How do various maize crop models vary in their responses to climate change factors?

Long-term trajectory of ozone impact on maize and soybean yields in the United States: A 40-year spatial-temporal analysis

Understanding the long-term change trends of ozone-induced yield losses is crucial for formulating strategies to alleviate ozone damaging effects, aiming towards achieving the Zero Hunger Sustainable Development Goal. Despite a wealth of experimental research indicating that ozone's influence on agricultural production exhibits marked fluctuations and differs significantly across various geographical locations, previous studies using global statistical models often failed to capture this spatial-temporal variability, leading to uncertainties in ozone impact estimation. To address this issue, we conducted a comprehensive assessment of the spatial-temporal variability of ozone impacts on maize and soybean yields in the United States (1981-2021) using a geographically and temporally weighted regression (GTWR) model. Our results revealed that over the past four decades, ozone pollution has led to average yield losses of -3.5% for maize and -6.1% for soybean, translating into an annual economic loss of approximately $2.6 billion. Interestingly, despite an overall downward trend in ozone impacts on crop yields following the implementation of stringent ozone emission control measures in 1997, our study identified distinct peaks of abnormally high yield reduction rates in drought years. Significant spatial heterogeneity was detected in ozone impacts across the study area, with ozone damage hotspots located in the Southeast Region and the Mississippi River Basin for maize and soybean, respectively. Furthermore, we discovered that hydrothermal factors modulate crop responses to ozone, with maize showing an inverted U-shaped yield loss trend with temperature increases, while soybean demonstrated an upward trend. Both crops experienced amplified ozone-induced yield losses with rising precipitation. Overall, our study highlights the necessity of incorporating spatiotemporal variability into assessments of crop yield losses attributable to ozone pollution. The insights garnered from our findings can contribute to the formulation of region-specific pollutant emission policies, based on the distinct profiles of ozone-induced agricultural damage across different regions.

Integrated crop and livestock systems increase both climate change adaptation and mitigation capacities

Integrated crop-livestock systems (ICLS) are proposed as key solutions to the various challenges posed to present-day agriculture which must guarantee high and stable yields while minimizing its impacts on the environment. Yet the complex relationships between crops, grasslands and animals on which they rely demand careful and precise management. In this study, from a 18-year ICLS field experiment in Brazil, that consists in annual no-till soybean-pastures grazed by beef cattle, we investigated the impacts of contrasted pastures grazing intensities (defined by sward heights of 10, 20, 30 and 40 cm, plus an ungrazed treatment) on the agroecosystem productivity and soil organic carbon (SOC) under both historical and future (2040-2070, RCP8.5) climatic conditions. We used an innovative methodology to model the ICLS with the STICS soil-crop model, which was validated with field observations. Results showed that the total system production increased along with grazing intensity because of higher stocking rates and subsequent live weight gains. Moderate and light grazing intensities (30 and 40 cm sward heights) resulted in the largest increase in SOC over the 18-year period, with all ICLS treatments leading to greater SOC contents than the ungrazed treatment. When facing climate change under future conditions, all treatments increased in productivity due to the CO2 fertilization effect and the increases in organic amendments that result from the larger stocking rate allowed by the increased pasture carrying capacity. Moderate grazing resulted in the most significant enhancements in productivity and SOC levels. These improvements were accompanied by increased resistance to both moderate and extreme climatic events, benefiting herbage production and live weight gain. Globally, our results show that adding a trophic level (i.e. herbivores) into cropping systems, provided that their carrying capacities are respected, proved to increase their ability to withstand climate change and to contribute to its mitigation.

The influence of climatic and environmental variables on sunflower planting season suitability in Tanzania

Crop survival and growth requires identification of correlations between appropriate suitable planting season and relevant climatic and environmental characteristics. Climatic and environmental conditions may cause water and heat stress at critical stages of crop development and thus affecting planting suitability. Consequently, this may affect crop yield and productivity. This study assesses the influence of climate and environmental variables on rain-fed sunflower planting season suitability in Tanzania. Data on rainfall, temperature, slope, elevation, soil and land use/or cover were accessed from publicly available sources using Google Earth Engine. This is a cloud-based geospatial computing platform for remote sensed datasets. Tanzania sunflower production calendar of 2022 was adopted to mark the start and end limits of planting across the country. The default climate and environmental parameters from FAO database were used. In addition, Pearson correlation was used to evaluate the relationship between rainfall, temperature over Normalized Difference Vegetation Index (NDVI) from 2000 to 2020 at five-year interval for January-April and June-September, for high and poor suitability season. The results showed that planting suitability of sunflower in Tanzania is driven more by rainfall than temperature. It was revealed that intra-annual planting suitability increases gradually from short to long- rain season and diminishes towards dry season of the year. January-April planting season window showing highest suitability (41.65%), whereas June-September indicating lowest suitability (0.05%). Though, not statistically significant, rainfall and NDVI were positively correlated with r = 0.65 and 0.75 whereas negative correlation existed between temperature and NDVI with r = -- 0.6 and - 0.77. We recommend sunflower subsector interventions that consider appropriate intra-regional and seasonal diversity as an important adaptive mechanism to ensure high sunflower yields.

Performance evaluation of various techniques in estimating precipitation record of a sparsely gauged mountainous watershed

Comprehensive precipitation data is essential for hydrological, agricultural, and climatological studies. Yet, gaps and sparse rain gauge distribution pose challenges, requiring imputation algorithms to fill data gaps. The aim of this research is to evaluate the performance of several approaches for estimating incomplete precipitation data in the Upper Indus Basin (UIB). Eight various imputation approaches were used on sparsely gauged mountainous UIB on a monthly time series of twenty-four meteorological observatories. Following that, the estimation approaches were evaluated using a rank-based approach comprising four different statistical indicators. The results indicate that multiple linear regression is the best-performing strategy for most of the stations regardless of season or orography, followed by the arithmetic average method and inverse distance weighing method.

Assessment of the sustainability of groundwater utilization and crop production under optimized irrigation strategies in the North China Plain under future climate change

Over-exploitation of groundwater due to intensive irrigation and anticipated climate change pose severe threats to the water and food security worldwide, particularly in the North China Plain (NCP). Limited irrigation has been recognized as an effective way to improve crop water productivity and slow the rapid decline of groundwater levels. Whether optimized limited irrigation strategies could achieve a balance between groundwater pumping and grain production in the NCP under future climate change deserves further study. In this study, an improved Soil and Water Assessment Tool (SWAT) model was used to simulate climate change impacts on shallow groundwater levels and crop production under limited irrigation strategies to suggest optimal irrigation management practices under future climate conditions in the NCP. The simulations of eleven limited irrigation strategies for winter wheat with targeted irrigations at different growth stages and with irrigated or rainfed summer maize were compared with future business-as-usual management. Climate change impacts showed that mean wheat (maize) yield under adequate irrigation was expected to increase by 13.2% (4.9%) during the middle time period (2041-2070) and by 11.2% (4.6%) during the late time period (2071-2100) under three SSPs compared to the historical period (1971-2000). Mean decline rate of shallow groundwater level slowed by approximately 1 m a-1 during the entire future period (2041-2100) under three SSPs with a greater reduction for SSP5-8.5. The average contribution rate of future climate toward the balance of shallow groundwater pumping and replenishment was 62.9%. Based on the simulated crop yields and decline rate of shallow groundwater level under the future climate, the most appropriate limited irrigation was achieved by applying irrigation during the jointing stage of wheat with rainfed maize, which could achieve the groundwater recovery and sustainable food production.

Influence of management practices on water-related grain yield determinants

Adequate management of N supply, plant density, row spacing, and soil cover has proved useful for increasing grain yields and/or grain yield stability of rainfed crops over the years. We review the impact of these management practices on grain yield water-related determinants: seasonal crop evapotranspiration (ET) and water use efficiency for grain production per unit of evapotranspired water during the growing season (WUEG,ET,s). We highlight a large number of conflicting results for the impact of management on ET and expose the complexity of the ET response to environmental factors. We analyse the influence of management practices on WUEG,ET,s in terms of the three main processes controlling it: (i) the proportion of transpiration in ET (T/ET), (ii) transpiration efficiency for shoot biomass production (TEB), and (iii) the harvest index. We directly relate the impact of management practices on T/ET to their effect on crop light interception and provide evidence that management practices significantly influence TEB. To optimize WUEG,ET,s, management practices should favor soil water availability during critical periods for seed set, thereby improving the harvest index. The need to improve the performance of existing crop growth models for the prediction of water-related grain yield determinants under different management practices is also discussed.

Upcycling rice yield trial data using a weather-driven crop growth model

Efficient plant breeding plays a significant role in increasing crop yields and attaining food security under climate change. Screening new cultivars through yield trials in multi-environments has improved crop yields, but the accumulated data from these trials has not been effectively upcycled. We propose a simple method that quantifies cultivar-specific productivity characteristics using two regression coefficients: yield-ability (β) and yield-plasticity (α). The recorded yields of each cultivar are expressed as a unique linear regression in response to the theoretical potential yield (Y_p) calculated by a weather-driven crop growth model, called as the "YpCGM method". We apply this to 72510 independent datasets from yield trials of rice that used 237 cultivars measured at 110 locations in Japan over 38 years. The YpCGM method can upcycle accumulated yield data for use in genetic-gain analysis and genome-wide-association studies to guide future breeding programs for developing new cultivars suitable for the world's changing climate.

Perennial grass root system specializes for multiple resource acquisitions with differential elongation and branching patterns

Roots optimize the acquisition of limited soil resources, but relationships between root forms and functions have often been assumed rather than demonstrated. Furthermore, how root systems co-specialize for multiple resource acquisitions is unclear. Theory suggests that trade-offs exist for the acquisition of different resource types, such as water and certain nutrients. Measurements used to describe the acquisition of different resources should then account for differential root responses within a single system. To demonstrate this, we grew Panicum virgatum in split-root systems that vertically partitioned high water availability from nutrient availability so that root systems must absorb the resources separately to fully meet plant demands. We evaluated root elongation, surface area, and branching, and we characterized traits using an order-based classification scheme. Plants allocated approximately 3/4th of primary root length towards water acquisition, whereas lateral branches were progressively allocated towards nutrients. However, root elongation rates, specific root length, and mass fraction were similar. Our results support the existence of differential root functioning within perennial grasses. Similar responses have been recorded in many plant functional types suggesting a fundamental relationship. Root responses to resource availability can be incorporated into root growth models via maximum root length and branching interval parameters.

Performance of APSIM to Simulate the Dynamics of Winter Wheat Growth, Phenology, and Nitrogen Uptake from Early Growth Stages to Maturity in Northern Europe

Performance of the APSIM (Agricultural Production Systems sIMulator) wheat model was assessed to simulate winter wheat phenology, biomass, grain yield, and nitrogen (N) uptake for its potential to optimize fertilizer applications for optimal crop growth and minimal environmental degradation. The calibration and evaluation dataset had 144 and 72 different field growing conditions (location (~7) × year (~5) × sowing date (2) × N treatment (7-13)), respectively, and included seven cultivars. APSIM simulated phenological stages satisfactorily with both model calibration and evaluation data sets with r² of 0.97 and RMSE of 3.98-4.15 BBCH (BASF, Bayer, Ciba-Geigy, and Hoechst) scale. Simulations for biomass accumulation and N uptake during early growth stages (BBCH 28-49) were also reasonable with r² of 0.65 and RMSE of 1510 kg ha^-1, and r² of 0.64-0.66 and RMSE of 28-39 kg N ha^-1, respectively, with a higher accuracy during booting (BBCH 45-47). Overestimation of N uptake during stem elongation (BBCH 32-39) was attributed to (1) high inter-annual variability in simulations, and (2) high sensitivity of parameters regulating N uptake from soil. Calibration accuracy of grain yield and grain N was higher than that of biomass and N uptake at the early growth stages. APSIM wheat model showed high potential for optimizing fertilizer management in winter wheat cultivation in Northern Europe.

Silver lining to a climate crisis in multiple prospects for alleviating crop waterlogging under future climates

Extreme weather events threaten food security, yet global assessments of impacts caused by crop waterlogging are rare. Here we first develop a paradigm that distils common stress patterns across environments, genotypes and climate horizons. Second, we embed improved process-based understanding into a farming systems model to discern changes in global crop waterlogging under future climates. Third, we develop avenues for adapting cropping systems to waterlogging contextualised by environment. We find that yield penalties caused by waterlogging increase from 3-11% historically to 10-20% by 2080, with penalties reflecting a trade-off between the duration of waterlogging and the timing of waterlogging relative to crop stage. We document greater potential for waterlogging-tolerant genotypes in environments with longer temperate growing seasons (e.g., UK, France, Russia, China), compared with environments with higher annualised ratios of evapotranspiration to precipitation (e.g., Australia). Under future climates, altering sowing time and adoption of waterlogging-tolerant genotypes reduces yield penalties by 18%, while earlier sowing of winter genotypes alleviates waterlogging by 8%. We highlight the serendipitous outcome wherein waterlogging stress patterns under present conditions are likely to be similar to those in the future, suggesting that adaptations for future climates could be designed using stress patterns realised today.

Assessing impacts of global climate change on water and food security in the black soil region of Northeast China using an improved SWAT-CO2 model

Future climate change may have substantial impacts on both water resources and food security in China's black soil region. The Liao River Basin (LRB; 220,000 km²) is representative of the main black soil area, making it ideal for studying climate change effects on black soil. In this study, the Soil and Water Assessment Tool (SWAT) model was first initialized for the LRB. Actual evapotranspiration (ET_a) values calculated using the Surface Energy Balance System (SEBS) model and city-level corn (Zea mays L.) yields were then used to calibrate the SWAT model. Finally, the SWAT model was modified to accept dynamic CO₂ input and output crop transpiration, soil evaporation, and canopy interception separately to explore the impacts of future climate change on ET related variables and crop water productivity (CWP) in the LRB. Simulation scenario design included 22 General Circulation Models (GCMs) and 4 Shared Socioeconomic Pathways (SSPs) scenarios from the latest Coupled Model Intercomparison Project 6 (CMIP6) for two 30-year periods of 2041-2070 and 2071-2100. The predicted results showed a significant (P < 0.05) increase in air temperatures and precipitation in the LRB. In contrast, solar radiation decreased significantly and was most reduced for the SSP3-7.0 scenario. Reference evapotranspiration (ET_o), ET_a, and soil evaporation significantly increased in future scenarios, while canopy interception and crop transpiration showed significant reductions, particularly under the 2071-2100 SSP5-8.5 scenario. Overall, corn yield elevated considerably (P < 0.05) with the largest increase for the SSP5-8.5 scenario during 2071-2100. However, the SSP3-7.0 scenario indicated a significant decline in yield. Future changes in CWP were similar to those for corn yield, with significant increases in the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. These findings suggested future climate change may have a positive impact on corn production in the black soil region of the LRB.

Optimizing maize yields using growth stimulants under the strategy of replacing chemicals with biological fertilizers

Partial replacement of chemicals with biological fertilizers is a recommended strategy to reduce the adverse environmental effects of chemical fertilizer losses. Enhancing the reduced mineral with biological fertilizers strategy by foliar application of humic acid (HA) and amino acids (AA) can reduce environmental hazards, while improving maize (Zea mays L.) production under semiarid conditions. The recommended doses of N, P and K (e.g., 286 kg N ha^-1, 75 kg P₂O₅ ha^-1 and 67 kg K₂O ha^-1) were applied as the first fertilization level (100% NPK) and were replaced with biofertilizers by 100%, 75%, 50% and 25% as levels of reducing mineral fertilization. These treatments were applied under four foliar applications of tap water (TW), HA, AA and a mixture of HA and AA. Our results reported significant reductions in all parameters, including maize ear yield attributes and grain nutrient uptake, when replacing the mineral NPK with biofertilizers by 25-100% replacement. However, these reductions were mitigated significantly under the application of growth stimulants in the descending order: HA and AA mixture>AA>HA>TA. Applying a mixture of HA and AA with 75% NPK + biofertilizers increased ear length, grain yield, grain uptake of N and K, and crude protein yield by 37, 3, 4, 11 and 7%, respectively as compared with 100% mineral fertilizer only. Moreover, all investigated parameters were maximized under the application of 75% NPK + biofertilizers combined with AA or the mixture of HA and AA, which reveals the importance of growth stimulants in enhancing the reduced chemical NPK strategy. It could be concluded that the mineral NPK rate can be reduced by 25% with biofertilization without any yield losses when combined with HA and AA under arid and semi-arid conditions. That achieves the dual goals of sustainable agriculture by improving yield, while reducing environmental adverse effects.

Future water security in the major basins of China under the 1.5 °C and 2.0 °C global warming scenarios

Freshwater is an essential resource for human lives, agriculture, industry, and ecology. Future water supply, water withdrawal, and water security under the impacts of climate change and human interventions have been of key concern. Numerous studies have projected future changes in river runoff and surface water resources under climate change. However, the changes in the major water withdrawal components including agricultural irrigation water, industrial, domestic and ecological water withdrawal, as well as the balance between water supply and withdrawal, under the joint impacts of climate change and socio-economic development have been seldom investigated, especially at the basin and national scales. In this study, changes in surface water resources, agricultural irrigation water, industrial, domestic and ecological water withdrawal, as well as the balances between water supply and withdrawal, under the baseline climate (2006-2015), 1.5 °C and 2.0 °C warming scenarios (2106-2115) in the 10 major basins across China, were investigated by combining modelling and local census data. The results showed that water withdrawal exceeded water supply in the basins of Liao River, Northwest River, Hai River, Yellow River and Huai River in the baseline period. Under the 1.5 °C and 2.0 °C warming scenarios, the shortage of water resources would aggravate in the above-mentioned basins and the Songhua River basin. And the surplus of water resources would reduce substantially in the basins of Yangtze River, Southeast River and Pearl River. Overall, the difference between water supply and water withdrawal was 435.88 billion m³ during the baseline period, and would be 261.84 and 218.39 billion m³, respectively, under the 1.5 °C and 2.0 °C warming scenarios. This study provides a comprehensive perspective on future water security in the 10 major basins across China, has important implications for water resources management and climate change adaptation.

Lessons from integrated seasonal forecast-crop modelling in Africa: A systematic review

Seasonal forecasts coupled with crop models can potentially enhance decision-making in smallholder farming in Africa. The study sought to inform future research through identifying and critiquing crop and climate models, and techniques for integrating seasonal forecast information and crop models. Peer-reviewed articles related to crop modelling and seasonal forecasting were sourced from Google Scholar, Web of Science, AGRIS, and JSTOR. Nineteen articles were selected from a search outcome of 530. About 74% of the studies used mechanistic models, which are favored for climate risk management research as they account for crop management practices. European Centre for Medium-Range Weather Forecasts and European Centre for Medium-Range Weather Forecasts, Hamburg, are the predominant global climate models (GCMs) used across Africa. A range of approaches have been assessed to improve the effectiveness of the connection between seasonal forecast information and mechanistic crop models, which include GCMs, analogue, stochastic disaggregation, and statistical prediction through converting seasonal weather summaries into the daily weather. GCM outputs are produced in a format compatible with mechanistic crop models. Such outputs are critical for researchers to have information on the merits and demerits of tools and approaches on integrating seasonal forecast and crop models. There is however need to widen such research to other regions in Africa, crop, farming systems, and policy.

Physiological and biochemical responses of hybrid maize (Zea mays L.) varieties grown under heat stress conditions

Maize (Zea mays L.) is the second most commonly produced and consumed crop after wheat globally and is adversely affected by high heat, which is a significant abiotic stress factor. This study was carried out to determine the physiological and biochemical responses of hybrid corn varieties under heat stress ('HS') compared to control ('C') conditions during the 2020 and 2021 growing seasons. The experiment was conducted under natural conditions in the Southeastern region of Turkey, where the most intense temperatures are experienced. This experiment used split plots in randomized blocks with three replications, with 'HS' and 'C' growing conditions applied to the main plots and the different hybrid corn varieties (FAO 650) planted on the sub plots. Mean values of days to 50% tasseling (DT, day), grain yield (GY, kg ha^-1), leaf water potential (LWP, %), chlorophyll-a (Chl-a, mg g^-1), cell membrane damage (CMD, %), and total phenol content (TPC, μg g^-1) were significantly different between years, growing conditions, and hybrid corn varieties. Changes in the climate played a significant role in the differences between the years and growing conditions (GC), while the genetic characteristics of the different corn varieties explained the differences in outcomes between them. The values of DT, GY, LWP, Chl-a, CMD, and TPC ranged from 49.06-53.15 days, 9,173.0-10,807.2 kg ha^-1, 78.62-83.57%, 6.47-8.62 mg g^-1, 9.61-13.54%, and 232.36-247.01 μg g^-1, respectively. Significant correlations were recorded between all the parameters. Positive correlations were observed between all the variables except for CMD. The increased damage to cell membranes under 'HS' caused a decrease in the other measured variables, especially GY. In contrast, the GY increased with decreased CMD. CMD was important in determining the stress and tolerance level of corn varieties under 'HS' conditions. The GY and other physiological parameters of ADA 17.4 and SYM-307 candidate corn varieties surpassed the control hybrid corn cultivars. The results revealed that the ADA 17.4 and SYM-307 cultivars might have 'HS'-tolerate genes.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

Agricultural Adaptation to Reconcile Food Security and Water Sustainability Under Climate Change: The Case of Cereals in Iran

In this study, we simulate the crop yield and water footprint (WF) of major food crops of Iran on irrigated and rainfed croplands for the historical and the future climate. We assess the effects of three agricultural adaptation strategies to climate change in terms of potential blue water savings. We then evaluate to what extent these savings can reduce unsustainable blue WF. We find that cereal production increases under climate change in both irrigated and rainfed croplands (by 2.6-3.1 and 1.4-2.3 million t yr-1, respectively) due to increased yields (6.6%-78.7%). Simultaneously, the unit WF (m3 t-1) tends to decrease in most scenarios. However, the annual consumptive water use increases in both irrigated and rainfed croplands (by 0.3-1.8 and 0.5-1.7 billion m3 yr-1, respectively). This is most noticeable in the arid regions, where consumptive water use increases by roughly 70% under climate change. Off-season cultivation is the most effective adaptation strategy to alleviate additional pressure on blue water resources with blue water savings of 14-15 billion m3 yr-1. The second most effective is WF benchmarking, which results in blue water savings of 1.1-3.5 billion m3 yr-1. The early planting strategy is less effective but still leads to blue water savings of 1.7-1.9 billion m3 yr-1. In the same order of effectiveness, these three strategies can reduce blue water scarcity and unsustainable blue water use in Iran under current conditions. However, we find that these strategies do not mitigate water scarcity in all provinces per se, nor all months of the year.

Dynamic linkages between climatic variables and agriculture production in Malaysia: a generalized method of moments approach

Climate change continues to pose a threat to the agricultural sectors worldwide, jeopardizing food and nutritional security, which is a critical component of the sustainable development agenda. Consequently, this study attempts to examine the impact of climatic variables (CO₂ emissions, energy resources, rainfall, temperature, fossil fuel consumption, and humidity) on agricultural production of rice, cereals, vegetables, coffee, and agriculture value added (as a percentage of GDP) in the Malaysian context. To this end, this study applied a generalized method of moments (GMM) estimator on the data obtained from the metrological station Malaysia, Department of Statistics Malaysia and World Development Indicators (WDI) spanning the period 1985-2016. The results revealed that temperature and energy consumption negatively and significantly affect rice and vegetable production, while the negative effect of rainfall, temperature, fossil fuel consumption, and humidity on cereal production is insignificant. The results also confirmed that CO₂ emissions have a negative and significant impact on coffee production. Likewise, temperature, energy consumption, and fossil fuel consumption exhibit a negative and significant influence on agriculture value added. These observations evidenced the adverse effect of climate change on various agricultural products in Malaysia. Therefore, in order to ensure robust and sustainable agricultural output in Malaysia, policymakers as well as environmentalists should work together to formulate appropriate adaptation strategies.

Six decades of warming and drought in the world's top wheat-producing countries offset the benefits of rising CO2 to yield

Future atmospheric carbon-dioxide concentration ([CO₂]) rise is expected to increase the grain yield of C3 crops like wheat even higher under drought. This expectation is based on small-scale experiments and model simulations based on such observations. However, this combined effect has never been confirmed through actual observations at the nationwide or regional scale. We present the first evidence that warming and drought in the world's leading wheat-producing countries offset the benefits of increasing [CO₂] to wheat yield in the last six decades. Using country-level wheat yield census observations, [CO₂] records, and gridded climate data in a statistical model based on a well-established methodology, we show that a [CO₂] rise of ~ 98 μmol mol^-1 increased the yield by 7% in the area of the top-twelve wheat-producing countries, while warming of 1.2 °C and water depletion of ~ 29 mm m^-2 reduced the wheat grain yield by ~ 3% and ~ 1%, respectively, in the last six decades (1961-2019). Our statistical model corroborated the beneficial effect of [CO₂] but contrasted the expected increase of grain yield under drought. Moreover, the increase in [CO₂] barely offsets the adverse impacts of warming and drought in countries like Germany and France, with a net yield loss of 3.1% and no gain, respectively, at the end of the sampling period relative to the 1961-1965 baseline. In China and the wheat-growing areas of the former Soviet Union-two of the three largest wheat-producing regions-yields were ~ 5.5% less than expected from current [CO₂] levels. Our results suggest shifting our efforts towards more experimental studies set in currently warm and dry areas and combining these with statistical and numerical modeling to improve our understanding of future impacts of a warmer and drier world with higher [CO₂].

Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.

A doubling of atmospheric CO2 mitigates the effects of severe drought on maize through the preservation of soil water

BACKGROUND AND AIMS

Drought limits maize production in many regions of the world, and this is likely to intensify in future. Elevated atmospheric CO2 (eCO2) can mitigate this by reducing stomatal conductance and water loss without reducing yield. The magnitude of this effect depends on the interaction of eCO2 and drought severity, but scarce data collected under severe drought conditions limit predictions of future maize production.

METHODS

We compared the severe drought × eCO2 responses of six maize genotypes from semi-arid and sub-humid growing regions.

KEY RESULTS

Genotypic differences were apparent in growth, gas exchange, water relations, grain quality, and biomass at maturity, but the response to eCO2 was consistent. Plants under drought and eCO2 had similar biomass and yield to irrigated plants at ambient CO2. Reduced stomatal conductance and water loss preserved soil moisture equivalent to 35 mm of rainfall and allowed sustained photosynthesis at higher rates for a longer period after watering stopped. Under irrigation, eCO2 improved maize growth but not grain yield.

CONCLUSIONS

The results suggest that eCO2 may extend the future land area available to rainfed maize cultivation, but cannot circumvent the absence of seasonal rainfall that restricts maize growth. Elevated CO2 will reduce water requirements of irrigated maize when atmospheric conditions drive high evapotranspiration.

Demonstration of local adaptation in maize landraces by reciprocal transplantation

Populations are locally adapted when they exhibit higher fitness than foreign populations in their native habitat. Maize landrace adaptations to highland and lowland conditions are of interest to researchers and breeders. To determine the prevalence and strength of local adaptation in maize landraces, we performed a reciprocal transplant experiment across an elevational gradient in Mexico. We grew 120 landraces, grouped into four populations (Mexican Highland, Mexican Lowland, South American Highland, South American Lowland), in Mexican highland and lowland common gardens and collected phenotypes relevant to fitness and known highland‐adaptive traits such as anthocyanin pigmentation and macrohair density. 67k DArTseq markers were generated from field specimens to allow comparisons between phenotypic patterns and population genetic structure. We found phenotypic patterns consistent with local adaptation, though these patterns differ between the Mexican and South American populations. Quantitative trait differentiation (Q_ST) was greater than neutral allele frequency differentiation (F_ST) for many traits, signaling directional selection between pairs of populations. All populations exhibited higher fitness metric values when grown at their native elevation, and Mexican landraces had higher fitness than South American landraces when grown in these Mexican sites. As environmental distance between landraces’ native collection sites and common garden sites increased, fitness values dropped, suggesting landraces are adapted to environmental conditions at their natal sites. Correlations between fitness and anthocyanin pigmentation and macrohair traits were stronger in the highland site than the lowland site, supporting their status as highland‐adaptive. These results give substance to the long‐held presumption of local adaptation of New World maize landraces to elevation and other environmental variables across North and South America.

Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models

Future climate scenarios are predicting considerable threats to sustainable maize production in arid and semi-arid regions. These adverse impacts can be minimized by adopting modern agricultural tools to assess and develop successful adaptation practices. A multi-model approach (climate and crop) was used to assess the impacts and uncertainties of climate change on maize crop. An extensive field study was conducted to explore the temporal thermal variations on maize hybrids grown at farmer's fields for ten sowing dates during two consecutive growing years. Data about phenology, morphology, biomass development, and yield were recorded by adopting standard procedures and protocols. The CSM-CERES, APSIM, and CSM-IXIM-Maize models were calibrated and evaluated. Five GCMs among 29 were selected based on classification into different groups and uncertainty to predict climatic changes in the future. The results predicted that there would be a rise in temperature (1.57-3.29 °C) during the maize growing season in five General Circulation Models (GCMs) by using RCP 8.5 scenarios for the mid-century (2040-2069) as compared with the baseline (1980-2015). The CERES-Maize and APSIM-Maize model showed lower root mean square error values (2.78 and 5.41), higher d-index (0.85 and 0.87) along reliable R² (0.89 and 0.89), respectively for days to anthesis and maturity, while the CSM-IXIM-Maize model performed well for growth parameters (leaf area index, total dry matter) and yield with reasonably good statistical indices. The CSM-IXIM-Maize model performed well for all hybrids during both years whereas climate models, NorESM1-M and IPSL-CM5A-MR, showed less uncertain results for climate change impacts. Maize models along GCMs predicted a reduction in yield (8-55%) than baseline. Maize crop may face a high yield decline that could be overcome by modifying the sowing dates and fertilizer (fertigation) and heat and drought-tolerant hybrids.

A global dataset for the projected impacts of climate change on four major crops

Reliable estimates of the impacts of climate change on crop production are critical for assessing the sustainability of food systems. Global, regional, and site-specific crop simulation studies have been conducted for nearly four decades, representing valuable sources of information for climate change impact assessments. However, the wealth of data produced by these studies has not been made publicly available. Here, we develop a global dataset by consolidating previously published meta-analyses and data collected through a new literature search covering recent crop simulations. The new global dataset builds on 8703 simulations from 202 studies published between 1984 and 2020. It contains projected yields of four major crops (maize, rice, soybean, and wheat) in 91 countries under major emission scenarios for the 21st century, with and without adaptation measures, along with geographical coordinates, current temperature and precipitation levels, projected temperature and precipitation changes. This dataset provides a solid basis for a quantitative assessment of the impacts of climate change on crop production and will facilitate the rapidly developing data-driven machine learning applications.

Measurement(s)	relative yield change
Technology Type(s)	crop simulation model
Factor Type(s)	geographic location • current average temperature • current annual precipitation • future mid-point • climate scenario • temperature change • annual precipitation change • CO2 ppm
Sample Characteristic - Organism	Zea mays • Oryza sativa • Glycine max • Triticum aestivum
Sample Characteristic - Environment	climate change
Sample Characteristic - Location	global

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.17427674

D. Weber T, Tesfaye K, Streck T. Climate change impact on wheat and maize growth in Ethiopia: A multi-model uncertainty analysis

Ethiopia’s economy is dominated by agriculture which is mainly rain-fed and subsistence. Climate change is expected to have an adverse impact particularly on crop production. Previous studies have shown large discrepancies in the magnitude and sometimes in the direction of the impact on crop production. We assessed the impact of climate change on growth and yield of maize and wheat in Ethiopia using a multi-crop model ensemble. The multi-model ensemble (n = 48) was set up using the agroecosystem modelling framework Expert-N. The framework is modular which facilitates combining different submodels for plant growth and soil processes. The multi-model ensemble was driven by climate change projections representing the mid of the century (2021–2050) from ten contrasting climate models downscaled to finer resolution. The contributions of different sources of uncertainty in crop yield prediction were quantified. The sensitivity of crop yield to elevated CO₂, increased temperature, changes in precipitations and N fertilizer were also assessed. Our results indicate that grain yields were very sensitive to changes in [CO₂], temperature and N fertilizer amounts where the responses were higher for wheat than maize. The response to change in precipitation was weak, which we attribute to the high water holding capacity of the soils due to high organic carbon contents at the study sites. This may provide the sufficient buffering capacity for extended time periods with low amounts of precipitation. Under the changing climate, wheat productivity will be a major challenge with a 36 to 40% reduction in grain yield by 2050 while the impact on maize was modest. A major part of the uncertainty in the projected impact could be attributed to differences in the crop growth models. A considerable fraction of the uncertainty could also be traced back to different soil water dynamics modeling approaches in the model ensemble, which is often ignored. Uncertainties varied among the studied crop species and cultivars as well. The study highlights significant impacts of climate change on wheat yield in Ethiopia whereby differences in crop growth models causes the large part of the uncertainties.

Evaluating the long-term impact of projected climate on rice-lentil-groundnut cropping system in Lower Gangetic Plain of India using crop simulation modelling

Most simulations of food production in response to various climates to date have used simulations of the same crop over multiple years. This study evaluated the impact of projected climate on performance of rice-lentil-groundnut cropping sequence in New Alluvial Zone of West Bengal, India, using DSSAT model. The study period consisted of baseline (1980-2010), mid-century (2040-2069) and end-century (2070-2099). Advancement in days to anthesis (2-13 days) was simulated for rice during the future periods. For lentil and groundnut, average advancement in days to anthesis was 1 day. Days to maturity were shortened by 3-16 days for rice and 0-7 days for lentil. Nevertheless, for groundnut, the days to maturity were simulated to increase by 1-9 days. The impact on final biomass and yield was simulated with and without CO₂ fertilization, and the positive impact of CO₂ fertilization was prominent for all the three crops. When CO₂ fertilization effect was considered, the yield of rice was projected to increase by 11-32%. On the other hand, yield of lentil and groundnut was estimated to change by - 31 to - 12% and - 33 to + 8%, respectively. Enhanced CO₂ could mitigate the magnitude of yield reduction due to enhanced temperature. Rice was benefited due to the carryover effect of residue from preceding groundnut and, hence, could sustain the yield on a long term. The study could also quantify the uncertainty in simulation of yield due to selection of GCMs.

Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks

The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Environmental data collected at the field level, such as soil and climatic information, can be relevant to improve predictive ability in genomic prediction models by describing more precisely genotype-by-environment interactions, which represent a key component of the phenotypic response for complex crop agronomic traits. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-related variables was derived and used in prediction models alongside genotypic data. Linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). These models were evaluated under four prediction problems: (1) tested and new genotypes in a new year; (2) only unobserved genotypes in a new year; (3) tested and new genotypes in a new site; (4) only unobserved genotypes in a new site. Accuracy in forecasting grain yield performance of new genotypes in a new year was improved by up to 20% over the baseline model by including environmental predictors with gradient boosting methods. For plant height, an enhancement of predictive ability could neither be observed by using machine learning-based methods nor by using detailed environmental information. An investigation of key environmental factors using gradient boosting frameworks also revealed that temperature at flowering stage, frequency and amount of water received during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.

Impacts of climate change on wheat phenology and yield in Indus Basin, Pakistan

Aim of this study is to quantify the impacts of climate change on phenology and yield of winter wheat in rainfed and irrigated regions of Pakistan by using integration of two well-known crop models including STICS and APSIM with CORDEX-SA regional climate models (RCMs). A number of different adaptation strategies based on early sowing (i.e. S1:10 and S2:20 days), irrigation (I1:15% and I2:30% additional water) and a combination of sowing and irrigation adaptations were examined to recover the potential losses that would occur due to climate change. The data for the wheat phenology, biomass (t/ha) at different stages and yield (t/ha) was obtained from several experiments at national research institutes in Pakistan under both rainfed and irrigated conditions. After calibration and validation of both crop models (STICS and APSIM), the current climate data were replaced with the CORDEX-SA RCM-projections for climate change impact analysis. A significant rising and declining trends were observed in temperature and precipitation patterns, respectively, for the selected study regions. Consequently, a substantial impact of climate change on wheat phenology (anthesis stage, maturity stage, growing length), biomass (t/ha) and yield (t/ha) was observed under scenario periods for RCP4.5 and RCP8.5. Additionally, the adaptation strategies on wheat for rainfed regions showed a substantial improvement in wheat biomass and yield simulated by STICS model particularly for sowing-2 under RCP4.5. Irrigated regions showed more improvement for irrigation-2 (I2) and combination of sowing-1 + irrigation-2 (S1 + I2) using the STICS model under both RCPs. Overall, it was observed that changes in crop phenology had a stronger impact in terms of crop yield for RCP8.5 as compare to RCP4.5. This study provides a valuable understanding and way forward for the better wheat management under changes in precipitation and temperature patterns. The study also discuss in detail, the adaptation strategies to cope with potential damage, over two different irrigation zones (rainfed and irrigated) in Pakistan.

An interaction regression model for crop yield prediction

Crop yield prediction is crucial for global food security yet notoriously challenging due to multitudinous factors that jointly determine the yield, including genotype, environment, management, and their complex interactions. Integrating the power of optimization, machine learning, and agronomic insight, we present a new predictive model (referred to as the interaction regression model) for crop yield prediction, which has three salient properties. First, it achieved a relative root mean square error of 8% or less in three Midwest states (Illinois, Indiana, and Iowa) in the US for both corn and soybean yield prediction, outperforming state-of-the-art machine learning algorithms. Second, it identified about a dozen environment by management interactions for corn and soybean yield, some of which are consistent with conventional agronomic knowledge whereas some others interactions require additional analysis or experiment to prove or disprove. Third, it quantitatively dissected crop yield into contributions from weather, soil, management, and their interactions, allowing agronomists to pinpoint the factors that favorably or unfavorably affect the yield of a given location under a given weather and management scenario. The most significant contribution of the new prediction model is its capability to produce accurate prediction and explainable insights simultaneously. This was achieved by training the algorithm to select features and interactions that are spatially and temporally robust to balance prediction accuracy for the training data and generalizability to the test data.

Stronger temperature-moisture couplings exacerbate the impact of climate warming on global crop yields

Rising air temperatures are a leading risk to global crop production. Recent research has emphasized the critical role of moisture availability in regulating crop responses to heat and the importance of temperature-moisture couplings in driving concurrent heat and drought. Here, we demonstrate that the heat sensitivity of key global crops depends on the local strength of couplings between temperature and moisture in the climate system. Over 1970-2013, maize and soy yields dropped more during hotter growing seasons in places where decreased precipitation and evapotranspiration more strongly accompanied higher temperatures, suggestive of compound heat-drought impacts on crops. On the basis of this historical pattern and a suite of climate model projections, we show that changes in temperature-moisture couplings in response to warming could enhance the heat sensitivity of these crops as temperatures rise, worsening the impact of warming by -5% (-17 to 11% across climate models) on global average. However, these changes will benefit crops where couplings weaken, including much of Asia, and projected impacts are highly uncertain in some regions. Our results demonstrate that climate change will impact crops not only through warming but also through changing drivers of compound heat-moisture stresses, which may alter the sensitivity of crop yields to heat as warming proceeds. Robust adaptation of cropping systems will need to consider this underappreciated risk to food production from climate change.

Acclimation of leaf photosynthesis and respiration to warming in field-grown wheat

Climate change and future warming will significantly affect crop yield. The capacity of crops to dynamically adjust physiological processes (i.e., acclimate) to warming might improve overall performance. Understanding and quantifying the degree of acclimation in field crops could ensure better parameterization of crop and Earth System models and predictions of crop performance. We hypothesized that for field-grown wheat, when measured at a common temperature (25°C), crops grown under warmer conditions would exhibit acclimation, leading to enhanced crop performance and yield. Acclimation was defined as (a) decreased rates of net photosynthesis at 25°C (A²⁵ ) coupled with lower maximum carboxylation capacity (V_cmax²⁵ ), (b) reduced leaf dark respiration at 25°C (both in terms of O₂ consumption R_dark _O₂²⁵ and CO₂ efflux R_dark _CO₂²⁵ ) and (c) lower R_dark _CO₂²⁵ to V_cmax²⁵ ratio. Field experiments were conducted over two seasons with 20 wheat genotypes, sown at three different planting dates, to test these hypotheses. Leaf-level CO₂ -based traits (A²⁵ , R_dark _CO₂²⁵ and V_cmax²⁵ ) did not show the classic acclimation responses that we hypothesized; by contrast, the hypothesized changes in R_{dark_} O₂ were observed. These findings have implications for predictive crop models that assume similar temperature response among these physiological processes and for predictions of crop performance in a future warmer world.

Lengthening of maize maturity time is not a widespread climate change adaptation strategy in the US Midwest

Increasing temperatures in the US Midwest are projected to reduce maize yields because warmer temperatures hasten reproductive development and, as a result, shorten the grain fill period. However, there is widespread expectation that farmers will mitigate projected yield losses by planting longer season hybrids that lengthen the grain fill period. Here, we ask: (a) how current hybrid maturity length relates to thermal availability of the local climate, and (b) if farmers are shifting to longer season hybrids in response to a warming climate. To address these questions, we used county-level Pioneer brand hybrid sales (Corteva Agriscience) across 17 years and 650 counties in 10 Midwest states (IA, IL, IN, MI, MN, MO, ND, OH, SD, and WI). Northern counties were shown to select hybrid maturities with growing degree day (GDD°C) requirements more closely related to the environmentally available GDD compared to central and southern counties. This measure, termed "thermal overlap," ranged from complete 106% in northern counties to a mere 63% in southern counties. The relationship between thermal overlap and latitude was fit using split-line regression and a breakpoint of 42.8°N was identified. Over the 17-years, hybrid maturities shortened across the majority of the Midwest with only a minority of counties lengthening in select northern and southern areas. The annual change in maturity ranged from -5.4 to 4.1 GDD year^-1 with a median of -0.9 GDD year^-1 . The shortening of hybrid maturity contrasts with widespread expectations of hybrid maturity aligning with magnitude of warming. Factors other than thermal availability appear to more strongly impact farmer decision-making such as the benefit of shorter maturity hybrids on grain drying costs, direct delivery to ethanol biorefineries, field operability, labor constraints, and crop genetics availability. Prediction of hybrid choice under future climate scenarios must include climatic factors, physiological-genetic attributes, socio-economic, and operational constraints.

Adoption of soil and water conservation technology and its effect on the productivity of smallholder rice farmers in Southwest Nigeria

This study estimated the effect of the adoption of soil and water conservation (SWC) on the productivity of 360 smallholder rice farmers in Southwest Nigeria. An endogenous switching regression model (ESRM) was employed to estimate the productivities of adopter and non-adopters of SWC. A doubly robust inverse-probability-weighted regression adjustment (IPWRA) was used as a credible remedy for potentially biased estimates of average treatment on the treated (ATT) and potential outcome mean (POM) of the endogenous treatment model. Significant variables, such as farmers’ locations, gender, marital status, annual temperature, annual precipitation, log of fertiliser and membership in farm-based organisation (FBO), were factors influencing the adoption of SWC among smallholder rice farmers. Factors such as age, marital status, rice experience, farm size, formal education, access to extension and labour in man-days significantly influenced the rice productivity of smallholder farmers who adopted SWC technology, while location, marital status, rice experience, farm size, formal education, access to extension and labour in man-days were the determinants of rice productivity among smallholder farmers who did not adopt SWC technology. The result from the inverse-probability-weighted regression adjustment estimation indicates that the adoption of SWC technology to mitigate the adverse effects of climate change improves the productivity of rice in the study area. To ensure effective dissemination and the adoption of new conservation technologies, government and stakeholders in rice production could take the lead in promotion and dissemination in the initial stages and, in the process, create an enabling environment for the effective participation of other stakeholders in rice production.

Ensemble modelling, uncertainty and robust predictions of organic carbon in long-term bare-fallow soils

Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate-change studies. It is imperative to increase confidence in long-term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process-based C models by comparing simulations to experimental data from seven long-term bare-fallow (vegetation-free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi-year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge-based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin-up initialization of SOC. Changes in the multi-model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt

This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.

Challenges to the Adaptation of Double Cropping Agricultural Systems in Brazil under Changes in Climate and Land Cover

The wide adoption of highly productive soy–maize double cropping has allowed Brazil to become one of the main producers and exporters of these commodities. However, land cover and climate change could affect the viability of double cropping due to a shortening of the rainy season, and both crops could be affected. The goals of this study were to evaluate if adaptation measures such as adoption of shorter-cycle cultivars and delaying sowing dates are effective to maintain soybean and maize yield in the main producing regions in Brazil. We used a crop model and four climate models to simulate double cropping in two climate scenarios that differ in Amazonia and Cerrado deforestation levels. We tested if 10 soybean and 17 maize sowing dates and three cultivar combination could reduce the impacts of a shorter rainy season in double cropping yield and gross revenue. Results showed a decrease in maize yield due to a delay of soybean sowing dates and rainfall reduction during the growing season. Adaptation through delaying sowing dates and the adoption of short cycle cultivars was not effective to maintain system revenue in all the study regions in a scenario with high deforestation levels.

Narrowing uncertainties in the effects of elevated CO2 on crops

Plant responses to rising atmospheric carbon dioxide (CO₂) concentrations, together with projected variations in temperature and precipitation will determine future agricultural production. Estimates of the impacts of climate change on agriculture provide essential information to design effective adaptation strategies, and develop sustainable food systems. Here, we review the current experimental evidence and crop models on the effects of elevated CO₂ concentrations. Recent concerted efforts have narrowed the uncertainties in CO₂-induced crop responses so that climate change impact simulations omitting CO₂ can now be eliminated. To address remaining knowledge gaps and uncertainties in estimating the effects of elevated CO₂ and climate change on crops, future research should expand experiments on more crop species under a wider range of growing conditions, improve the representation of responses to climate extremes in crop models, and simulate additional crop physiological processes related to nutritional quality.

Sources of uncertainty for wheat yield projections under future climate are site-specific

Understanding sources of uncertainty in climate-crop modelling is critical for informing adaptation strategies for cropping systems. An understanding of the major sources of uncertainty in yield change is needed to develop strategies to reduce the total uncertainty. Here, we simulated rain-fed wheat cropping at four representative locations in China and Australia using eight crop models, 32 global climate models (GCMs) and two climate downscaling methods, to investigate sources of uncertainty in yield response to climate change. We partitioned the total uncertainty into sources caused by GCMs, crop models, climate scenarios and the interactions between these three. Generally, the contributions to uncertainty were broadly similar in the two downscaling methods. The dominant source of uncertainty is GCMs in Australia, whereas in China it is crop models. This difference is largely due to uncertainty in GCM-projected future rainfall change across locations. Our findings highlight the site-specific sources of uncertainty, which should be one step towards understanding uncertainties for more robust climate-crop modelling.

Responses of plant biomass and yield component in rice, wheat, and maize to climatic warming: a meta-analysis

Responses of plant biomass and yield components to warming are species-specific and are shifted as increased warming magnitude rises; this finding improves the results of IPCC AR5. The responses of crop yields to climatic warming have been extensively reported from experimental results, historical yield collections, and modeling research. However, an integrative report on the responses of plant biomass and yield components of three major crops to experimental warming is lacking. Here, a meta-analysis based on the most recent warming experiments was conducted to quantify the climatic warming responses of the biomass, grain yield (GY), and yield components of three staple crops. The results showed that the wheat total aboveground biomass (TAGB) increased by 6.0% with general warming, while the wheat GY did not significantly respond to warming; however, the responses shifted with increases in the mean growing season temperature (MGST). Negative effects on wheat TAGB and GY appeared when the MGSTs were above 15 °C and 13 °C, respectively. The wheat GY and the number of grains per panicle decreased by 8.4% and 7.5%, respectively, per degree Celsius increase. Increases in temperature significantly reduced the rice TAGB and GY by 4.3% and 16.6%, respectively, but rice straw biomass increased with increasing temperature. However, the rice grain weight and the number of panicles decreased with continuous increasing temperature (ΔT_a). The maize biomass, GY, and yield components all generally decreased with climatic warming. Finally, the crop responses to climatic warming were significantly influenced by warming time, warming treatment facility, and methods. Our findings can improve the assessment of crop responses to climatic warming and are useful for ensuring food security while combating future global climate change.

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub-Saharan Africa

Smallholder farmers in sub-Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low-input systems is currently lacking. We evaluated the impact of varying [CO₂ ], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi-arid Rwanda, hot subhumid Ghana and hot semi-arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in-season soil water content from 2-year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO₂ ], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO₂ ]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management.

Climate change and the need for agricultural adaptation

Agriculture and food security are predicted to be significantly impacted by climate change, though the impact will vary by region and by crop. Combined with the increasing global population, there is an urgent need for agriculture to adapt to ensure future food security for this growing population. Adaptation strategies include changing land and cropping practices, the development of improved crop varieties and changing food consumption and waste. Recent advances in genomics and agronomy can help alleviate some of the impacts of climate change on food production; however, given the timeframe for crop improvement, significant investment is required to realise these changes. Ultimately, there is a limit as to how far agriculture can adapt to the changing climate, and a political will to reduce the impact of burning of fossil fuels on the global climate is essential for long term food security.

Spatio-Temporal Analysis of Historical and Future Climate Data in the Texas High Plains

Agricultural production in the Texas High Plains (THP) relies heavily on irrigation and is susceptible to drought due to the declining availability of groundwater and climate change. Therefore, it is meaningful to perform an overview of possible climate change scenarios to provide appropriate strategies for climate change adaptation in the THP. In this study, spatio-temporal variations of climate data were mapped in the THP during 2000–2009, 2050–2059, and 2090–2099 periods using 14 research-grade meteorological stations and 19 bias-corrected General Circulation Models (GCMs) under representative concentration pathway (RCP) scenarios RCP 4.5 and 8.5. Results indicated different bias correction methods were needed for different climatic parameters and study purposes. For example, using high-quality data from the meteorological stations, the linear scaling method was selected to alter the projected precipitation while air temperatures were bias corrected using the quantile mapping method. At the end of the 21st century (2090–2099) under the severe CO2 emission scenario (RCP 8.5), the maximum and minimum air temperatures could increase from 3.9 to 10.0 °C and 2.8 to 8.4 °C across the entire THP, respectively, while precipitation could decrease by ~7.5% relative to the historical (2000–2009) observed data. However, large uncertainties were found according to 19 GCM projections.

Photosynthesis in a Changing Global Climate: Scaling Up and Scaling Down in Crops

Photosynthesis is the major process leading to primary production in the Biosphere. There is a total of 7000bn tons of CO2 in the atmosphere and photosynthesis fixes more than 100bn tons annually. The CO2 assimilated by the photosynthetic apparatus is the basis of crop production and, therefore, of animal and human food. This has led to a renewed interest in photosynthesis as a target to increase plant production and there is now increasing evidence showing that the strategy of improving photosynthetic traits can increase plant yield. However, photosynthesis and the photosynthetic apparatus are both conditioned by environmental variables such as water availability, temperature, [CO2], salinity, and ozone. The "omics" revolution has allowed a better understanding of the genetic mechanisms regulating stress responses including the identification of genes and proteins involved in the regulation, acclimation, and adaptation of processes that impact photosynthesis. The development of novel non-destructive high-throughput phenotyping techniques has been important to monitor crop photosynthetic responses to changing environmental conditions. This wealth of data is being incorporated into new modeling algorithms to predict plant growth and development under specific environmental constraints. This review gives a multi-perspective description of the impact of changing environmental conditions on photosynthetic performance and consequently plant growth by briefly highlighting how major technological advances including omics, high-throughput photosynthetic measurements, metabolic engineering, and whole plant photosynthetic modeling have helped to improve our understanding of how the photosynthetic machinery can be modified by different abiotic stresses and thus impact crop production.

Spatiotemporal impacts of climate change on food production: case study of Shaanxi Province, China

The climate change on the impact of grain production potential has significant regional differences. Researchers have studied the grain production potential of various crop combinations or focused on single crop types in a typical area; however, the regional differences of the climate change on the impact of grain production potential were neglected. This paper used the Global Agro-Ecological Zone (GAEZ 3.0) model to focus on the analysis what is the climate change on the impact of grain production potential in different geographic units (Northern Shaanxi Plateau, Guanzhong Basin, Qinba Mountain) in Shaanxi Province of China. The case showed that the precipitation (Pre) what made changes of grain production potential was the most important factor in different geographic units. The increase of Pre had a positive impact on the grain production potential in Northern Shaanxi Plateau and Guanzhong Basin. However, in Qinba Mountain, due to excessive Pre in the Qinba Mountains, the decrease of Pre had a certain positive impact on the grain production potential. The precipitation was less in the Northern Shaanxi Plateau; therefore, its major factors leading to changes of crop production were precipitation and rainfall days. The increase of the mean maximum temperature (Tmx) and the mean minimum temperature (Tmn) had a positive impact of the grain production potential in the Northern Shaanxi Plateau and Guanzhong Basin. The higher temperature had a negative impact on the grain production potential. In Qinba Mountain, the increase of the temperature has a certain negative impact on the grain production potential. It has more influence of Tmx in the Guanzhong Basin and Qinba Mountain rather than that in the Northern Shaanxi Plateau. Generally speaking, the major climatic factors leading grain production potential were Pre and Tmx in Guanzhong Basin and Qinba Mountain.

Estimating photosynthetic traits from reflectance spectra: A synthesis of spectral indices, numerical inversion, and partial least square regression

The lack of efficient means to accurately infer photosynthetic traits constrains understanding global land carbon fluxes and improving photosynthetic pathways to increase crop yield. Here, we investigated whether a hyperspectral imaging camera mounted on a mobile platform could provide the capability to help resolve these challenges, focusing on three main approaches, that is, reflectance spectra-, spectral indices-, and numerical model inversions-based partial least square regression (PLSR) to estimate photosynthetic traits from canopy hyperspectral reflectance for 11 tobacco cultivars. Results showed that PLSR with inputs of reflectance spectra or spectral indices yielded an R2 of ~0.8 for predicting V cmax and J max , higher than an R2 of ~0.6 provided by PLSR of numerical inversions. Compared with PLSR of reflectance spectra, PLSR with spectral indices exhibited a better performance for predicting V cmax (R2 = 0.84 ± 0.02, RMSE = 33.8 ± 2.2 μmol m-2  s-1 ) while a similar performance for J max (R2 = 0.80 ± 0.03, RMSE = 22.6 ± 1.6 μmol m-2  s-1 ). Further analysis on spectral resampling revealed that V cmax and J max could be predicted with ~10 spectral bands at a spectral resolution of less than 14.7 nm. These results have important implications for improving photosynthetic pathways and mapping of photosynthesis across scales.

Modeling Adaptation Strategies against Climate Change Impacts in Integrated Rice-Wheat Agricultural Production System of Pakistan

There are numerous anticipated effects of climate change (CC) on agriculture in the developing and the developed world. Pakistan is among the top ten most prone nations to CC in the world. The objective of this analysis was to quantify the economic impacts of CC on the agricultural production system and to quantify the impacts of suggested adaptation strategies at the farm level. The study was conducted in the Punjab province’s rice-wheat cropping system. For this purpose, climate modeling was carried out by using two representative concentration pathways (RCPs), i.e., RCPs 4.5 and 8.5, and five global circulation models (GCMs). The crop modeling was carried out by using the Agricultural Production Systems Simulator (APSIM) and the Decision Support System for Agrotechnology Transfer (DSSAT) crop simulation models (CSMs), which were tested on the cross-sectional data of 217 farm households collected from the seven strata in the study area. The socio-economic impacts were calculated using the Multidimensional Impact Assessment Tradeoff Analysis Model (TOA-MD). The results revealed that CC’s net economic impact using both RCPs and CSMs was negative. In both CSMs, the poverty status was higher in RCP 8.5 than in RCP 4.5. The adaptation package showed positive results in poverty reduction and improvement in the livelihood conditions of the agricultural households. The adoption rate for DSSAT was about 78%, and for APSIM, it was about 68%. The adaptation benefits observed in DSSAT were higher than in APSIM. The results showed that the suggested adaptations could have a significant impact on the resilience of the atmospheric changes. Therefore, without these adaptation measures, i.e., increase in sowing density, improved cultivars, increase in nitrogen use, and fertigation, there would be negative impacts of CC that would capitalize on livelihood and food security in the study area.

Towards a multiscale crop modelling framework for climate change adaptation assessment

Predicting the consequences of manipulating genotype (G) and agronomic management (M) on agricultural ecosystem performances under future environmental (E) conditions remains a challenge. Crop modelling has the potential to enable society to assess the efficacy of G × M technologies to mitigate and adapt crop production systems to climate change. Despite recent achievements, dedicated research to develop and improve modelling capabilities from gene to global scales is needed to provide guidance on designing G × M adaptation strategies with full consideration of their impacts on both crop productivity and ecosystem sustainability under varying climatic conditions. Opportunities to advance the multiscale crop modelling framework include representing crop genetic traits, interfacing crop models with large-scale models, improving the representation of physiological responses to climate change and management practices, closing data gaps and harnessing multisource data to improve model predictability and enable identification of emergent relationships. A fundamental challenge in multiscale prediction is the balance between process details required to assess the intervention and predictability of the system at the scales feasible to measure the impact. An advanced multiscale crop modelling framework will enable a gene-to-farm design of resilient and sustainable crop production systems under a changing climate at regional-to-global scales.