tech companies, venture capitalists and often small start-ups and new entrants. At the same time there are several public institutions that publish open data, under the condition that the privacy of persons must be guaranteed. The future of Smart Farming may unravel in a continuum of two extreme scenarios: 1) closed, proprietary systems in which the farmer is part of a highly integrated food supply chain or 2) open, collaborative systems in which the farmer and every other stakeholder in the chain network is flexible in choosing business partners as well for the technology as for the food production side. The further development of data and application infrastructures (platforms and standards) and their institutional embedment will play a crucial role in the battle between these scenarios. From a socio-economic perspective, the authors propose to give research priority to organizational issues concerning governance issues and suitable business models for data sharing in different supply chain scenarios.

Agricultural systems in Europe face accumulating economic, ecological and societal challenges, raising concerns about their resilience to shocks and stresses. These resilience issues need to be addressed with a focus on the regional context in which farming systems operate because farms, farmers' organizations, service suppliers and supply chain actors are embedded in local environments and functions of agriculture . We define resilience of a farming system as its ability to ensure the provision of the system functions in the face of increasingly complex and accumulating economic, social, environmental and institutional shocks and stresses, through capacities of robustness, adaptability and transformability. We (i) develop a framework to assess the resilience of farming systems, and (ii) present a methodology to operationalize the framework with a view to Europe's diverse farming systems. The framework is designed to assess resilience to specific challenges (specified resilience) as well as a farming system's capacity to deal with the unknown, uncertainty and surprise (general resilience). The framework provides a heuristic to analyze system properties, challenges (shocks, long-term stresses), indicators to measure the performance of system functions, resilience capacities and resilience-enhancing attributes. Capacities and attributes refer to adaptive cycle processes of agricultural practices, farm demographics, governance and risk management. The novelty of the framework pertains to the focal scale of analysis, i.e. the farming system level, the consideration of accumulating challenges and various agricultural processes, and the consideration that farming systems provide multiple functions that can change over time. Furthermore, the distinction between three resilience capacities (robustness, adaptability, transformability) ensures that the framework goes beyond narrow definitions that limit resilience to robustness. The methodology deploys a mixed-methods approach: quantitative methods, such as statistics, econometrics and modelling, are used to identify underlying patterns, causal explanations and likely contributing factors; while qualitative methods, such as interviews, participatory approaches and stakeholder workshops, access experiential and contextual knowledge and provide more nuanced insights. More specifically, analysis along the framework explores multiple nested levels of farming systems (e.g. farm, farm household, supply chain, farming system) over a time horizon of 1–2 generations, thereby enabling reflection on potential temporal and scalar trade-offs across resilience attributes. The richness of the framework is illustrated for the arable farming system in Veenkoloniën, the Netherlands. The analysis reveals a relatively low capacity of this farming system to transform and farmers feeling distressed about transformation, while other members of their households have experienced many examples of transformation.

Digital Twins are very promising to bring smart farming to new levels of farming productivity and sustainability. A Digital Twin is a digital equivalent of a real-life object of which it mirrors its behaviour and states over its lifetime in a virtual space. Using Digital Twins as a central means for farm management enables the decoupling of physical flows from its planning and control. As a consequence, farmers can manage operations remotely based on (near) real-time digital information instead of having to rely on direct observation and manual tasks on-site. This allows them to act immediately in case of (expected) deviations and to simulate effects of interventions based on real-life data. This paper analyses how Digital Twins can advance smart farming. It defines the concept, develops a typology of different types of Digital Twins, and proposes a conceptual framework for designing and implementing Digital Twins. The framework comprises a control model based on a general systems approach and an implementation model for Digital Twin systems based on the Internet of Things—Architecture (IoT-A), a reference architecture for IoT systems. The framework is applied to and validated in five smart farming use cases of the European IoF2020 project, focussing on arable farming, dairy farming, greenhouse horticulture, organic vegetable farming and livestock farming.

CONTEXT Many jurisdictions have policies that require simultaneous reductions in greenhouse gas (GHG) emissions and nitrogen (N) and phosphorus (P) losses to water. In New Zealand's pasture-based dairy sector, farm planning tools are used to target actions to mitigate emissions and nutrient losses. However, the regional effectiveness and co-benefits from these actions are highly variable. OBJECTIVE We aimed to determine if widely promoted on farm actions, implemented independently, could reduce emissions and nutrient losses with minimal trade-offs, and if effectiveness would vary by region. METHODS Using data from over 750 dairy farms (2013–2024) and > 6300 data records in New Zealand, we used gradient boosted regression trees to model changes in emissions and losses in response to six mitigation actions: three examined reduced fertiliser usage options, two improved irrigation and effluent practices, and one avoided winter forage cropping. RESULTS AND CONCLUSIONS Modelling indicated reductions in N and P losses to water (0–21% and 1–10%, respectively) and GHG emissions (1–11%) with no statistically significant trade-offs between water and air outcomes. Avoiding winter forage cropping substantially reduced N and P losses in regions where the practice is prevalent. The greatest overall benefits were observed in Canterbury and Southland. Results demonstrate that widely promoted good farming practices can deliver simultaneous improvements in water quality and climate performance. SIGNIFICANCE This study provides national-scale evidence to support ongoing good farming practice implementation and give farmers confidence that multiple environmental outcomes can be pursued with minimal unintended consequences, while highlighting that the extent and feasibility of uptake will depend on individual farm systems and local conditions.

Context Soil water-heat-salt migration and crop growth in film-mulched drip irrigation fields in seasonally frozen regions are influenced by partial mulching and drip irrigation during the growth period, as well as soil freeze-thaw processes during the fallow period. Understanding year-round soil water-heat-salt dynamics and crop growth is essential for sustainable agricultural water use. Objective However, existing year-round models often use one-dimensional (1D) approaches during the growth period, which neglect horizontal salt migration under partial mulching and drip irrigation, leading to an overestimation of salt leaching efficiency. This limitation can be addressed by developing a year-round model that incorporates the distinct dynamics of both growth and fallow periods. Methods In this study, we developed a year-round coupled model, CAWM-S, by integrating the previously developed Crop and Water Model based on HYDRUS-2D (CAWM-H2D) with the SHAW model, which considers soil freeze-thaw processes. The key features of CAWM-S include detailed depiction of 2D soil water-heat-salt migration, crop growth and yield formation, and energy balance at the film-mulched soil surface during the growth period, as well as the redistribution of soil water-salt due to freeze-thaw processes in the fallow period. The model was calibrated and validated using monitored data from two consecutive year-round periods in a film-mulched drip irrigation cotton field in southern Xinjiang, a region with seasonal frozen soils, under different irrigation water salinity levels. This validation is treatment-wise and limited to the same site and two-year period; independent validation across other sites, years, crops, and irrigation systems remains necessary. Results and conclusions The simulated results for soil water, temperature, salinity, crop growth indicators, and yield generally matched the measured data across the two years with varying salinity levels, with treatment-averaged R 2 values ranging from 0.48 to 0.98 and NSE values 0.42 to 0.97. Furthermore, we analyzed the effects of different irrigation volume and salinity combinations on soil desalination rates, evapotranspiration and its components, crop yield, and water productivity, revealing a quadratic relationship between optimal irrigation volume and water salinity under film-mulched drip irrigation. Validation results show that CAWM-S effectively quantifies coupled soil water-heat-salt dynamics and crop growth, and predicts salt redistribution across year-round cycles in seasonally frozen regions. Significance It provides an effective tool for supporting sustainable agriculture by aiding water saving, control of soil salinization and improvement of crop water productivity in the film-mulched drip irrigation fields within seasonally frozen regions.

CONTEXT Farm economic sustainability is commonly assessed through individual outcomes such as profitability, income or productivity. This can obscure differences and trade-offs among the economic functions supporting long-term farm viability. Spanish sheep and goat farming provides an informative setting because environmental and social value coexist with economic fragility and substantial exposure to agricultural-policy support. OBJECTIVE This study develops and empirically examines a function-oriented framework to assess how ESG-related mechanisms are associated with farm economic sustainability and whether these associations differ across outcomes. It distinguishes five internal economic functions-profitability, liquidity, labour productivity, productive capital and fixed capital formation-from public support received as a complementary institutional-exposure outcome. METHODS A balanced panel of 480 Spanish sheep and goat farms observed from 2015 to 2022 (3840 farm-year observations) was analysed using FADN/RECAN data. Separate two-way farm and year fixed-effects models were estimated for the six outcomes, with farm-clustered robust standard errors. Joint Wald tests assessed dimension-level ESG associations, while cross-equation coefficient-equality tests on standardised outcomes assessed heterogeneity. Benjamini-Hochberg false-discovery-rate correction addressed multiple testing. Robustness analyses examined extreme observations, outcome scaling, non-linearities, cross-dimensional interactions, lagged explanatory associations and outcome persistence, including checks for short-panel dynamic bias. RESULTS AND CONCLUSIONS ESG-related associations are selective and strongly heterogeneous. Land availability is positively associated with labour productivity, fixed capital formation and public support received. Energy expenditure is negatively associated with profitability, liquidity and labour productivity, but positively associated with fixed capital formation. Mineral fertiliser use is positively associated with productive capital, whereas herbivore feed costs show no significant association with any outcome in the main specification. Social associations are concentrated primarily in labour productivity; governance/institutional associations are outcome-specific. After false-discovery-rate correction, joint associations remain for the environmental dimension with labour productivity, productive capital and fixed capital formation, the social dimension with labour productivity, and the governance/institutional dimension with public support received. Cross-equation tests reject coefficient homogeneity globally and for each ESG dimension. Principal patterns are broadly robust to distributional treatments and outcome scaling, although some associations are sensitive to transformation. Robust non-linearities are concentrated in labour productivity, and no tested cross-dimensional interaction survives multiple-testing correction. Dynamic analyses indicate particularly strong persistence in productive capital, with positive persistence also in profitability and labour productivity. SIGNIFICANCE Distinguishing internal economic functions from institutional exposure reveals heterogeneity and trade-offs that a single performance indicator can conceal. The framework provides a diagnostic basis for sustainability assessment and policy discussion without assuming that improvement in one outcome compensates for deterioration in another. Accounting-based ESG proxies and observational within-farm associations do not establish causal effects or directly measure environmental, social or governance performance. Findings apply to Spanish sheep and goat farms continuously represented in FADN during 2015–2022.

CONTEXT Glyphosate-based herbicides are widely used worldwide due to their broad efficacy and low cost. In the European Union (EU), large volumes are mobilized and enter the environment, sparking environmental concerns and calls for stricter regulation. However, few studies have tackled the contextual factors and agronomic reasoning that could influence glyphosate use and the resulting degree of dependence in arable cropping systems. METHODS On a dataset describing the farm context and details of cropping systems in 898 French farms over 11 years, we used Random Forests and Classification and Regression Trees to elucidate how combinations of pedoclimatic, agronomic, and operational factors link to the likelihood of glyphosate applications and the quantities used. RESULTS AND CONCLUSIONS Challenging production contexts and pedoclimatic conditions were often associated with glyphosate use, suggesting an adaptive response to environmental constraints. The relationship between rotational diversity and glyphosate use varied by management strategy, with diversified cropping systems exhibiting reduced quantities. Yet, diverse rotations under conservation-oriented systems also often maintained reliance on glyphosate-based herbicides. Cover crops were associated with frequent glyphosate applications, whereas temporary grasslands—often associated with livestock integration—were associated with reduced use, likely indicative of land reallocation in which perennial grassland phases displace annuals and offer opportunities to implement non-chemical alternatives. The prevalence of mid-fallow applications suggests that organizational convenience shapes application timing by spreading out activity peaks, revealing opportunities to reduce glyphosate through alternative management optimization. SIGNIFICANCE Three distinct usage tiers—low, moderate, and high—contrast across various archetypes, encompassing operational convenience to technical ‘lock-ins’. These findings offer valuable insights for reducing dependence on glyphosate and developing more sustainable cropping systems.

Context Green manure is increasingly recognized as a sustainable practice for improving soil fertility, enhancing carbon cycling, and sustaining rice productivity. However, its effects on soil labile carbon pools and rice yield vary widely across climate conditions, soil backgrounds, agricultural management measures, and residue quality. The relative importance of green manure characteristics and management practices in regulating these responses remains insufficiently quantified. Objective This study aimed to quantify the effects of green manure incorporation on soil labile carbon pools and rice yield, identify the roles of climate, soil characteristics, management measures, and green manure traits, and determine the key drivers of carbon pool activation and yield formation by integrating meta-analysis with machine learning. Methods A meta-analysis was conducted using 472 paired observations from 94 peer-reviewed studies. We quantified the effects of green manure on soil organic carbon (SOC), microbial biomass carbon (MBC), dissolved organic carbon (DOC), particulate organic carbon (POC), and rice yield. Subgroup analyses were performed according to climate zone, mean annual temperature, mean annual precipitation, initial soil characteristics, experimental duration, green manure incorporation rate, incorporation timing, green manure type, and green manure C, N, P, and K contents. Random forest, LightGBM, and gradient boosting decision tree models were used to identify dominant predictors. Results and conclusions Green manure significantly increased SOC, MBC, DOC, and rice yield by 14.6%, 26.5%, 29.4%, and 21.9%, respectively, demonstrating its dual role in activating soil carbon pools and enhancing rice productivity. These responses were jointly regulated by climate, soil properties, management practices, and green manure quality, with stronger effects generally observed under warm and humid climates, appropriate application rates and incorporation timing, favorable soil backgrounds, and suitable residue nutrient composition. Machine learning identified mean annual temperature, green manure incorporation rate, and experimental duration as key drivers of SOC; soil available phosphorus, green manure carbon content, and soil pH as the most influential variables within the MBC model; initial SOC, soil pH, and experimental duration as major regulators of DOC; and green manure carbon content, soil pH, and soil total nitrogen as key determinants of rice yield. Significance This study provides quantitative evidence that green manure improves soil labile carbon pools and sustains rice productivity. Optimizing green manure type, application rate, incorporation timing, and residue nutrient quality according to regional climate and soil conditions can support sustainable, region-specific paddy management.

CONTEXT Sustainable agricultural production in arid regions is constrained by water scarcity and soil salinization. Effective irrigation and salinity management requires integrated understanding of soil water–salt dynamics and crop responses, yet existing modeling approaches often treat two-dimensional soil processes and dynamic crop growth separately, limiting their utility for field-scale management analysis. OBJECTIVE This study aims to develop and validate a coupled modeling framework that integrates two-dimensional soil water and solute transport with crop growth simulation, in order to improve the representation of soil–crop interactions under film-mulched drip irrigation and to provide a quantitative basis for irrigation and salinity management. METHODS A HYDRUS-AquaCrop-Coupled (HAC) model was constructed by externally linking the HYDRUS-2D model (water flow and solute transport) and the AquaCrop model (water-driven crop growth) via a central coupler written in Python. The two models operate on synchronized daily time steps with bidirectional data exchange: AquaCrop supplies daily potential transpiration, soil evaporation, and canopy cover to HYDRUS-2D, while HYDRUS-2D returns soil water content and salinity profiles for use as initial conditions in the subsequent AquaCrop time step. The coupled model was calibrated and validated using two-year field data, encompassing multiple irrigation and initial salinity treatments. Twenty-five scenarios combining five irrigation levels and five initial salinity levels were simulated to examine crop yield and biomass responses. RESULTS AND CONCLUSIONS The HAC model outperformed the standalone AquaCrop model in reproducing soil water content and salinity, with average RMSE improvements of 27.6% and 18.36%, respectively, while maintaining good agreement with observed canopy cover, aboveground biomass, and crop yield ( R 2 > 0.89, d > 0.81). Scenario simulations for the tested site indicated that the yield remained near its maximum when initial ECe ≤ 7.7 dS·m −1 and declined beyond this level. Model-derived, site-specific stage thresholds increased from 8.46 dS·m −1 at the seedling stage to 12.22 dS·m −1 at the flower boll stage. Under non-saline conditions, increasing irrigation amount within the tested range accelerated attainment of maximum yield but provided limited additional yield benefit. SIGNIFICANCE This study demonstrates a practical external coupling methodology that leverages the complementary strengths of a mechanistic soil model and a parsimonious crop model without requiring extensive source code modification. The model application here is restricted to the specific experimental site of the study region. To enable broader extrapolation, additional evaluations across varied soil types, climatic zones and cropping systems are necessary. Such follow-up work can quantify irrigation–salinity–yield trade-offs in water-limited salinized regions and enhance the generalizability of the proposed coupling framework.

CONTEXT China, the global largest pork producer and consumer, faces severe environmental pressures from inadequate manure management, yet comprehensive empirical analyses of environment-economy trade-offs remain scarce. OBJECTIVE This study provides a comprehensive comparative assessment of manure treatment modes in pig production across multiple environmental footprints and economic benefits, and optimizes stage-specific technologies into integrated packages that reconcile mitigation with profitability across the manure management chain. METHODS This study integrates farm survey data ( n = 162), life cycle assessment, and linear and multi-objective optimization to assess co-benefits or trade-offs of technology combinations under various objectives. RESULTS AND CONCLUSIONS Anaerobic digestion (AD) mode minimizes greenhouse gas (GHG) emissions and phosphorus (P) losses, while combining composting with AD mode minimizes reactive nitrogen (Nr) emissions. Single-objective optimization reveals acute trade-offs, profit maximization exacerbates emissions, whereas environmental minimization depresses net returns by up to 48%. Multi-objective optimization shows these trade-offs are surmountable. Whole-chain packages integrating source reduction, process control, and end-of-pipe measures reduce Nr by 41–44%, GHG by 15–16%, and P by 22–48%, while limiting profit reductions to 12–22%. Notably, these co-benefit packages increase net ecosystem economic benefits (NEEB) by 85–99% relative to the baseline, as the societal value of emission reductions outweighs the pig farm profit losses. For advanced modes, further environmental mitigation gains are smaller (Nr 30–35%) and costlier, indicating diminishing returns. SIGNIFICANCE Our study demonstrates that systematic technology integration can reconcile environmental sustainability with economic viability while generating net societal benefits in livestock production.

CONTEXT Low-carbon rice farming is widely recognized as a key strategy for agricultural decarbonization, yet adoption remains limited among smallholder farmers despite expanding AI-enabled advisory and increasing awareness of climate risks. Existing adoption-based models provide limited explanation for why improved information and awareness often fail to translate into low-carbon transition. OBJECTIVE This study explains how farmers translate information into transition intention under real-world constraints by conceptualizing low-carbon agriculture as a constrained system transition. It develops a process-based cognitive decision pathway linking risk perception, perceived feasibility, and decision confidence, while positioning AI-enabled advisory as cognitive infrastructure rather than a direct transition intention. METHODS Survey data were collected from 398 smallholder rice farmers in Vietnam's Mekong Delta. Partial least squares structural equation modelling (PLS-SEM) was employed to estimate a framework integrating sequential mediation and moderated mediation, with government support and market access modelled as system-level enabling conditions. RESULTS AND CONCLUSIONS AI-enabled advisory significantly enhances risk perception, perceived feasibility, and decision confidence but has no direct effect on transition intention. Perceived feasibility emerges as the critical implementation bottleneck, while decision confidence functions as the final cognitive mechanism translating favourable feasibility assessments into transition intention. In contrast, risk perception increases awareness but does not significantly improve feasibility, revealing a fundamental tension between recognizing climate risks and judging transition to be practically achievable. Government support and market access further strengthen this decision pathway by reinforcing the translation of cognitive readiness into transition intention. SIGNIFICANCE This study makes three principal contributions. First, it reconceptualizes low-carbon agriculture as a constrained system transition rather than a discrete adoption decision. Second, it advances a process-based cognitive decision pathway that explains how farmers progress from implementation feasibility to confidence to act under uncertainty. Third, it repositions AI-enabled advisory as cognitive infrastructure, demonstrating that its effectiveness depends on enabling institutional and market conditions rather than information provision alone. These findings provide a more integrated theoretical framework for understanding low-carbon agricultural transition and offer practical guidance for designing AI-enabled and system-oriented transition policies.