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The trend of population growth and the consequent need to increase agricultural production in order to provide for the necessary food is critical topic nowadays. To account for the risks and damage caused by changes in weather and climate constrains, it is important to know how these changes affect the agricultural productivity. Wheat is the world's most widely grown crop and is directly related to worldwide food security issues. As a result, it has always been of interest to researchers as a strategic crop. In this study, the correlation of the Water Requirement Satisfaction Index in six growing periods of rainfed wheat with its yield was examined and found that the highest correlation is seen over the entire growing season. Based on statistical distributions and copula functions, the sensitivity and rainfed wheat yield to the Water Requirement Satisfaction Index was examined. Clayton Copula was selected based on AIC and RMSE evaluation indices. The probability of having crop losses is around 50%, in general. The results showed that the yield risk of rainfed wheat in the Tabriz region drops to 46% under low climatic risk conditions and reaches to 98% under medium and high climate risk conditions. The results allow farmers and stakeholders to better plan and manage food security by knowing the changes in rainfed wheat yield depending on weather conditions. By using different options under different conditions such as wet events, it is also possible to work towards higher yield amounts of rainfed wheat and other rainfed crops. • Water Requirement Satisfaction Index determines changes in rainfed wheat yield. • Copulas are efficient in modelling the interplay between rainfed wheat yield and WRSI. • The yield response to WRSI values is not linear. • Wheat yield vulnerability is amplified under medium-high climate risk conditions. • WRSI shows potential as a critical early warning indicator for farmers. [ABSTRACT FROM AUTHOR]

Rainfed cereal yields show high variability depending on the varying conditions of concurrent factors during the crop year. Among them, hydrometeorological factors such as maximum temperature, rainfall, and notably, soil moisture, strongly affect crop production, but the greatest source of uncertainty on yield estimates stems from their interaction. This is of special interest in water-limited regions where climate change is expected to affect more intensely, but also in others where water is increasingly limited. Despite the highly non-linear nature of the interactions, simple statistic models such as multilinear regression accurately explore a notable proportion of the variability of cereal yields. To describe the impacts behind interactions, we perform stepwise multilinear regression of meteorological factors derived from E-OBSv23 database and soil moisture from ERA5-Land against annual wheat and barley yields for the period 1981–2019 in the main cereal regions of Spain. The multivariate approach characterizes the temporal shifts of factors' influence. Beyond the temporal shifts on the synchrony of the factors, some of them tend to co-dominate the impact during the critical period of crop development, with soil moisture exceeding all others in relevance. Multivariate analysis fosters discussion about the impact of the choice of variables on the model fit, as well as on the pertinence of monthly and annual scales for explorative and predictive purposes. Monthly models perform particularly well during the critical period of growth and reproduction of crops and consistently better than univariate estimates. The annual model built using the data of the months of maximum impact of key variables outperforms the model at a monthly scale, which underlines the decisive role of the critical period. Similarly, results highlight the worth of parsimony in modelling. Soil moisture stands out as the principal concurrent variable to improve yield estimates from environmental data, which governs yields of rainfed water-limited croplands. • Soil moisture stands out from environmental factors on influence on cereal yields. • Stepwise multilinear regression of less colinear factors largely explains yield variability. • Using key variables in the critical period of crop sensitivity improves model fit. • Soil moisture notably improves model estimates both at monthly and yearly scales. [ABSTRACT FROM AUTHOR]

Water productivity (WP), a performance indicator, defines if the agricultural production system uses the resources competently. Spatial information on crop WP is fundamental to knowing where a gap in WP exists along with underlying reasons within the gaps, where a crop production system performs well, and where developments are still possible to enhance the water-productive performance. This study proposes to assess spatial wheat WP in drought-prone northwestern Bangladesh. Both primary and secondary data, such as Sentinel 2 MSI, Landsat 8 OLI satellite images, actual evapotranspiration (ET a) from Lysimeter, and wheat farmers' field data over the study area, were collected to accomplish this study. The wheat-cultivated fields were delineated first using Sentinel 2 MSI data during the 2018 wheat growing season. WP parameters, namely crop yield and ET a were estimated using Landsat 8 satellite data. Based on yield data from the farmer's field and normalized difference vegetation index (NDVI) values generated from the same fields, a linear regression model was developed. This model was applied to obtain crop yield maps from the NDVI maps of the study area. A remote sensing-based well-tested, robust algorithm surface energy balance algorithm for land (SEBAL) was used to estimate ET a using Landsat 8 images and local ground information. Cloud-free single-date images corresponding to wheat growth stages such as initial, development, mid-season, and late-season were used to estimate seasonal ET a. SEBAL-estimated single date ET a was validated by newly developed (under this study) Lysimeter-measured ET a. Wheat maps were then masked to get crop-specific yield and ET a maps. Finally, the WP maps of wheat were produced by the mathematical operation of the yield and ET a raster maps. The NDVI- crop yield (Y) models were calibrated and validated, which shows an acceptable degree of dispersion between observed and estimated values. Thakurgaon and Panchagorh districts (second administrative tier) are higher wheat-yielding districts in the study area. The average wheat-yield of the study area was 3.51 t ha−1, which is close to the department of agricultural extension (officially) reported data. In the case of the denominator (ET a) of WP estimation, results show that SEBAL-estimated wheat ET a on the image dates is close to ET a estimated by the Lysimeter. The average seasonal ET a for wheat is found as 253 mm. The estimated highest and lowest ET a demands are found in Thakurgaon and Panchagorh districts, respectively. The estimated WP of wheat ranged between 1.33 and 1.46 kg m−3, with an average of 1.39 kg m−3 among the eight study districts. Panchagorh and Thakurgaon districts are the highest water productive, whereas Gaibandha and Nilphamari show the minimum level of the WP. Among the Y-ET a , WP-ET a , and WP-Y relationships, WP-Y shows the highest correlation (r2 =0.73), as the higher WP observed mainly due to high yielding capacity. Besides, suitable soil characteristics and weather conditions favor wheat WP in those areas. This study identifies high-performing bright and low-performing hot spots of WP across the northwest of Bangladesh. Finally, stakeholders and decision-makers can use the information to define priority areas, set goals for improvement, and justify the type of investment or measures for improving water-productive agriculture in Bangladesh. • SEBAL algorithm was used to estimate actual evapotranspiration (ET a) from Landsat 8. • Estimated ET a was validated by a Newly developed Lysimeter-ET a. • Wheat yield (Y) was estimated with a linear regression model. • Estimated spatial WP fall between the ranges of estimates of global wheat WP. • This study identifies high-performing bright and low-performing hot spots of WP. [ABSTRACT FROM AUTHOR]

Irrigation plays an important role in crop yield production in arid and semi-arid regions. However, irrigation effects have not been well addressed in the application of crop models at a regional scale due to limited data availability, which constrains the reliability and accuracy of simulation results. Assimilating remote sensing information into crop models can provide a viable approach to reduce associated uncertainties. In this study, regional irrigation data for winter wheat (Triticum aestivum L.) grown on the Loess Plateau was used to calibrate and validate the ChinaAgrosys (China Agricultural System) crop model at site and regional scales. Remote sensing data was then assimilated into the ChinaAgrosys crop model under four assimilation schemes. Two remotely sensed assimilation state variables (i.e., LAI and NDVI) and two assimilation algorithms (i.e., PSO (Partical swarm optimization) and SCE-UA (Shuffled complex evolution)) were considered. During the winter wheat growing season on the Loess Plateau, 30.6% of the wheat production area was irrigated once, 6.7% was irrigated two times, 3.7% was irrigated three times, and the remaining wheat area was rainfed. The R2 values between maturity date, LAI, and yield simulated by the ChinaAgrosys crop model and observations at 21 agrometeorological stations on the Loess Plateau were greater than 0.73, 0.44, and 0.60, respectively, during 2010–2015. The accuracy and spatial heterogeneity of winter wheat yield estimation were effectively improved by assimilating remote sensing data into the ChinaAgrosys crop model based on regional irrigation data. Under the four assimilation schemes, the combination of PSO+NDVI produced the highest accuracy for yield estimation in Hongtong county (92.8%), followed by SCE-UA+NDVI (92.0%). Our results demonstrated the importance of accounting for the spatial heterogeneity of water availability when applying a crop model in arid and semi-arid regions. Additionally, our analysis regarding different assimilation state variables and algorithms indicated that both simulation accuracy and calculation efficiency should be considered when assimilating remote sensing data into a crop model for simulating crop growth at regional scales. • Spatial heterogeneity of regional irrigation data was estimated from yield. • ChinaAgrosys crop model was applied at a regional scale using irrigation data. • Four schemes to assimilate remote sensing data into ChinaAgrosys were tested. • Simulation results were improved by accounting for spatial heterogeneity of irrigation data. • Simulation results were improved by assimilating remotely sensed LAI and NDVI. [ABSTRACT FROM AUTHOR]

Drought is a natural hazard that may decrease agricultural production. To investigate crop growth and yield responses to drought conditions are vital for drought prevention during crop growth periods. This study aims to analyze the impacts of meteorological and agricultural droughts on wheat/maize yields from multiple perspectives and to select the key parameters which describe the best relationship between crop yield and drought indices. Using standardized precipitation evapotranspiration index (SPEI) and soil moisture deficit index (SMDI) at 1- to 9-month timescales, the drought characteristics of different crop growth periods at the selected 98 sites in different subregions were analyzed. DSSAT-CERES-Wheat/Maize models were used to simulate the leaf area index (LAI), biomass and yield of spring wheat, spring and summer maize over 1961 − 2018. The relationships between yield related factors and SPEI/SMDI 0–10 at different timescales were investigated using Pearson correlation. The key timescale and growth period which showed the best correlations between crop yield/growth and SPEI/SMDI were determined and used to obtain the yield/growth equations using multivariable linear regression. The results showed that: (1) The temporal variations of SPEI and SMDI 0–10 differed with different timescales, months and subregions. DSSAT-CERES generally performed well in simulating growth and yields of wheat and maize over 1961–2018. (2) For spring wheat, the correlations of yield and SMDI were highest at 3-month timescale in July, at 5-month timescale in July and at 3-month timescale in June in subregions I, II and IV, respectively. For spring maize, in subregion I, yield correlated with 1-month SPEI in June best), while yield was correlated largest with 4-month SPEI in August in subregion III and IV. For summer maize, the best correlations occurred in August between yield and 4-month SPEI. Therefore, different crop had varying key parameters for drought prevention measures. (3) The multivariable linear equations described yield/growth vs. drought indices relationship well for different crops. The results are referable for providing measures for agricultural production practice under drought. • Agricultural drought index SMDI (soil moisture deficit index) can better identify drought in spring wheat growth period, and meteorological drought index SPEI (standardized precipitation evapotranspiration index) has a greater correlation with maize yield. • The continuous drought during jointing-tasseling period has the greatest influence on the spring maize yield. • For summer maize, drought had the greatest effect on yield during the anthesis period, the significant positive correlation between yield SPEI and at the 4-month timescale in August was the best. • The linear slopes of SMDI has greater influence on the linear slopes of yield related factors of spring wheat than that of SPEI，but the opposite was true for maize. [ABSTRACT FROM AUTHOR]

Excessive use of water resources is threatening agriculture, other business and populations in China, India and USA, and crop production is expected to play a critical role in the sustainable use of water. In China, plastic mulching is widely employed in crop production, but its impact on agricultural water footprint has not been properly described. A meta-analysis was performed to examine how plastic mulching influences volumetric water availability (VWA), global stress-weighted water footprint per unit of output energy (WFo), and per unit of net economic return in crop production (WFe). Data extracted from 394 published studies in maize (Zea mays L.), wheat (Triticum aestivum L.), and potato (Solanum tuberosum L.) were used in the meta-analysis on crop production at the regional scale and across a range of N fertilization gradients in China. Plastic mulching reduced VWA by 15.3%, 14.1%, and 16.3% in maize, wheat, and potato, respectively, when compared to non-mulching practice, with corresponding reductions of WFo by 33.3%, 28.1%, and 32.1% and of WFe by 14.8%, 31.1%, and 31.0% in maize, wheat, and potato, respectively. Water footprint for crop production increased first and then decreased over the past four decades in China, and was greater in northwest and north central regions of China than in other regions. Under moderate and high N fertilization (>100 kg N ha−1) in dry regions, plastic mulching increased the impact on VWA, thereby reducing WFo and WFe. Water footprint for the production in potato and maize was lower than that in wheat. The meta-analysis demonstrated that plastic mulching practices reduced water footprint while lowering VWA. Therefore, plastic mulching has potential for saving water resource when appropriate growing region and N fertilizer applications are considered. • Plastic mulching (PM) significantly reduced crop's water footprint. • Under moderate and high N input, PM had greater effect on water footprint. • PM was more effective in reducing water footprint in relatively drought environments. [ABSTRACT FROM AUTHOR]

Climate change impacts crop productivity as atmospheric conditions and water supply change, particularly in intensive cropping areas. This study used the validated AquaCrop Model, which was run with downscaled daily climate data produced by SDSM and CanESM2. The changes in the potential grain yield of winter wheat and summer maize and water budget during the cropping seasons were estimated for the Huang-Huai-Hai Plain (3H Plain) under RCP4.5 and RCP8.5 scenarios. The results show that the potential yield of winter wheat is increasing with similar spatial patterns in the 2030s, 2050s, and 2080s, with much of the increase is distributed in Shandong and northeastern parts of Henan. During the winter wheat growth period, the water budget deficit will likely improve from −210 mm in the 2030s to −202 mm in 2080s under RCP4.5 and from −206 mm in the 2030s to −191 mm in 2080s under RCP8.5 across the 3H Plain. The water budget during the winter wheat period will continue to be in deficit in the north 3H Plain and improvements are estimated mostly in the lower southern areas of the Plain. The summer maize potential yield is estimated to increase from the baseline period, but yields will decrease by 0.81%, 1.19%, and 2.10% in the 2030s, 2050s, and 2080s, respectively, under RCP8.5 compared to RCP4.5. During the summer maize growth period, the water budget is also estimated to improve from 109 mm in 2030s to 126 mm in 2080s under RCP4.5 and 107 mm in the 2030s to 163 mm in 2080s under RCP8.5. This increase is mainly estimated in the central and south of the 3H Plain. The estimated ET c of winter wheat shows no significant decrease, while the reduction of 6 mm and 13 mm for summer maize is observed under RCP4.5 and RCP8.5, respectively. The study provides scientific evidence to devise adaptation and mitigation climate change strategies for agricultural productivity and water resource management. • The potential winter wheat yield will increase under RCP4.5 and RCP8.5 scenarios. • The increase in wheat yield follows a similar spatial pattern under both RCP scenarios. • The potential summer maize yield will decrease under RCP8.5 as compared to RCP4.5. • Water deficit for wheat will remain similar to the present with increased temperature. • Water budget during summer will significantly improve in the latter half of this century. [ABSTRACT FROM AUTHOR]

Cropping system models are widely used to assess the impacts of and adaptation practices to climate change on agricultural production. However, crop growth simulations at large scales have often lacked consideration of variation in crop cultivars, which were represented by different sets of genetic coefficients in crops models. In this study, taking the phenology of spring wheat (Triticum aestivum L.) as an example, we compared four different strategies for upscaling genetic parameters in phenology simulations at large scales with two experimental datasets. The first dataset was from field experiments comprising 40 different spring wheat cultivars at Altay (2014) and Yangling (2015–2017) station; the second dataset was historical (2010–2014) observed phenology records from 57 national agro-meteorological observation stations in China. The four strategies were the representative cultivar estimated at a single site (SSPs), the representative cultivar estimated at the 57 sites (NRPs), the various representative cultivars estimated at different agro-ecological zones (RRPs), and the virtual cultivars generated from the posterior distributions (VCPs). The posterior distributions aforesaid were established based on the calibrated parameter values of the 40 different spring wheat cultivars planted in Yangling. Then, 1000 sets of VCPs were randomly sampled from the posterior distributions. The results indicated that both the SSPs and NRPs strategy obtained large errors and uncertainties in spring wheat phenology simulations in China since only one representative cultivar was used. The RRPs strategy achieved the second high and the highest accuracy in anthesis and maturity data simulations. The VCPs strategy obtained the highest accuracy in anthesis simulation but relative larger errors in maturity simulation. The VCPs strategy can be directly used in large-scale crop growth simulations without tedious process of calibration. Hence, this strategy is recommended in areas where observations are scarce and for model users who not good at model parameter estimation. • Four upscaling strategies for estimations of regional genetic parameter were compared. • Applying a single cultivar in regional crop growth simulations produced large errors. • Posterior distributions of spring wheat cultivar-genetic parameters were generated. • Using posterior distributions simplified parameter estimations and increased accuracy. [ABSTRACT FROM AUTHOR]

The water footprint (WF) of crop production indicates the water consumption for crop growth in a specific area over a certain time, enabling comprehensive water use efficiency assessments to be achieved for different types of water in an irrigation district. Daily and monthly blue (irrigation water) and green (rainfall) water resources are unevenly distributed in monsoon climate areas. However, the intra-annual evolution of the WF of crop production for a monsoon irrigation district is lacking. In the current study, maize and wheat production in the Baojixia Irrigation District (BID) of Shaanxi in China selected as the case study for the period 2008–2017, where the WF of crop production was analyzed based on a regional distributed hydrological model and the associated meteorological driving factors on daily, monthly, and yearly scales were identified. Results reveal that the method of WF of crop production established in the study can be applied to hydrological simulation of irrigated areas without drainage system. The blue WFs of maize and wheat production accounted for 34% and 36% of the irrigation water supplies, respectively. On a yearly scale, precipitation had the greatest impact on the green and blue WFs of maize, while sunshine hours and temperature had the greatest impact on those of wheat, respectively. On daily and monthly scales, different meteorological factors played key roles in the WF of crop production in different typical years. The time scale effect existed in the impact of meteorological driving factors on the WF of crop production, reaching its maximum on a monthly scale. Both water shortages and water waste coexist in BID. The irrigated area should rationally allocate blue water and effectively utilize green water resource to improve crop yield. Meanwhile, the time scale effect in the WF assessment of crop production and driving factors cannot be neglected. The irrigation strategy should be adjusted to cope with the impact of meteorological conditions according to the results and actual situation. [Display omitted] • Daily and monthly water footprint of irrigated crops were analyzed. • Precipitation had the largest effect on the water footprint of maize. • Sunshine hours and temperature effected largest on the water footprint of wheat. • Monthly time scale effect of meteorological factors on water footprint was larger. [ABSTRACT FROM AUTHOR]