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A limited nuclear war between India and Pakistan could ignite fires large enough to emit more than 5 Tg of soot into the stratosphere. Climate model simulations have shown severe resulting climate perturbations with declines in global mean temperature by 1.8 °C and precipitation by 8%, for at least 5 y. Here we evaluate impacts for the global food system. Six harmonized state-of-the-art crop models show that global caloric production from maize, wheat, rice, and soybean falls by 13 (±1)%, 11 (±8)%, 3 (±5)%, and 17 (±2)% over 5 y. Total single-year losses of 12 (±4)% quadruple the largest observed historical anomaly and exceed impacts caused by historic droughts and volcanic eruptions. Colder temperatures drive losses more than changes in precipitation and solar radiation, leading to strongest impacts in temperate regions poleward of 30±N, including the United States, Europe, and China for 10 to 15 y. Integrated food trade network analyses show that domestic reserves and global trade can largely buffer the production anomaly in the first year. Persistent multiyear losses, however, would constrain domestic food availability and propagate to the Global South, especially to food-insecure countries. By year 5, maize and wheat availability would decrease by 13% globally and by more than 20% in 71 countries with a cumulative population of 1.3 billion people. In view of increasing instability in South Asia, this study shows that a regional conflict using <1% of the worldwide nuclear arsenal could have adverse consequences for global food security unmatched in modern history.

AimsFertilisating crops with zinc (Zn) is considered important to enhance agricultural productivity and combat human Zn deficiencies in sub-Saharan Africa. However, it is unclear on which soils Zn fertilisation can lead to higher yields and increased grain Zn concentrations. This study aimed to find soil properties that predict where soil Zn is limiting maize yields and grain Zn concentrations, and where these respond positively to Zn fertilisation.MethodsZinc omission trials were set up at multiple farm locations in Kenya (n = 5), Zambia (n = 4) and Zimbabwe (n = 10). Grain yields and tissue Zn concentrations were analysed from plots with a full fertiliser treatment as compared to plots where Zn was omitted.ResultsA positive maize yield response to soil Zn fertilisation was found at only two out of nineteen locations, despite soil Zn levels being below suggested critical concentrations at most locations. Soil properties nor plant concentrations were able to explain maize yield response to Zn fertilisation. However, positive responses in Zn uptake and grain Zn concentrations to Zn fertilisation were found at the majority of sites, especially in soils with low pH and organic carbon contents. Labile soil Zn measurements related more with Zn uptake (R2 = 0.35) and grain Zn concentrations (R2 = 0.26) than actual available Zn measurements.ConclusionsWe conclude that soil Zn fertilisation did not increase maize yields, but can increase maize grain Zn concentrations, especially in soils with low pH and organic carbon content. Predicting a yield response to Zn fertilisation based on soil properties remains a challenge.

Within-field variability of crop yield levels has been extensively investigated, but the spatial variability of crop yield responses to agronomic treatments is less understood. On-farm precision experimentation (OFPE) can be a valuable tool for the estimation of in-field variation of optimal input rates and thus improve agronomic decisions. Therefore, the objectives of this study were to investigate the spatial variability of optimal input rates in OFPE and the potential economic benefit of site-specific input management. Mixed geographically weighted regression (GWR) models were used to estimate local yield response functions. The methodology was applied to investigate the spatial variability in corn response to nitrogen and seed rates in four cornfields in Illinois, USA. The results showed that spatial heterogeneity of model parameters was significant in all four fields evaluated. On average, the RMSE of the fitted yield decreased from 1.2 Mg ha−1 in the non-spatial global model to 0.7 Mg ha−1 in the GWR model, and the r-squared increased from 10 to 68%. The average potential gain of using optimized uniform rates of seed and nitrogen was US$ 65.00 ha−1, while the added potential gain of the site-specific application was US$ 58.00 ha−1. The combination of OFPE and GWR proved to be an effective tool for testing precision agriculture’s central hypothesis of whether optimal input application rates display adequate spatial variability to justify the costs of the variable rate technology itself. The reported results encourage more research on response-based input management recommendations instead of the still widespread focus on yield-based algorithms.

How maize yield response to precipitation varies across a large spatial scale is unclear compared with the well-understood temperature response, even though precipitation change is more erratic with greater spatial heterogeneity. This study provides a spatial-explicit quantification of maize yield response to precipitation in the contiguous United States and investigates how precipitation response is altered by natural and human factors using statistical and crop model data. We find the precipitation responses are highly heterogeneous with inverted-U (40.3%) being the leading response type, followed by unresponsive (30.39%), and linear increase (28.6%). The optimal precipitation threshold derived from inverted-U response exhibits considerable spatial variations, which is higher under wetter, hotter, and well-drainage conditions but lower under drier, cooler, and poor-drainage conditions. Irrigation alters precipitation response by making yield either unresponsive to precipitation or having lower optimal thresholds than rainfed conditions. We further find that the observed precipitation responses of maize yield are misrepresented in crop models, with a too high percentage of increase type (59.0% versus 29.6%) and an overestimation in optimal precipitation threshold by ∼90 mm. These two factors explain about 30% and 85% of the inter-model yield overestimation biases under extreme rainfall conditions. Our study highlights the large spatial heterogeneity and the key role of human management in the precipitation responses of maize yield, which need to be better characterized in crop modeling and food security assessment under climate change.

Background and aims Enhanced silicate rock weathering (ERW) on cropland soils can increase crop yield and promote carbon dioxide (CO 2 ) sequestration. Applying silicate rock powder to flooded rice paddies can promote weathering, but the effects of ERW on rice production and CO 2 removal rates in the field remain unclear. Methods We investigated the effects of adding wollastonite (CaSiO 3 ) powder (5 t ha −1 ) to rice paddy plots on soil properties, rice yield, rice grain quality, grain arsenic, grain cadmium, and soil CO 2 sequestration in Liaoning Province, Northeast China. Results Wollastonite application increased soil pH, soil available silicon (Si) content, and Si uptake by rice. Wollastonite application increased grain number by 10% per panicle (15 ± 2), total grain number by 15%, and rice yield by 12% (1.4 ± 0.1 t ha −1 ). After five months of rice growth, soil inorganic carbon (SIC) content in the surface soil increased by 1.20 ± 0.03 t CO 2 ha −1 in wollastonite treatments. We estimated a net profit of $300 (U.S.) ha −1 from yield increase and carbon trade with wollastonite application to this paddy field. Conclusions Wollastonite application to paddy fields in Northeast China promoted rice yield and CO 2 sequestration in the surface soil. This soil CO 2 sequestration triples that from the control soil and is comparable to prior pot trials. Although field trials are needed on the limits to CO 2 sequestration and rice yield increases with wollastonite application, such applications promise to increase soil CO 2 sequestration and profits for a key crop.

This study evaluated the effects of deficit irrigation strategies on wheat production, water productivity, and nitrogen dynamics in a semi-arid region of Pakistan. Field experiments were conducted over three crop seasons (2019–2022) with four irrigation treatments: 100% (I 100 ), 80% (I 80 ), 60% (I 60 ), and 40% (I 40 ) of crop evapotranspiration (ETc) requirements. Canopy cover dynamics exhibited quadratic relationships within days after sowing, attaining maximum covers of 95, 93, 89, and 75% under I 100 , I 80 , I 60 , and I 40 , respectively. Biomass accumulation followed similar quadratic trends, maximizing at 15.1 t ha −1 , 11.0 t ha −1 , 8.5 t ha −1 , and 6.0 t ha −1 for the respective treatments. Irrigation significantly affected biomass yield (BY), grain yield (GY), and nitrogen uptake, with I 100 having higher values than deficit treatments. Relative to I 100 , the BY decreases were 8% (I 80 ), 23% (I 60 ), and 48% (I 40 ), while the GY reductions were 7%, 23%, and 50%, respectively. Grain nitrogen uptake ranged from 123 kg N ha −1 (I 100 ) to 59 kg N ha −1 (I 40 ), mirroring yield trends. Water use efficiency based on biomass (WUE b ) and grain yield (WUE g ) remained consistent across I 100 , I 80 , and I 60 but dropped significantly under I 40 . The yield response factor (K y ) analysis indicated that wheat exhibited moderate sensitivity to water stress, with K y values of 1.25 (I 60 ) and 1.02 (I 80 ). These findings suggest that deficit irrigation at 80% ETc can optimize water conservation while sustaining wheat productivity and resource-use efficiency in semi-arid environments.

Weather variability disrupts food grain production and agricultural sustainability. While existing literature highlights the stationary relationship between weather variables and agricultural outcomes, it often overlooks their bearing on land use changes. This study investigates the dynamic effects of weather variations on crop yields, farmland use and intensity in Odisha, India, using district-level data from 2001-18. By employing a panel autoregressive distributive lag model, we assess long- and short-term relationships between weather parameters and agricultural yields. Results reveal a negative yield elasticity to rainfall deviation, ranging from -0.16 for wheat to -0.48 for green gram in the long term. In the short term, however, elasticity is positive for some pulses (green gram, urad) and oilseeds (groundnuts). Rainfall deviation and maximum temperature adversely affect the rate and intensity of farmland use but enhance crop diversification in both the short and long term.

Global use of reactive nitrogen (N) has increased over the past century to meet growing food and biofuel demand, while contributing to substantial environmental impacts. Addressing continued N management challenges requires anticipating pathways of future N use. Several studies in the scientific literature have projected future N inputs for crop production under a business-as-usual scenario. However, it remains unclear how using yield response functions to characterize a given level of technology and management practices (TMP) will alter the projections when using a consistent dataset. In this study, to project N inputs to 2050, we developed and tested three approaches, namely ‘Same nitrogen use efficiency (NUE)’, ‘Same TMP’, and ‘Improving TMP’. We found the approach that considers diminishing returns in yield response functions (‘Same TMP’) resulted in 268 Tg N yr −1 of N inputs, which was 61 and 48 Tg N yr −1 higher than when keeping NUE at the current level with and without considering changes in crop mix, respectively. If TMP continue to evolve at the pace of past five decades, projected N inputs reduce to 204 Tg N yr −1 , a value that is still 59 Tg N yr −1 higher than the inputs in the baseline year 2006. Overall, our results suggest that assuming a constant NUE may be too optimistic in projecting N inputs, and the full range of projection assumptions need to be carefully explored when investigating future N budgets.

Absence of site-specific nutrient recommendation and high spatial variability of soil fertility are major factors affecting maize response to applied nutrients in Nigeria. In this study, we assessed maize response to applied nutrients and nutrient use efficiency in different management zones (MZs), for designing site-specific nutrient management recommendations for maize in the maize belt of Nigeria. The maize belt in Nigeria was earlier delineated into four MZsMZs (MZ1 to MZ4) based on soil properties. In the current study, data from two different trials, nutrient omission trials ( N = 293) and fertilizer response trial ( N = 705), conducted in the years 2015–2017, were extracted for MZ1 to MZ3; to analyze maize yield responses to application of N, P and K, and secondary and micro-nutrients. Maize yield response to K application was only positive in MZ1. Responses to N and P application were positive for all MZs. However, the magnitude of maize response to P varied between the MZs, indicating a differentiation in the degree to which P is limiting maize production in the study area. Average nitrogen requirement was higher for MZ3 (138 kg ha −1 ), than for MZ2 and MZ1 (121 and 83 kg ha −1 , respectively). Average P requirement was higher for MZ3 (45 kg ha −1 ) than for the other zones. Potassium requirement was 26% and 28% higher in MZ2 and MZ3 compared with MZ1 (∼15 kg ha −1 ). The use of the specific nutrient rates for the MZs may reduce risks and uncertainties in crop production. The delineated MZs of the maize belt of Nigeria that incorporates spatial variability in soil fertility conditions are useful for nutrient management for larger areas.