Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone

Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005-2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha(-1) applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha(-1) applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching.

Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series

A novel methodology is proposed to robustly map oil seed rape (OSR) flowering phenology from time series generated from the Copernicus Sentinel-1 (S1) and Sentinel-2 (S2) sensors. The time series are averaged at parcel level, initially for a set of 229 reference parcels for which multiple phenological observations on OSR flowering have been collected from April 21 to May 19, 2018. The set of OSR parcels is extended to a regional sample of 32,355 OSR parcels derived from a regional S2 classification. The study area comprises the northern Brandenburg and Mecklenburg-Vorpommern (N) and the southern Bavaria (S) regions in Germany. A method was developed to automatically compute peak flowering at parcel level from the S2 time signature of the Normalized Difference Yellow Index (NDYI) and from the local minimum in S1 VV polarized backscattering coefficients. Peak flowering was determined at a temporal accuracy of 1 to 4 days. A systematic flowering delay of 1 day was observed in the S1 detection compared to S2. Peak flowering differed by 12 days between the N and S. Considerable local variation was observed in the N-S parcel-level flowering gradient. Additional in-situ phenology observations at 70 Deutscher Wetterdienst (DWD) stations confirm the spatial and temporal consistency between S1 and S2 signatures and flowering phenology across both regions. Conditions during flowering strongly determine OSR yield, therefore, the capacity to continuously characterize spatially the timing of key flowering dates across large areas is key. To illustrate this, expected flowering dates were simulated assuming a single OSR variety with a 425 growing degree days (GDD) requirement to reach flowering. This GDD requirement was calculated based on parcel-level peak flowering dates and temperatures accumulated from 25-km gridded meteorological data. The correlation between simulated and S2 observed peak flowering dates still equaled 0.84 and 0.54 for the N and S respectively. These Sentinel-based parcel-level flowering parameters can be combined with weather data to support in-season predictions of OSR yield, area, and production. Our approach identified the unique temporal signatures of S1 and S2 associated with OSR flowering and can now be applied to monitor OSR phenology for parcels across the globe.

Quantifying the impact of climate change on crop yield and water footprint of rice in the Nam Oon Irrigation Project, Thailand

Northeast Thailand makes a significant contribution to fragrant and high-quality rice consumed within Thailand and exported to other countries. The majority of rice is produced in rainfed conditions while irrigation water is supplied to rice growers in the dry season. This paper quantifies the potential impact of climate change on the water footprint of rice production using the DSSAT (CERES-Rice) crop growth model for the Nam Oon Irrigation Project located in Northeast Thailand. Crop phenology data was obtained from field experiments and used to set up and validate the CERES-Rice model. The present and future water footprint of rice, the amount of water evaporated during the growing period, was calculated under current and future climatic condition for the irrigation project area. The outputs of three regional climate models (ACCESS-CSIRO-CCAM, CNRM-CM5-CSIRO-CCAM, and MPI-ESM-LR-CSIRO-CCAM) for scenarios RCP 4.5 and RCP 8.5 were downscaled using quantile mapping method. Simulation results show a considerably high increase in the water footprint of KDML-105 and RD-6 rice varieties ranging from 56.5 to 92.2% and 27.5 to 29.7%. respectively for the future period under RCP 4.5, and 71.4 to 76.5% and 27.9 to 37.6%, respectively under RCP 8.5 relative to the simulated baseline water footprint for the period 1976-2005. Conversely, the ChaiNat-1 variety shows a decrease in projected water footprint of 42.1 to 39.4% under RCP 4.5 and 38.5 to 31.7% under RCP 8.5. The results also indicate a huge increase in the future blue water footprint, which will consequently cause a high increment in the irrigation water requirement in order to meet the plant's evaporation demand. The research outcome highlights the importance of proper adaptation strategies to reduce or maintain acceptable water footprints under future climate conditions.

Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases

Disease and pest alert models are able to generate information for agrochemical applications only when needed, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models to be used in disease and pest warning systems as a function of the weather in order to improve the efficiency of chemical control of pests of the coffee tree. Thus, we correlated the infection rates with the weather variables and also calibrated and tested machine learning algorithms to predict the incidence of coffee rust, cercospora, coffee miner, and coffee borer. We used weather and field data obtained from coffee plantations in production in the southern regions of the State of Minas Gerais (SO_MG) and from the region of the Cerrado Mineiro; these crops did not receive phytosanitary treatments. The algorithms calibrated and tested for prediction were (a) Multiple linear regression (RLM); (b) K Neighbors Regressor (KNN); (c) Random Forest Regressor (RFT), and (d) Artificial Neural Networks (MLP). As dependent variables, we considered the monthly rates of coffee rust, cercospora, coffee miner, and coffee tree borer, and the weather elements were considered as independent (predictor) variables. Pearson correlation analyses were performed considering three different time periods, 1-10 d (from 1 to 10 days before the incidence evaluation), 11-20 d, and 21-30 d, and used to evaluate the unit correlations between the weather variables and infection rates of coffee diseases and pests. The models were calibrated in years of high and low yields, because the biannual variation of harvest yield of coffee beans influences the severity of the diseases. The models were compared by the Willmott's 'd', RMSE (root mean square error), and coefficient of determination (R²) indices. The result of the more accurate algorithm was specialized for the SO_MG and Cerrado Mineiro regions using the kriging method. The weather variables that showed significant correlations with coffee rust disease were maximum air temperature, number of days with relative humidity above 80%, and relative humidity. RFT was more accurate in the prediction of coffee rust, cercospora, coffee miner, and coffee borer using weather conditions. In the SO_MG, RFT showed a greater accuracy in the predictions for the Cerrado Mineiro in years of high and low yields and for all diseases. In SO_MG, the RMSE values ranged from 0.227 to 0.853 for high-yield and 0.147 and 0.827 for low-yield coffee in the coffee borer forecasting.

Modeling future water footprint of barley production in Alberta, Canada: Implications for water use and yields to 2064

Despite the perception of being one of the most agriculturally productive regions globally, crop production in Alberta, a western province of Canada, is strongly dependent on highly variable climate and water resources. We developed agro-hydrological models to assess the water footprint (WF) of barley by simulating future crop yield (Y) and consumptive water use (CWU) within the agricultural region of Alberta. The Soil and Water Assessment Tool (SWAT) was used to develop rainfed and irrigated barley Y simulation models adapted to sixty-seven and eleven counties, respectively through extensive calibration, validation, sensitivity, and uncertainty analysis. Eighteen downscaled climate projections from nine General Circulation Models (GCMs) under the Representative Concentration Pathways 2.6 and 8.5 for the 2040-2064 period were incorporated into the calibrated SWAT model. Based on the ensemble of GCMs, rainfed barley yield is projected to increase while irrigated barley is projected to remain unchanged in Alberta. Results revealed a considerable decrease (maximum 60%) in WF to 2064 relative to the simulated baseline 1985-2009 WF. Less water will also be required to produce barley in northern Alberta (rainfed barley) than southern Alberta (irrigated barley) due to reduced water consumption. The modeled WF data adjusted for water stress conditions and found a remarkable change (increase/decrease) in the irrigated counties. Overall, the research framework and the locally adapted regional model results will facilitate the development of future water policies in support of better climate adaptation strategies by providing improved WF projections.

Optimizing the phosphorus use in cotton by using CSM-CROPGRO-cotton model for semi-arid climate of Vehari-Punjab, Pakistan

Crop nutrient management is an essential component of any cropping system. With increasing concerns over environmental protection, improvement in fertilizer use efficiencies has become a prime goal in global agriculture system. Phosphorus (P) is one of the most important nutrients, and strategies are required to optimize its use in important arable crops like cotton (Gossypium hirsutum L.) that has great significance. Sustainable P use in crop production could significantly avoid environmental hazards resulting from over-P fertilization. Crop growth modeling has emerged as an effective tool to assess and predict the optimal nutrient requirements for different crops. In present study, Decision Support System for Agro-technology Transfer (DSSAT) sub-model CSM-CROPGRO-Cotton-P was evaluated to estimate the observed and simulated P use in two cotton cultivars grown at three P application rates under the semi-arid climate of southern Punjab, Pakistan. The results revealed that both the cultivars performed best at medium rate of P application (57 kg ha^-1) in terms of days to anthesis, days to maturity, seed cotton yield, total dry matter production, and harvest index during 2013 and 2014. Cultivar FH-142 performed better than MNH-886 in terms of different yield components. There was a good agreement between observed and simulated days to anthesis (0 to 1 day), days to maturity (0 to 2 days), seed cotton yield, total dry matter, and harvest index with an error of -4.4 to 15%, 12-7.5%, and 13-9.5% in MNH-886 and for FH-142, 4-16%, 19-11%, and 16-8.3% for growing years 2013 and 2014, respectively. CROPGRO-Cotton-P would be a useful tool to forecast cotton yield under different levels of P in cotton production system of the semi-arid climate of Southern Punjab.

Using crop modeling to evaluate the impacts of climate change on wheat in southeastern turkey

The extreme temperatures and uneven distribution of rainfall associated with climate change are expected to affect agricultural productivity and food security. A study was conducted to evaluate the impact of climate change on wheat in southeastern regions of Turkey. The CERES-wheat crop simulation model was calibrated and evaluated with data from eight surveyed farms. The four farms were used for calibration and four for evaluation. Climate change scenarios were developed for the middle (2036-2065) and late 21st century (2066-2095) under representative concentration pathways (RCPs 4.5 and 8.5) for study sites in Islahiye and Nurdagi. Model calibration results showed a good agreement between observed and simulated yield with only a 1 to 11% range of error. The model evaluation results showed good fit between observed and simulated values of all parameters with % error ranged from 0.51 to 13.3%. Future climate change projections showed that maximum temperature (Tmax) will increase between 1.6 °C (RCP4.5) and 2.3 °C (RCP8.5), while minimum temperature (Tmin) will increase between 1.0 °C (RCP4.5) and 1.5 °C (RCP8.5) for mid-century. At the end of the century, Tmax is projected to increase from 2 °C (RCP4.5) to 4 °C (RCP8.5) and Tmin from 1.3 °C (RCP4.5) to 3.1 °C (RCP8.5). Climate change impacts results showed that future rise in temperature will reduce wheat yield by 16.3% in mid-century and 16.8% at the end of the century at Islahiye and for Nurdagi, while 13.0% in mid and 14.4% end of the century. The use of climate and crop modeling technique provides useful information in evaluating the climate change impacts and may assist stakeholders to make decisions to overcome the negative impacts in the near and long term.

The role of climate in the trend and variability of Ethiopia's cereal crop yields

Food security has been and will continue to be a major challenge in Ethiopia. The country's smallholder, rainfed agriculture renders its food production system extremely vulnerable to climate variability and extremes. In this study, we investigate the impact of past climate variability and change on the yields of five major cereal crops in Ethiopia-barley, maize, millet, sorghum, and wheat-during the period 1979-2014 using the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. The model is calibrated at both the site and agroecological-zone scales. At the sites studied, the model results suggest that climate in the past four decades may have contributed to an increasing trend in maize yield, a decreasing trend in wheat yield, and no clear trend in the yields of barley and millet; cereal crop yield is positively correlated with growing season solar radiation and temperature, but negatively correlated with growing season precipitation. For modeled cereal crops across the nation during the study period, yield in western Ethiopia is positively correlated with solar radiation and day time temperature; in the eastern and southeastern Ethiopia where water is a limiting factor for growth, yield is positively correlated with precipitation but negatively correlated with solar radiation and both day time and night time temperature. The national average of simulated yields of most crops (except maize) showed an overall decreasing (although not statistically significant) trend induced by past climate variability and changes. Over a large portion of the highly productive areas where there is a negative correlation between yield and temperature, yield is simulated to have significantly decreased over the past four decades, an indication of adverse climate impact in the past and potential food security concern in the future.

Drought patterns: their spatiotemporal variability and impacts on maize production in Limpopo province, South Africa

Due to global climate change, droughts are likely to become more frequent and more severe in many regions such as in South Africa. In Limpopo, observed high climate variability and projected future climate change will likely increase future maize production risks. This paper evaluates drought patterns in Limpopo at two representative sites. We studied how drought patterns are projected to change under future climatic conditions as an important step in identifying adaptation measures (e.g., breeding maize ideotypes resilient to future conditions). Thirty-year time horizons were analyzed, considering three emission scenarios and five global climate models. We applied the WOFOST crop model to simulate maize crop growth and yield formation over South Africa's summer season. We considered three different crop emergence dates. Drought indices indicated that mainly in the scenario SSP5-8.5 (2051-2080), Univen and Syferkuil will experience worsened drought conditions (DC) in the future. Maize yield tends to decline and future changes in the emergence date seem to impact yield significantly. A possible alternative is to delay sowing date to November or December to reduce the potential yield losses. The grain filling period tends to decrease in the future, and a decrease in the duration of the growth cycle is very likely. Combinations of changed sowing time with more drought tolerant maize cultivars having a longer post-anthesis phase will likely reduce the potential negative impact of climate change on maize.

High-biomass C4 grasses-Filling the yield gap

A significant increase in agricultural productivity will be required by 2050 to meet the needs of an expanding and rapidly developing world population, without allocating more land and water resources to agriculture, and despite slowing rates of grain yield improvement. This review examines the proposition that high-biomass C₄ grasses could help fill the yield gap. High-biomass C₄ grasses exhibit high yield due to C₄ photosynthesis, long growth duration, and efficient capture and utilization of light, water, and nutrients. These C₄ grasses exhibit high levels of drought tolerance during their long vegetative growth phase ideal for crops grown in water-limited regions of agricultural production. The stems of some high-biomass C₄ grasses can accumulate high levels of non-structural carbohydrates that could be engineered to enhance biomass yield and utility as feedstocks for animals and biofuels production. The regulatory pathway that delays flowering of high-biomass C₄ grasses in long days has been elucidated enabling production and deployment of hybrids. Crop and landscape-scale modeling predict that utilization of high-biomass C₄ grass crops on land and in regions where water resources limit grain crop yield could increase agricultural productivity.

Global implications of regional grain production through virtual water trade

Crop yields (Y) and virtual water content (VWC) of agricultural production are affected by climate variability and change, and are highly dependent on geographical location, crop type, specific planting and harvesting practice, soil property and moisture, hydro-geologic and climate conditions. This paper assesses and analyzes historical (1985-2009) and future (2040-2064) Y and VWC of three cereal crops (i.e., wheat, barley, and canola) with high spatial resolution in the highly intensive agricultural region of Alberta, Canada, using the Soil and Water Assessment Tool (SWAT). A calibrated and validated SWAT hydrological model is used to supplement agricultural (rainfed and irrigation) models to simulate Y and crop evapotranspiration (ET) at the sub-basin scales. The downscaled climate projections from nine General Climate Models (GCMs) for RCP 2.6 and RCP 8.5 emission scenarios are fed into the calibrated SWAT model. Results from an ensemble average of GCMs show that Y and VWC are projected to change drastically under both RCPs. The trade (export-import) of wheat grain from Alberta to more than a hundred countries around the globe led to the annual saving of ~5 billion m³ of virtual water (VW) during 1996-2005. Based on the weighted average of VWC for both rainfed and irrigated conditions, future population and consumption, our projections reveal an annual average export potential of ~138 billion m³ of VW through the flow of these cereal crops in the form of both grain and other processed foods. This amount is expected to outweigh the total historical provincial water yield of 66 billion m³ and counts for 47% of total historical precipitation and 61% of total historical actual ET. The research outcome highlights the importance of local high-resolution inputs in regional modeling and understanding the local to global water-food trade policy for sustainable agriculture.

Time-series spectral dataset for croplands in France (2006-2017)

Decadal time-series derived from satellite observations are useful for discriminating crops and identifying crop succession at national and regional scales. However, use of these data for crop modeling is challenged by the presence of mixed pixels due to the coarse spatial resolution of these data, which influences model accuracy, and the scarcity of field data over the decadal period necessary to calibrate and validate the model. For this data article, cloud-free satellite “Vegetation Indices 16-Day Global 250 m” Terra (MOD13Q1) and Aqua (MYD13Q1) products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS), as well as the Land Parcel Information System (LPIS) vector field data, were collected throughout France for the 12-year period from 2006 to the end of 2017. A GIS workflow was developed using R software to combine the MOD13Q1 and MYD13Q1 products, and then to select “pure” MODIS pixels located within single-crop parcels over the entire period. As a result, a dataset for 21,129 reference plots (corresponding to “pure” pixels) was generated that contained a spectral time-series (red band, near-infrared band, Normalized Difference Vegetation Index (NDVI), and Enhanced Vegetation Index (EVI)) and the associated annual crop type with an 8-day time step over the period. This dataset can be used to develop new classification methods based on time-series analysis using deep learning, and to monitor and predict crop succession.

Simulated dataset of corn response to nitrogen over thousands of fields and multiple years in Illinois

Nitrogen (N) fertilizer recommendations for corn (Zea mays L.) in the US Midwest have been a puzzle for several decades, without agreement among stakeholders for which methodology is the best to balance environmental and economic outcomes. Part of the reason is the lack of long-term data of crop responses to N over multiple fields since trial data is often limited in the number of soils and years it can explore. To overcome this limitation, we designed an analytical platform based on crop simulations run over millions of farming scenarios over extensive geographies. The database was calibrated and validated using data from more than four hundred trials in the region. This dataset can have an important role for research and education in N management, machine leaching, and environmental policy analysis. The calibration and validation procedure provides a framework for future gridded crop model studies. We describe dataset characteristics and provide thorough descriptions of the model setup.

The use of remote sensing to derive maize sowing dates for large-scale crop yield simulations

One of the major sources of uncertainty in large-scale crop modeling is the lack of information capturing the spatiotemporal variability of crop sowing dates. Remote sensing can contribute to reducing such uncertainties by providing essential spatial and temporal information to crop models and improving the accuracy of yield predictions. However, little is known about the impacts of the differences in crop sowing dates estimated by using remote sensing (RS) and other established methods, the uncertainties introduced by the thresholds used in these methods, and the sensitivity of simulated crop yields to these uncertainties in crop sowing dates. In the present study, we performed a systematic sensitivity analysis using various scenarios. The LINTUL-5 crop model implemented in the SIMPLACE modeling platform was applied during the period 2001-2016 to simulate maize yields across four provinces in South Africa using previously defined scenarios of sowing dates. As expected, the selected methodology and the selected threshold considerably influenced the estimated sowing dates (up to 51 days) and resulted in differences in the long-term mean maize yield reaching up to 1.7 t ha^-1 (48% of the mean yield) at the province level. Using RS-derived sowing date estimations resulted in a better representation of the yield variability in space and time since the use of RS information not only relies on precipitation but also captures the impacts of socioeconomic factors on the sowing decision, particularly for smallholder farmers. The model was not able to reproduce the observed yield anomalies in Free State (Pearson correlation coefficient: 0.16 to 0.23) and Mpumalanga (Pearson correlation coefficient: 0.11 to 0.18) in South Africa when using fixed and precipitation rule-based sowing date estimations. Further research with high-resolution climate and soil data and ground-based observations is required to better understand the sources of the uncertainties in RS information and to test whether the results presented herein can be generalized among crop models with different levels of complexity and across distinct field crops.

In-season weather data provide reliable yield estimates of maize and soybean in the US central Corn Belt

Weather conditions regulate the growth and yield of crops, especially in rain-fed agricultural systems. This study evaluated the use and relative importance of readily available weather data to develop yield estimation models for maize and soybean in the US central Corn Belt. Total rainfall (Rain), average air temperature (Tavg), and the difference between maximum and minimum air temperature (Tdiff) at weekly, biweekly, and monthly timescales from May to August were used to estimate county-level maize and soybean grain yields for Iowa, Illinois, Indiana, and Minnesota. Step-wise multiple linear regression (MLR), general additive (GAM), and support vector machine (SVM) models were trained with Rain, Tavg, and with/without Tdiff. For the total study area and at individual state level, SVM outperformed other models at all temporal levels for both maize and soybean. For maize, Tavg and Tdiff during July and August, and Rain during June and July, were relatively more important whereas for soybean, Tavg in June and Tdiff and Rain during August were more important. The SVM model with weekly Rain and Tavg estimated the overall maize yield with a root mean square error (RMSE) of 591 kg ha-1 (4.9% nRMSE) and soybean yield with a RMSE of 205 kg ha-1 (5.5% nRMSE). Inclusion of Tdiff in the model considerably improved yield estimation for both crops; however, the magnitude of improvement varied with the model and temporal level of weather data. This study shows the relative importance of weather variables and reliable yield estimation of maize and soybean from readily available weather data to develop a decision support tool in the US central Corn Belt.

Impacts of spring freeze events on a perennial tree fruit crop across the central and eastern USA

This study uses a crop simulation model driven by 40 years (1981-2020) of daily gridded meteorological data from PRISM to assess the impacts of spring freeze events on cherry trees, a representative temperate perennial tree-fruit crop, across six regions of the central and eastern USA: the Northern and Southern Great Plains (NGP, SGP), Upper Midwest (UMW), Ohio Valley (OHV), New York-Pennsylvania (NY-PA), and Virginia-North Carolina (VA-NC). Freeze damage exhibits a clear latitudinal gradient, with damage frequency and severity decreasing from south to north. The most frequent and severe damage occurs in the SGP, followed by VA-NC, while the least is observed in the UMW and NY-PA. Damage frequency decreases as phenological stage advances, with the first two vegetative stages being the most affected. False spring events, defined as early side-green onset followed by freeze damage, mirror this spatial pattern and are more closely linked to the timing of side-green dates than to freeze-damage frequency. Trends in damage day frequency and severity show notable longitudinal variability, with decreasing trends in the lower OHV flanked by increasing trends in the SGP and VA-NC. Decreasing trends are also observed in northern parts of the UMW and NGP, though significant trends are limited to small areas. These patterns reflect the interplay between spring warm-up timing, phenological development, and seasonal vulnerability, modulated by sub-freezing temperature frequency and severity. The findings highlight the complexity of overwintering crops' responses to climate variability and the need for caution in assessing cold injury risks under future climate scenarios.

Integration of Crop Growth Models and Genomic Prediction

Crop growth models (CGMs) consist of multiple equations that represent physiological processes of plants and simulate crop growth dynamically given environmental inputs. Because parameters of CGMs are often genotype-specific, gene effects can be related to environmental inputs through CGMs. Thus, CGMs are attractive tools for predicting genotype by environment (G×E) interactions. This chapter reviews CGMs, genetic analyses using these models, and the status of studies that integrate genomic prediction with CGMs. Examples of CGM analyses are also provided.

Impact of calibrating a low-cost capacitance-based soil moisture sensor on AquaCrop model performance

Sensor data and agro-hydrological modeling have been combined to improve irrigation management. Crop water models simulating crop growth and production in response to the soil-water environment need to be parsimonious in terms of structure, inputs and parameters to be applied in data scarce regions. Irrigation management using soil moisture sensors requires them to be site-calibrated, low-cost, and maintainable. Therefore, there is a need for parsimonious crop modeling combined with low-cost soil moisture sensing without losing predictive capability. This study calibrated the low-cost capacitance-based Spectrum Inc. SM100 soil moisture sensor using multiple least squares and machine learning models, with both laboratory and field data. The best calibration technique, field-based piece-wise linear regression (calibration r2 = 0.76, RMSE = 3.13 %, validation r2 = 0.67, RMSE = 4.57 %), was used to study the effect of sensor calibration on the performance of the FAO AquaCrop Open Source (AquaCrop-OS) model by calibrating its soil hydraulic parameters. This approach was tested during the wheat cropping season in 2018, in Kanpur (India), in the Indo-Gangetic plains, resulting in some best practices regarding sensor calibration being recommended. The soil moisture sensor was calibrated best in field conditions against a secondary standard sensor (UGT GmbH. SMT100) taken as a reference (r2 = 0.67, RMSE = 4.57 %), followed by laboratory calibration against gravimetric soil moisture using the dry-down (r2 = 0.66, RMSE = 5.26 %) and wet-up curves respectively (r2 = 0.62, RMSE = 6.29 %). Moreover, model overfitting with machine learning algorithms led to poor field validation performance. The soil moisture simulation of AquaCrop-OS improved significantly by incorporating raw reference sensor and calibrated low-cost sensor data. There were non-significant impacts on biomass simulation, but water productivity improved significantly. Notably, using raw low-cost sensor data to calibrate AquaCrop led to poorer performances than using the literature. Hence using literature values could save sensor costs without compromising model performance if sensor calibration was not possible. The results suggest the essentiality of calibrating low-cost soil moisture sensors for crop modeling calibration to improve crop water productivity.

Climate change enhances stability of wheat-flowering-date

The stability of winter wheat-flowering-date is crucial for ensuring consistent and robust crop performance across diverse climatic conditions. However, the impact of climate change on wheat-flowering-dates remains uncertain. This study aims to elucidate the influence of climate change on wheat-flowering-dates, predict how projected future climate conditions will affect flowering date stability, and identify the most stable wheat genotypes in the study region. We applied a multi-locus genotype-based (MLG-based) model for simulating wheat-flowering-dates, which we calibrated and evaluated using observed data from the Northern China winter wheat region (NCWWR). This MLG-based model was employed to project flowering dates under different climate scenarios. The simulated flowering dates were then used to assess the stability of flowering dates under varying allelic combinations in projected climatic conditions. Our MLG-based model effectively simulated flowering dates, with a root mean square error (RMSE) of 2.3 days, explaining approximately 88.5 % of the genotypic variation in flowering dates among 100 wheat genotypes. We found that, in comparison to the baseline climate, wheat-flowering-dates are expected to shift earlier within the target sowing window by approximately 11 and 14 days by 2050 under the Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 climate scenarios, respectively. Furthermore, our analysis revealed that wheat-flowering-date stability is likely to be further strengthened under projected climate scenarios due to early flowering trends. Ultimately, we demonstrate that the combination of Vrn and Ppd genes, rather than individual Vrn or Ppd genes, plays a critical role in wheat-flowering-date stability. Our results suggest that the combination of Ppd-D1a with winter genotypes carrying the vrn-D1 allele significantly contributes to flowering date stability under current and projected climate scenarios. These findings provide valuable insights for wheat breeders and producers under future climatic conditions.

Climate trends and soybean production since 1970 in Mississippi: Empirical evidence from ARDL model

Studying historical response of crops to weather conditions at a finer scale is essential for devising agricultural strategies tailored to expected climate changes. However, determining the relationship between crop and climate in Mississippi (MS) remains elusive. Therefore, this research attempted to i) estimate climate trends between 1970 and 2020 in MS during the soybean growing season (SGS) using the Mann-Kendall and Sen slope method, ii) calculate the impact of climate change on soybean yield using an auto-regressive distributive lag (ARDL) econometric model, and iii) identify the most critical months from a crop-climate perspective by generating a correlation between the detrended yield and the monthly average for each climatic variable. Specific variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), carbon dioxide emissions (CO2), and relative humidity (RH). All required diagnostic-tests i.e., pre-analysis, post-analysis, model-sensitivity, and assessing the models' goodness-of-fit were performed and statistical standards were met. A positive trend in Tmin (+0.25 °C/decade), and a negative trend in DTR (-0.18 °C/decade) was found. Although Tmax, PT, and RH showed non-significant trends, numerical changes were noted as +0.11 °C/decade, +3.03 mm/decade, and -0.06 %/decade, respectively. Furthermore, soybean yield was positively correlated with Tmin (in June and September), PT (in July and August), and RH (in July), but negatively correlated with Tmax (in July and August) and DTR (in June, July, and August). Soybean yield was observed to be significantly reduced by 18.11 % over the long-term and by 5.51 % over the short-term for every 1 °C increase in Tmax. With every unit increase in Tmin and CO2 emissions, the yield of soybeans increased significantly by 7.76 % and 3.04 %, respectively. Altogether, soybeans in MS exhibited variable sensitivity to short- and long-terms climatic changes. The results highlight the importance of testing climate-resilient agronomic practices and cultivars that encompass asymmetric sensitivities in response to climatic conditions of MS.

Agronomic monsoon onset definitions to support planting decisions for rainfed rice in Bangladesh

The usability gaps between climate information producers and users have always been an issue in climate services. This study aims to tackle the gap for rice farmers in Bangladesh by exploring the potential value of tailored agronomic monsoon onset definitions. Summer aman rice is primarily cultivated under rainfed conditions, and farmers rely largely on monsoon rainfall and its onset for crop establishment. However, farmers' perception of the arrival of sufficient rains does not necessarily coincide with meteorological definitions of monsoon onset. Therefore, localized agronomic definitions of monsoon onset need to be developed and evaluated to advance in the targeted actionable climate forecast. We analyzed historical daily rainfall from four locations across a north-south gradient in Bangladesh and defined dynamic definitions of monsoon onset based on a set of local parameters. The agronomic onset definition was evaluated in terms of attainable yields simulated by a rice simulation model compared to results obtained using conventional meteorological onset parameters defined by the amount of rainfall received and static onset dates. Our results show that average simulated yields increase up to 7 - 9% and probabilities of getting lower yields are reduced when the year-to-year varying dynamic onset is used over the two drier locations under fully rainfed conditions. It is mainly due to earlier transplanting dates, avoiding the impact of drought experienced with early monsoon demise. However, no yield increases are observed over the two wetter locations. This study shows the potential benefits of generating "localized and translated" climate predictions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10584-024-03736-z.

Assessing the sensitivity of alfalfa yield potential to climate impact under future scenarios in Iran

Alfalfa is a major forage crop in Iran. To quantify the impact of climate change on its yield and water application for irrigation in Iran, the SSM-iCrop2 simulation model and two GCMs of IPSL and HadGEM were used under RCP4.5 and RCP8.5 for the 2050s. Despite increased temperatures, alfalfa forage yield will increase in most of the regions across the country due to acceleration of spring regrowth, a higher number of cuttings, increased incident and received photosynthetically active radiation because of increased growing season length due to increased temperatures, and positive effect of CO₂ on photosynthesis and radiation use efficiency. Changes in climatic conditions have had a significant impact on alfalfa net irrigation water, and the sum of net irrigation water has a direct relationship with alfalfa yield. Due to increased temperature, changes in rainfall, and improved concentration of atmospheric CO₂, the forage yield of alfalfa will fluctuate highly under all climatic scenarios. The highest increase and decrease in the average yield using the HadGEM model under RCP8.5 was 32 and - 33%, respectively. The average net irrigation water of alfalfa increased by 36% in the HadGEM model under RCP8.5 and decreased by - 41% in the IPSL model under RCP8.5. Therefore, to improve alfalfa yield in Iran in the future, strategies compatible such as high temperature-tolerant cultivars may be the most reasonable approaches.

Maladaptation in cereal crop landraces following a soot-producing climate catastrophe

Aerosol-producing global catastrophes such as nuclear war, super-volcano eruption, or asteroid strike, although rare, pose a serious threat to human survival. Light-absorbing aerosols would sharply reduce temperature and solar radiation reaching the earth's surface, decreasing crop productivity including for locally adapted traditional crop varieties, i.e. landraces. Here, we test post-catastrophic climate impacts on barley, maize, rice, and sorghum, four crops with extensive landrace cultivation, under a range of nuclear war scenarios that differ in the amount of black carbon aerosol (soot) injected into the climate model. We used a crop growth model to estimate gradients of environmental stressors that drive local adaptation. We then fit genotype environment associations using high density genomic markers with gradient forest offset (GF offset) methods and predicted maladaptation through time. As a validation, we found that our GF models successfully predicted local adaptation of maize landraces in multiple common gardens across Mexico. We found strong concordance between GF offset and disruptions in climate, and landraces of all tested crop species were predicted to be the most maladapted across space and time where soot-induced climate change was the greatest. We further used our GF models to identify landrace varieties best matched to specific post-catastrophic conditions, indicating potential substitutions for agricultural resilience. We found the best landrace genotype was often far away or in another nation, though countries with more climatic diversity had better within-country substitutions. Our results highlight that a soot-producing catastrophe would result in the global maladaptation of landraces and suggest that current landrace adaptive diversity is insufficient for agricultural resilience in the case of the soot scenarios with the greatest change to climate.

Evaluation of CRU-JRA gridded meteorological dataset for modeling of wheat production systems in Iran

Meteorological variables are essential inputs for agricultural simulation models and the lack of measured data is a big challenge for the application of these models in many agricultural zones. Studies indicated that gridded meteorological datasets can be proper replacements for measured data. This paper aimed to examine a new gridded meteorological dataset namely CRU-JRA for crop modeling intents. The CRU-JRA is a 6-hourly dataset with a spatial resolution of 0.5° × 0.5° that was primarily constructed for modeling purposes. The CERES-Wheat model in the Decision Support System for Agrotechnology Transfer (DSSAT) was used for the simulation of irrigated and rainfed wheat production systems in Iran. Results showed that the CRU-JRA maximum and minimum temperature values had a relatively fine accuracy with a normalized root mean square error (NRMSE) of 14% for the simulated grain yield. The performance of the CRU-JRA solar radiation values for the simulation of grain yield was similar with a NRMSE of 14.4%. The weakest performance was found for the CRU-JRA precipitation values with a NRMSE of 18.9%. Overall, the CRU-JRA dataset performed comparatively acceptable and similar to existing gridded meteorological datasets for crop modeling purposes in the study area, however further calibrations can improve the accuracy of the next versions of this dataset. More research is necessary for the investigation of the CRU-JRA dataset for agricultural modeling purposes across diverse climates.

Calibration, testing and application of the AquaCrop model for bean crop under irrigation regimes

Crop growth simulation models relate the soil-water-plant-atmosphere components to estimate the development and yield of plants in different scenarios, enabling the identification of efficient irrigation strategies. The aim of this study was to calibrate crop coefficients for a common bean cultivar (IAPAR 57) and assess the AquaCrop model's efficacy in simulating crop growth under different irrigation regimes (T0 - non-irrigated, T1-fully irrigated, and T2-deficit irrigated) and sowing dates (S1-March 21, S2-April 24, and S3-August 23). Successful calibration was achieved for crop seasons with suitable temperatures to crop growth (S1 and S3). However, during periods with suboptimal temperatures (April 24 season), coupled with reduced irrigation supply (T0 and T2), the AquaCrop model did not appropriately account for the combined effects of thermal and water stresses. Despite adjustments to stress coefficients, this led to an overestimation of crop growth and yield. In long-term simulations, the model successfully replicated the variability of crop water availability over cropping seasons, reflecting the impact of precipitation variations. It recommended irrigation strategies for the study region (irrigate at depletion of 120 and 170% of readily available water for sowing on March 21 and August 24, respectively) to achieve high crop yield (> 2,769 kg ha-1) and water productivity (1,050 to 1,445 kg m-3) with minimal application depths (< 150 mm). While acknowledging the need for improvements in thermal stress calculations, the AquaCrop model demonstrates promising utility in studies and applications where water availability significantly influences crop production.