Improving apparent N recovery efficiency (REN) in rice systems is essential for sustaining yields while reducing environmental impacts. This study integrates a global meta-analysis with crop-modelling to assess N management strategies. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we analyzed 25,754 observations from 45 studies (2015–2025) for grain yield, N uptake, and REN across N application rates and environmental conditions. Additionally, 38 model calibration experiments (2,400 observations) and 91 model validation experiments (3,870 observations) were included in the crop modelling assessment.Nitrogen application increased rice yield by ∼85% relative to the control and enhanced N uptake, with maximum yields achieved at 300–450 kg N ha-1. However, REN declined to higher application rates, indicating diminishing returns and a greater risk of N losses. Environmental conditions strongly modulated crop responses, with optimal performance observed at soil pH 5.5–6.0, temperatures of 21–26 °C, and moderate rainfall. In parallel, widely used crop simulation models, including CERES-Rice, ORYZA, and WHCNS, demonstrated strong performance in simulating grain yield and N uptake (coefficient of determination; R2 > 0.85), supporting their applicability as decision-support tools for N management. Variability in model performance was linked to uncertainties in input data quality, parameterization, and the representation of key biophysical processes within each model. The publication bias analysis demonstrated that the estimated effect sizes for both grain yield and N uptake has not substantially influenced the meta-analysis results, and has supported the reliability of the meta-analysis findings.Overall, integrating meta-analysis and crop modelling highlights the trade-off between maximizing yield and maintaining high REN, underscoring the need for optimized, site-specific N management strategies. Crop models offer a valuable platform for pre-assessing management scenarios, thereby reducing reliance on resource intensive field experiments. These findings provide important insights into improving fertilizer recommendations and guiding sustainable rice production across diverse agro-ecological conditions.
Lignocellulosic straw from cereal crops such as wheat, rice, maize, and barley constitute one of the most abundant yet underutilized agricultural residues globally, offering a renewable, low-cost feedstock for ruminants nutrition. However, its high lignin content limits microbial access to cellulose and hemicellulose, while the limited capacity of the rumen microbiome to degrade lignin contributes to poor feed efficiency, reduced animal productivity, methane emissions, and crop-residue burning. This review explores the potential of genetic engineering to develop genetically modified ruminants capable of producing synthetic lignin-degrading enzymes in saliva, thereby facilitating lignin breakdown in the oral cavity and enhancing straw utilization. The biochemical challenges to lignocellulose degradation, existing lignin-degradation mechanisms in ruminants, and proof-of-concept evidence from transgenic models are examined, with emphasis on their implications for feed efficiency, livestock productivity, and environmental sustainability. The available evidence indicates that expressing lignin-degrading enzymes in ruminants could improve the utilization of crop residues by making cellulose and hemicellulose more accessible, thereby reducing feed costs, improving animal productivity and promote environmental sustainability. With proper safety evaluation and suitable feeding and management practices, this approach could offer a promising strategy for better and more sustainable utilization of cereal crop residues in ruminants.
There is an urgent need to identify dairy farms that are resilient to climate change and have high resource-use efficiency. This study assessed the resilience (standardised milk production and gross operating surplus) and efficiency (nitrogen-use efficiency and value creation) of the agroecological and experimental dairy farm OasYs compared to 22 low-input commercial dairy farms. The mean, slope and mean absolute residuals of a mixed linear regression of each indicator from 2017-2023 were used to calculate the indicator’s score for each farm. We ranked and clustered the farms by their scores and compared their farming practices. OasYs ranked among the highest for milk production (3rd), gross operating surplus (4th) and nitrogen-use efficiency (3rd) but lowest for value creation (14th). OasYs belonged to the most resilient and efficient cluster of farms even though it experienced more adverse weather conditions than the other 9 farms in the cluster with 16 summer days/yr whose maximum temperature exceeded 30°C against 6 days/yr on average for the other 9 farms, and lower mean effective rainfall (-186 mm) than the other 9 farms (6 mm). This was allowed by its (i) highly diversified land use with multiple forage crops (e.g. fodder beet) covering 26.9% of the area used to feed livestock against 1.2% on average in the other 9 farms, which helped buffer the variability in weather and extended the duration of the grazing season, and (ii) a more diversified herd thanks three unique practices not found in other farms i.e. three-way crossbreeding and two calving periods that helped balance fertility, health and milk production, and extended lactations that shortened each cow’s non-productive periods. The results provide empirical evidence that agroecological dairy-cattle farming can achieve both resilience and efficiency in the context of climate change.
Matching nitrogen (N) supply with available soil water is a major challenge in dryland conservation cropping systems, where rainfall infiltration and soil hydraulic conductivity control the depth and availability of surface-applied fertilizer N. However, the extent to which fallow rainfall can redistribute surface-applied fertilizer N into deeper soil layers under residue-retained dryland systems, and how this process is influenced by residue retention and subsoil hydraulic continuity, remain poorly understood. This study examined how residue addition, subsoil hydraulic conductivity and fertilizer application timing regulate the redistribution and fate of surface-applied N during fallow and its recovery by a subsequent wheat crop. A controlled lysimeter experiment using 15N-labelled urea evaluated residue management (residue vs. no-residue), subsoil hydraulic conductivity (unrestricted vs. restricted), and fertilizer application timing (early, mid, and late fallow) under simulated fallow rainfall. Fertilizer-N movement followed rainfall infiltration through mass flow, but residue addition and subsoil hydraulic discontinuity modified its depth and retention. Residue enhanced profile water storage by reducing evaporative loss yet promoted near-surface fertilizer-N retention and delayed losses, while the hydraulically restricted profile limited infiltration depth. Mid-fallow application maintained recovery comparable to early application while producing relatively low apparent loss, reflecting a favourable balance between N mobility and retention. Under early-fallow application, 19–46% of applied fertilizer N was redistributed below 20 cm by the end of fallow, with crop recovery comparable to mid-fallow under similar residue and hydraulic conditions. In contrast, late application after simulated fallow rainfall ceased resulted in minimal redistribution, the lowest crop recovery (<5%) and the highest apparent N loss (35–39%). The findings indicate that treating fertilizer application timing as a hydrological rather than calendar decision can improve fertilizer N recovery and reduce loss risk in dryland conservation agriculture.
Agricultural systems face the increasing challenge of balancing societal objectives, including food production, energy supply, and environmental and biodiversity conservation. Perennial biomass crops such as Miscanthus × giganteus (Miscanthus) and Silphium perfoliatum (Silphium) could contribute to the achievement of ecological and biodiversity objectives, however their implementation in Germany is still scarce. This study therefore assesses whether Miscanthus and Silphium can contribute to improved environmental performance in Baden-Württemberg (BW), Germany, and quantifies associated economic trade-offs. We apply PALUDEVAL, a regional land-use optimization model operating at NUTS-3 level, which integrates economic decision-making with an extensive ecological assessment including indicators for soil erosion, nitrate leaching, particulate phosphorous (PP) losses, greenhouse gas (GHG) emissions and biodiversity. In scenarios requiring a 20% reduction in erosion, nitrate leaching, PP and GHG emissions, and a 20% increase in BW’s average biodiversity – potential – index (BPI), results show that Silphium provides the strongest biodiversity improvements, while Miscanthus yields the highest reductions in nitrate leaching and GHG emissions. However, across most scenarios, flowering fallow (FF) emerges as the dominant, more cost-effective measure, reaching up to ∼34,100 ha compared to ∼22,700 ha for Miscanthus, ∼19,500 for Silphium in the Multi-Target scenario, thereby limiting the expansion of Miscanthus and Silphium under current economic conditions. Reducing risk-related cost premiums by 50% more than doubles the Miscanthus area to ∼51,600 ha in this scenario, while Silphium increases moderately to ∼22,600 ha. Compliance with environmental targets leads to notable economic impacts: Regional gross margins decline by up to 26% in the Multi-Target scenario, while public expenditure increases substantially. Spatial allocation patterns suggest that Miscanthus is primarily suited to northern BW, whereas Silphium is favored in regions with established biogas capacity. Overall, PALUDEVAL highlights the conditional potential of Miscanthus and Silphium to support environmental policy goals when appropriately targeted and incentivized.
Sustainable rice production depends on soil health, the foundation of environmental quality and ecosystem services. Conventional tillage without straw return degrades black soils across Northeast China, yet few studies have simultaneously evaluated soil health, soil ecosystem multifunctionality (soil EMF), ecosystem services, and rice grain yield (RGY) under contrasting long-term management regimes. We examined five treatments: conventional tillage without straw return (CT, control), conventional tillage with straw return (CTS), autumn tillage (autumn ploughing plus rotary tillage) with straw return (AT(P+R)S), autumn rotary tillage with straw return (AT(R)S), and autumn rotation tillage (one-year ploughing per one-year rotary tillage) with straw return (AT(P/R)S). Compared with CT, AT(R)S increased the weighted soil health index–minimum data set by 4.24 % and induced positive ΔZ shifts in soil EMF, carbon sequestration, and nutrient recycling of 6.33, 2.12, and 4.38, respectively, accompanied by a 17.96 % increase in RGY driven by improved biological and chemical indicators. Random forest analysis suggested that available phosphorus (AvP), soil organic matter (SOM), and acid phosphatase (ACP) were potential predictors of soil health; AvP, soil urease, and ACP may contribute to soil EMF; and AvP and ACP were important predictors of RGY. Structural equation modeling showed that AT(R)S was positively associated with yield primarily by improving soil health via a biological indicator. AT(R)S emerged as the best-performing treatment for enhancing soil health, soil EMF, and ecosystem services while maintaining high yields, offering new insights for promoting sustainable rice production and soil management in black paddy soils.
Grazing regulates nutrient cycling in grasslands primarily through livestock behaviors of intaking, trampling, and excrement. However, how litter and dung decomposition are associated with these behaviors under different grazing management remain unexplored. We performed a two-year field experiment examining the effects of two grazing systems (continuous grazing, CG; rotational grazing, RG) and two intensities (8 and 16 sheep ha-1) on livestock behaviors and litter/dung decomposition in an alpine meadow on the Qinghai-Xizang Plateau. RG of 8 sheep ha-1 increased daily dry matter intake, live weight, dung mass, and urine mass, but decreased walking steps compared to CG. Average wind speed, initial dung phosphorus, walking steps, and initial litter nitrogen explained the most variance in litter dry matter (DM) decomposition. For dung DM decomposition, average relative humidity, initial dung nitrogen, and initial litter nitrogen were the top factor. Livestock behavior explained the largest proportion of variance in litter (42.5–60.5%) and dung (46.1–54.2%) decomposition, exceeding that of climate, plant traits, and soil properties. Grazing intensity negatively impacted litter decomposition but positively impacted dung decomposition, while climate and grazing system negatively affected both. These findings demonstrate that RG at moderate grazing intensity optimizes the distribution of livestock behaviors, which are associated with enhanced litter and dung decomposition and nutrient cycling in alpine meadow ecosystems.
Improper nitrogen (N) management and unstable field efficacy of rhizobial inoculants are core constraints limiting symbiotic nitrogen fixation, rhizosphere soil function and N use efficiency of adzuki bean (Vigna angularis L.) in global temperate agricultural regions. A 4-year consecutive fixed-site field experiment (2021–2024) was performed on the northern China dominant adzuki bean cultivar ‘Baohong 947’ with a two-factor randomized block design. Three nitrogen application rates (37.5, 75, 150 kg N ha-1) and three rhizobial inoculation treatments were set to explore the synergistic regulation mechanism of nitrogen-rhizobium interaction and screen the optimal agronomic management strategy. Results showed that medium N (N2) combined with R1 (N2R1) achieved optimal comprehensive performance: it significantly enhanced nodulation, nitrogenase activity, rhizosphere nutrient-cycling enzyme activities and photosynthetic efficiency (all differences significant at P<0.05). The 4-year average grain yield reached 760.51 kg ha-1, representing a 10.2%–31.8% increase compared with other treatments; grain nutritional quality was also improved, with soluble protein and total starch contents increasing by 8.0% and 7.6% respectively relative to conventional high-N management. Path least squares path modeling confirmed that nodulation nitrogen fixation and reproductive organ N allocation act as two independent yet synergistic core mediators driving yield formation (path coefficients 0.556***–0.849***). In conclusion, medium N paired with concentrated Sinorhizobium inoculant synergistically improves adzuki bean productivity via system-level regulation of the rhizosphere-nodule-plant continuum. This strategy reduces synthetic N input by 30% relative to local conventional high-N management, and provides a robust technical support for sustainable intensification of temperate legume production systems worldwide.
Sustainable intensification of rainfed agriculture in semi-arid regions requires strategies that enhance productivity while mitigating environmental footprints. A two-year field trial was performed on the Loess Plateau to evaluate the synergistic effects of film mulching (white, black, and no mulching) and nitrogen management (conventional urea at 270 and 200 kg N ha-1; controlled-release urea at 170 and 140 kg N ha-1) on forage maize (Zea may L.) productivity, resource use efficiency, energy performance, and multi-scale carbon (C), nitrogen (N), and water footprints. White film mulching notably raised the thermal time use efficiency (TUE) by 12.5% in 2023 compared to no mulching. Notably, film mulching greatly increased dry matter production (DMP) by 7.0–24.1%, precipitation productivity (PPDM) by 7.4–24.1% and partial nitrogen balance (PNB) by 9.8–21.1% without increasing seasonal water loss. White film yielded the greatest DMP and energy output (EO), and net energy (NE), while consistently reduced C, N, and water footprints across both dry matter- and nutrient-based functional units. The controlled-release urea strategy at 140 kg N ha-1 maintained yield with 48% less nitrogen input while simultaneously reducing C, N, and water footprints across dry matter- and nutrient-based functional units, and significantly enhancing PNB, NE, energy ratio (ER), energy use efficiency (EUE), and energy productivity. Consequently, white film mulching combined with reduced-dose controlled-release urea (140 kg N ha-1) achieved the highest sustainability evaluation index (0.90), demonstrating an optimal balance between productivity and environmental performance. This integrated strategy represents an effective practice for sustainable intensification of rainfed forage maize production in semi-arid agroecosystems.
The Japanese apple industry is shifting from traditional low-density to modern high-density orchards to address labor shortages and improve productivity. However, the environmental consequences of maintaining premium fruit quality during this transition remain unquantified. This study applied Life Cycle Assessment (LCA) and energy analysis to three orchard systems in Aomori and Iwate prefectures: a traditional flat open-center system (FO-A), a high-density slender spindle system (SS-A), and a free spindle system (FS-I). Functional units were 1 kg of apples and 1 ha of orchard land. Results showed distinct environmental profiles among the systems. SS-A recorded the lowest Global Warming Potential, 26% lower than FO-A and 13% lower than FS-I, and the lowest Fossil Resource Scarcity and aquatic Ecotoxicity under both functional units. FS-I achieved the lowest Marine Eutrophication but had 2.4 times higher Terrestrial Ecotoxicity and the highest fuel consumption. Despite being the least productive, FO-A had the lowest Terrestrial and Human Toxicity under both functional units due to its temporary wooden stakes, unlike the permanent steel used in high-density systems. A European ultra-high-density tall spindle system achieved 331% higher labor productivity and 34% lower specific energy than the Japanese slender spindle system. However, energy intensity per ha was 15% higher, indicating the trade-off between product-level efficiency and land-use intensity. Mitigating environmental impacts requires training-system-specific nutrient management, integrated pest management, and alternative materials for trellis systems. Diversifying market channels beyond the high-value gift market would further support sustainable production and balance cultural heritage with environmental and economic goals.
Cereal/legume intercropping can effectively reduce nitrogen (N) fertilizer input and increase nutrient uptake and farmland productivity in intensive agroecosystems. However, it is unclear how intercropping regulates rhizosphere soil N status and microbiome function to increase crop N uptake. To address this knowledge gap, we performed a two-year field experiment to investigate how proso millet/mung bean intercropping and four N fertilizer rates affect soil N turnover and microbial functionality in the proso millet rhizosphere. Intercropping significantly boosted proso millet grain yield (13.23%) by regulating root N metabolism and N uptake (27.28%) over the two-year study. This positive growth dynamics was related to the promotion of N turnover by intercropping, which was manifested by the elevated NO2−−N and NO3−−N contents in the rhizosphere, reduced net consumption of soil NH4+−N and increased net consumption of NO3−−N. Compared with other N fertilizer management systems, the N2 (120 kg N ha−1) treatment under the intercropping system yielded greater advantages for proso millet production. Metagenomics indicated that intercropping coupled with reduced N application upregulated the abundances of NRT2, narK, nrtP, nasA, nrtB, nasE, cynB, and microorganisms such as Nocardioides in nitrate assimilation N transport, compared with sole cropping with N3. The partial least squares path and random forest models further showed that the promoting effect of intercropping with N fertilizer on root N metabolism was positively correlated with the functional genes and microbial species participating in nitrate assimilation N transport. Root physiological metabolism positively influences plant N accumulation and grain yield by increasing soil N uptake. Our results provide new insights into the theory underlying the reduction in N fertilizer application through intercropping, to advance sustainable agricultural practices.
Decades of intensive agriculture have driven widespread degradation of black soils in Northeast China, underscoring an urgent need to protect this critical resource to safeguard food security. However, the impact of varying straw management and tillage practices on soil quality and crop yield remains debated. Additionally, existing studies have largely focused on topsoil, while the subsoil quality and its contribution to crop productivity have been largely overlooked. Therefore, a two-year field experiment was carried out with a factorial combination of two straw regimes (straw incorporation vs. straw removal) and four tillage methods, including no tillage (NT), rotary tillage (RT), plow tillage (PT), and strip deep tillage (SDT). Our results showed that straw incorporation significantly raised both the soil quality index and maize yield. Under straw removal, the influence of different tillage practices on soil physicochemical properties was relatively limited; for example, SOC, DOC, MBC, and NH4+ showed no significant differences in either the topsoil or subsoil, and thus their effects on yield were not significant. With straw incorporation, both SDT and PT increased maize yield and aboveground biomass. Notably, SDT enhanced maize yield by 2.5-8.3% in the second year compared with NT, RT, and PT. This improvement was attributed to a 29.1% rise in subsoil organic carbon and a 32.6% increase in subsoil nutrients (mineral nitrogen and available phosphorus), favoring yield-related traits such as kernels per ear and 100-kernel weight. Maize yield significantly increased with improving subsoil quality index (R2 = 0.49, p < 0.01). These findings highlight the importance of subsoil improvement for yield enhancement and indicate that strip deep tillage combined with straw incorporation is a promising strategy to support sustainable agricultural production in degraded and intensively cultivated regions.
Spatial mapping of residual soil nitrate (NO3-) after harvest is essential for targeted nitrogen (N) management and reducing leaching risk. Specifically, predicting NO3- in spatially diversified cropping systems is challenging because soil properties, crop types, and fertilization rates vary between and adjacent crops. This study tested a stacked ensemble machine learning model to predict crop-scale post-harvest NO3- of the topsoil (0-30 cm) in a diversified patch cropping system in Brandenburg, Germany. Five years of data (2020–2024) from 30 patches and 9 different crops were used. The framework integrated data on volumetric soil moisture, precipitation, Sentinel-2 vegetation indices, and N management variables aggregated over temporal windows from 1 to 90 days before harvest. The stacked ensemble of Random Forest and XGBoost achieved the highest performance for the 90-day window (RMSE = 11.5 NO3-N kg ha-1, MAE = 9.0 NO3-N kg ha-1, R2 = 0.61), outperforming both base learners individually. The feature importance analysis (SHAP) identified cumulative precipitation and NDVI as the dominant predictors. Predicted responses showed increased residual NO3- under low precipitation (<150 mm) and high N fertilizer rates (>150 kg N ha-1), whereas NO3- declined with high NDVI averages above 0.55. The five-year dataset captured both a persistent texture-driven spatial gradient in residual NO3- and a drought-related reversal in 2022, when sandy low-yielding patches exhibited unusually high post-harvest NO3- under exceptionally dry pre-harvest conditions. These results demonstrate that a multi-source machine learning approach can identify relative post-harvest NO3- hotspots in spatially heterogeneous diversified cropping systems with contrasting crop phenologies and management conditions. The framework provides a locally calibratable template for targeted N monitoring in comparable climates, spatially heterogeneous diversified cropping systems with the availability of soil, local weather, crop, and management data.
Traditional prataculture management models are increasingly inadequate under the dual pressures of global climate change and land degradation, limiting the sustainable development of grassland resources and the realization of China's "big food" concept. Grounded in systems holism and socio-ecological coupling theory, we propose an intelligent pratacultural management framework integrating artificial intelligence (AI), Internet of Things (IoT), remote sensing, and multi-source data fusion. The framework was developed through a structured synthesis of peer-reviewed literature on socio-ecological systems, digital agriculture, and grassland management, systematically compared against existing frameworks to identify structural and functional gaps. The framework organizes management around three factor groups (abiotic, biological, and social), three interfaces (plant–land, pasture–livestock, and pasture/animal–market), and four production layers (pre-plant, plant, animal, and post-biological), operationalized through five sequential stages: data collection, modeling, analysis, decision-making, and implementation. The framework enables real-time ecosystem monitoring, dynamic simulation, optimized resource allocation, and predictive early-warning across the full forage–livestock–market chain. Key challenges to AI adoption in prataculture, including data quality, algorithm adaptability, talent shortages, and economic barriers, are identified, alongside future development opportunities. Unlike general socio-ecological system frameworks or crop-focused smart-farming models, this framework provides a grassland-specific, hierarchically operationalized system with real-time dynamic coupling capabilities, offering a practical blueprint for the digital and sustainable transformation of grassland management, with China's grassland systems serving as the primary empirical basis for framework development and illustrated application, while the underlying architecture is intended to be transferable, with region-specific calibration, to other extensive pastoral systems.
Biochar application to soil can increase carbon sequestration; however, the long-term mechanisms through which different feedstocks and nitrogen (N) fertilization jointly regulate the distribution and stability of soil organic carbon components within aggregates remain unclear. We used a 12-year paddy field experiment in Yuhang County, Zhejiang Province, China, to compare rice straw and bamboo biochars with and without N fertilization, evaluating black carbon (BC, the refractory polycyclic aromatic carbon fraction derived from biochar) and dissolved black carbon (DBC) content, and aromaticity across aggregate size classes. Rice straw biochar preferentially accumulated in microaggregates (0.053−0.25 mm), increasing BC amount by 181−189%, while bamboo biochar predominantly enriched macroaggregates (0.25−2 mm), enhancing BC storage by 683−729% and significantly increasing BC aromatic condensation (B6CA/B5CABC increased by 19−22%) relative to the control. Although total DBC concentrations did not change significantly, biochar application enriched the condensed aromatic structures of DBC. Annual N fertilization reduced the aromatic condensation of BC and DBC, with stronger effects under rice straw biochar amendment. Structural equation modeling suggested that black carbon (BC) accumulation closely covaried with the bulk soil organic carbon (SOC) pool, highlighting the importance of its physical integration and physical protection within the broader organic matrix. Conversely, DBC retention was governed by the interaction of dissolved organic matter (DOM) components and metal oxides. Our results emphasize that biochar characteristics determine its geochemical fate and stabilization pathways within soil aggregates, and that physical preservation of highly aromatic biochar within aggregates is crucial for achieving long-term carbon sequestration. We reveal that N fertilization can compromise aromatic stability, exposing a critical trade-off between maximizing crop productivity and maintaining long-term soil carbon sinks in biochar-amended paddy systems.
Diversified cropping practices can improve land use efficiency and crop productivity; however, their effects on year-to-year yield stability are inconsistent, and the mechanisms regulating yield stability remain unclear. Therefore, a 4-year field study was conducted to evaluate the productivity, yield stability, and soil carbon pool stability of wheat–annual forage crop rotation systems versus wheat monoculture. Six cropping practices were tested: fallow after spring wheat (W-F), multiple cropping of forage oats after spring wheat (W-O), multiple cropping of forage oats mixed with common vetch after spring wheat (W-O/C), multiple cropping of common vetch after spring wheat (W-C), multiple cropping of forage oats mixed with hairy vetch after spring wheat (W-O/H), and multiple cropping of hairy vetch after spring wheat (W-H). Results revealed that the W-H treatment increased wheat grain yield (GY) and stability by 3.9% and 36.8%, respectively, compared with the W-F treatment. The W-O/H treatment achieved the highest forage productivity, with dry matter yield (DMY) and crude protein yield (CPY) reaching 8969.1 and 1193.7 kg ha-1, respectively, and increased the system wheat equivalent yield (WEY), DMY, and CPY by 88.6%, 76.8%, and 89.2%, respectively, relative to the W-F treatment. Multiple cropping also enhanced temporal stability, reducing the coefficient of variation (CV) of system productivity by 45.0%, 47.2%, and 40.7% under W-O/C, W-O/H, and W-H treatments, respectively, compared with W-F treatment. Legume-based and mixed forage systems increased soil organic carbon (SOC), readily oxidizable carbon (ROC), and carbon pool management index (CPMI). Notably, CPMI was positively correlated with WEY, DMY, and CPY, but negatively with their CV, indicating improved productivity and stability. Mantel test and random forest analyses further showed that forage productivity, its stability, and CPMI were key drivers of system productivity stability. The W-H treatment was the most suitable option for stabilizing wheat GY, whereas the W-O/H treatment was the best choice for maximizing overall system productivity and sustainability index (2.03). This finding provided a scientific basis for optimizing wheat-forage systems in irrigated regions and similar agroecosystems worldwide.
Fusarium crown rot (FCR) has been a serious threat to cereal production in many areas worldwide. Common wheat and barley suffer similar levels of yield loss from this disease, with Fusarium pseudograminearum as the predominant pathogen causing FCR in both crop species. Compared to wheat, barley shows more severe symptoms of stem-base browning and accumulates more fungal biomass under FCR infection. However, unlike wheat, whiteheads are not a common occurrence in FCR-infected barley crops, raising the question of which physiological traits drive yield loss in barley under FCR infection. To address this, 16 barley genotypes were assessed across five field sites over two cropping seasons in Australia. Data on grain yield and yield components, including thousand kernel weight (TKW), kernel number per spike (KNPS), and fertile tiller number (FTN) from both non-inoculated and inoculated treatments were collected and analysed. Significant grain yield loss in the presence of FCR was detected in each of these field trials. Invariably, yield loss caused by FCR infection was attributable to a reduction in the number of fertile tillers, with no significant difference in TKW and KNPS detected in any of the trials. These findings offer new targets for minimising FCR damage through breeding and crop management efforts.
Seasonal forage scarcity and the resulting grassland-livestock imbalance severely constrain the sustainable development of animal husbandry on the Qinghai-Tibetan Plateau (QTP). In response, we propose a conceptual ‘Type-Temporal-Spatial’ coupling framework to systematically optimize forage supply structures, operationalized through two engineering approaches: the “Crop-Livestock” Engineering (temporal coupling) and the “Agriculture-Grassland-Assisted Livestock Engineering” Engineering (spatial coupling). To contextualize this framework, we construct a heuristic ‘ideal-type’ sequence tracing the evolutionary trajectory of “Pasture Wisdom”—from traditional nomadism (Wisdom A) to rotational grazing (Wisdom B), system coupling (Wisdom C), digital intelligent ranching (Wisdom D), and periodic intelligent intervention (Wisdom E). We clarify that this sequence serves as a conceptual framework for analyzing management paradigm shifts, rather than a deterministic historical trajectory. Our analysis reveals that while System Coupling (Wisdom C) effectively balances regional supply and demand, it relies heavily on external material inputs, may lead to considerable energy dissipation. In contrast, the Intelligent Ranch mode (Wisdom D) offers a pathway to “internal integration” through precise digital management. However, due to ecological, infrastructural, and socio-technical lags, a complete transition to Wisdom D is not immediately feasible; thus, the system will remain in a long-term transitional phase characterized by the symbiosis of Wisdom C and D. Finally, we propose a vision of “Periodic Intelligent Intervention” (Wisdom E). In this paradigm, technology acts as a temporary external stabilizer, intervening cyclically to reduce ecosystem disorder or degradation risk when illustrative degradation thresholds (e.g., NDVI drops) are breached, and withdrawing to allow natural resilience to govern upon recovery. It should be emphasized that this process does not represent a universal, linear path of development. Different pastoral areas may remain at the same stage for extended periods or may combine multiple stages simultaneously. This framework provides a conceptual basis for achieving a low-dissipation, efficient, and sustainable pastoral ecosystem on the QTP.
Weather index insurance (WII) has been proposed as an effective risk transfer measure against extreme weather events affecting smallholder farmers in Sub-Saharan Africa. Despite the potential of this insurance type to stabilize farm income under extreme weather conditions, subscription rates by farmers have been very low. One of the reasons for the low subscription is basis risk, which relates to mismatches between payouts and losses, leading to misunderstanding and distrust on the part of farmers. Using an integrated bio-economic modelling approach for Northern Ghana, we quantify basis risk arising from three misalignments: spatial mismatch in rainfall inputs, temporal mismatch due to heterogeneity in planting dates, and biophysical mismatch linked to soil water-holding capacity (proxied by soil depth). Spatial basis risk was assessed by adjusting the daily precipitation data decreasing the values by up to the 10th percentile and increasing them up to the 90th percentile compared to the reference. For temporal basis risk planting dates were varied by delaying them by 7 to 21 days and increasing them by 14 days. To assess the sensitivity of index performance to soil variability, reference soil depths were increased and decreased by 30 cm. We evaluate insurance performance on maize crops using household outcomes (gross margin and assets). Results show that misalignment can substantially weaken risk protection, with the largest effects in product basis risk where soil water storage differs from insurance contract reference assumptions. Consistent with prior work, our results reinforce that WII contracts should align with local agronomic and environmental conditions; we add incremental evidence by quantifying how residual misalignment, especially soil-depth heterogeneity and planting-date shifts can weaken protection in shock years.
Drought is a major hydroclimatic hazard with significant impacts on agriculture. However, drought is very unpredictable and the tandem between drought conditions and crop yield is still poorly understood. Particularly, in a humid region such as the southeastern coastal plain of the United States (US) where rainfed agriculture is dominant, a better understanding of crop responses to drought conditions is essential for anticipating yield fluctuations. Hence, this study addresses drought implications on crops by proposing a modeling framework for an early-season prediction of corn, cotton, peanuts, and soybeans yields in a region of the southeastern US encompassing the states of Georgia, North Carolina, and South Carolina. The framework uses county-level standardized precipitation and evapotranspiration index (SPEI) and detrended crop yields time series. Explicitly, a z-score transformation was used to develop a classification scheme that distinguishes gradual levels of crop yields. Supervised Machine Learning models including the random forests (RF) and the k-nearest neighbors (kNN) were distinctly trained and tested for yield prediction based on SPEI. Results showed a disparity of performance depending on the model, the crop, and the prediction scales. For instance, the accuracy of predicting crop yields at regional and state scales was up to 74% with RF and 77% with kNN models. This slight outperformance of the kNN models was consistent at all scales for each of the four crops, but the kNN structure is relatively complex compared to RF. However, macro-averaged F1score values sustained variable predictability among yield levels. Nevertheless, both RF and kNN models showed reasonable performances for predicting three or two levels of yield. Interestingly, the models' inputs are mid-season SPEI values, allowing yield prediction months ahead of the harvesting periods. Hence, the framework's prediction can be useful for anticipating crop yield anomalies at the regional scale.
