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Applications of remote sensing using unmanned aerial vehicle (UAV) in agriculture has proved to be an effective and efficient way of obtaining field information. In this study, we validated the feasibility of utilizing multi-temporal color images acquired from a low altitude UAV-camera system to monitor real-time wheat growth status and to map within-field spatial variations of wheat yield for smallholder wheat growers, which could serve as references for site-specific operations. Firstly, eight orthomosaic images covering a small winter wheat field were generated to monitor wheat growth status from heading stage to ripening stage in Hokkaido, Japan. Multi-temporal orthomosaic images indicated straightforward sense of canopy color changes and spatial variations of tiller densities. Besides, the last two orthomosaic images taken from about two weeks prior to harvesting also notified the occurrence of lodging by visual inspection, which could be used to generate navigation maps guiding drivers or autonomous harvesting vehicles to adjust operation speed according to specific lodging situations for less harvesting loss. Subsequently orthomosaic images were geo-referenced so that further study on stepwise regression analysis among nine wheat yield samples and five color vegetation indices (CVI) could be conducted, which showed that wheat yield correlated with four accumulative CVIs of visible-band difference vegetation index (VDVI), normalized green-blue difference index (NGBDI), green-red ratio index (GRRI), and excess green vegetation index (ExG), with the coefficient of determination and RMSE as 0.94 and 0.02, respectively. The average value of sampled wheat yield was 8.6 t/ha. The regression model was also validated by using leave-one-out cross validation (LOOCV) method, of which root-mean-square error of predication (RMSEP) was 0.06. Finally, based on the stepwise regression model, a map of estimated wheat yield was generated, so that within-field spatial variations of wheat yield, which was usually seen as general information on soil fertility, water potential, tiller density, etc., could be better understood for applications of site-specific or variable-rate operations. Average yield of the studied field was also calculated according to the map of wheat yield as 7.2 t/ha.

Wheat yield production is largely attributed by weather parameters. Model developed by multiple linear, neural network and penalised regression techniques using weather data have the potential to provide reliable, timely and cost-effective prediction of wheat yield. Wheat yield data and weather parameter during crop growing period (46th to 15th SMW) for more than 30 years were collected for study area and model was developed using stepwise multiple linear regression (SMLR), principal component analysis (PCA) in combination with SMLR, artificial neural network (ANN) alone and in combination with PCA, least absolute shrinkage and selection operator (LASSO) and elastic net (ENET) techniques.  Analysis was carried out by fixing 70% of the data for calibration and remaining dataset for validation. On examining these models, LASSO and elastic net are performing excellent having nRMSE value less than 10 % for four out of five location and good for one location, because of prevention in over fitting and reducing regression coefficient by penalization.

Under the 1.5 °C and 2.0 °C warming scenarios, few studies have explored the different influences of CO2, temperature, and precipitation on wheat yield in China. Hence, we analyzed the different influences of above factors on wheat yield after analyzing the wheat yield change. Results show that (1) the wheat yield of China would increase under the two warming scenarios. The spring wheat yield of the Northeast Spring Wheat Region (DB_S) and the Northwest Spring Wheat Region (XB_S) would increase more than that in the other two spring wheat-planting subregions. The winter wheat yield of the Southwest Winter Wheat Region (XN_W) and Middle and Lower Yangtze Winter Wheat Region (CJ_W) would increase more significantly compared with that in the other three winter wheat-planting subregions. (2) CO2 fertilization was identified as the main factor leading to a wheat yield increase, with an estimated increase of 12–18% under the 1.5 °C warming scenario and 18–21% under the 2.0 °C warming scenario. (3) Without considering CO2 fertilization, compared with precipitation, temperature dominated the spatial patterns of the wheat yield responses to climate change. Temperature influence on wheat yield was mainly reflected in the amplitudes of yield increase, while precipitation mainly affected the proportion of yield-increasing area. For wheat-planting regions with less precipitation, such as the North Spring Wheat Region (BB_S) and the Huang-Huai Winter Wheat Region (HH_W), the wheat yield increase caused by precipitation was usually more pronounced. Our findings would help inform policy decisions related to wheat production in China to better cope with future climate warming.

This study aims to provide new insight on the wheat yield historical response to climate processes throughout Spain by using statistical methods. Our data includes observed wheat yield, pseudo-observations E-OBS for the period 1979 to 2014, and outputs of general circulation models in phase 5 of the Coupled Models Inter-comparison Project (CMIP5) for the period 1901 to 2099. In investigating the relationship between climate and wheat variability, we have applied the approach known as the partial least-square regression, which captures the relevant climate drivers accounting for variations in wheat yield. We found that drought occurring in autumn and spring and the diurnal range of temperature experienced during the winter are major processes to characterize the wheat yield variability in Spain. These observable climate processes are used for an empirical model that is utilized in assessing the wheat yield trends in Spain under different climate conditions. To isolate the trend within the wheat time series, we implemented the adaptive approach known as Ensemble Empirical Mode Decomposition. Wheat yields in the twenty-first century are experiencing a downward trend that we claim is a consequence of widespread drought over the Iberian Peninsula and an increase in the diurnal range of temperature. These results are important to inform about the wheat vulnerability in this region to coming changes and to develop adaptation strategies.

Past research has extensively analyzed the impact of individual weather factors on wheat yields, leading to the use of discriminant function and principal component analysis to harness weekly weather data to develop robust forecast models (Agrawal et al., 2012; Sisodia et al., 2014; Kumar et al., 2019). Model 1 used five un-weighted weather indices Model 2 used five weighted weather indices Model 3 used 30 indices (both weighted and un-weighted, including interaction indices) Model 4 used five weighted and five un-weighted weather indices Model 5 used five un-weighted and 10 un-weighted interaction weather indices and Model 6 used five weighted and 10 weighted weather indices The weather data of five weather parameters (maximum and minimum temperatures, rainfall, wind velocity, and sunshine hours), from the 44th standard meteorological week (SMW) to the 7th SMW of the following year, for 15 years (2000-01 to 2014-15) were used for developing models. Authors contribution: P. K. Singh: Conceptualized, data analysis, and drafted the manuscript; P. Singh: Data collection and interpretation of the analysis; V. D. Chaturvedi: Reviewing, editing and final manuscript.

The escalation in extreme weather events has raised concerns for agriculture. The quantification of the impacts of extreme events on crop yield has predominantly concentrated on individual events like drought or heat. Numerous instances have showcased the destructive effects of compound extreme events on crop yields, surpassing those of individual events. However, their influence extent is region‐specific and not fully understood in Australia's crop belt. Using a biophysical‐statistical modeling approach, we quantified the individual impacts of drought, heat, frost, and compound drought and extreme temperature (DET) events on wheat yield variations in Australia. We first developed indices for these different extreme events during the wheat reproductive period based on the APSIM (Agricultural Production System sIMulator) model and then used these indices in multiple linear regression models to quantify their impacts on wheat yield variations. We found that, during 1990–2021, drought, heat, and frost events explained 48% of yield variation, while the percentage increased to 54% after including DET events, with some regions even up to 86%. In extreme low‐yield years, the relative importance of DET events surpassed the sum importance of individual drought, heat, and frost events, reaching 52% in years with yields below the 10th percentiles, respectively. Our findings highlight the need to factor compound extreme weather events into climate risk management to inform the mitigation of yield losses or crop failure. Plain Language Summary Global warming has brought more extreme weather events like drought, heatwaves, frost, etc. These extreme events seriously threaten agricultural production and food security, especially multiple co‐occurring weather events, which usually cause far more destructive effects than individual ones. In this study, we used a combined modeling approach to precisely quantify the impacts of individual and co‐occurring extreme weather events on wheat yield variation over the past three decades in Australia. We found that these extreme weather events were responsible for more than half of the wheat yield variation, with multiple co‐occurring extreme weather events particularly responsible for severe wheat yield losses in Australia. These findings highlight the need to factor co‐occurring extreme weather events into climate risk management to inform the mitigation of yield losses or crop failure. Key Points We developed a combined modeling approach to quantify the effects of compound drought and extreme temperature (DET) on wheat yield Annual average DET intensity contributed an additional 6% of Australia's wheat yield variation beyond univariate drought, heat, and frost intensities In extreme low‐yield years, annual average DET intensity dominated wheat yield loss, with relative contribution exceeding 50%

Climate change is a major concern in wheat agroecosystems as it can affect productivity and crop water use. This study used the AquaCrop model to evaluate climate change impacts on the wheat yield, crop water use and water footprint of wheat production in the Middle-Manyame sub-catchment of Zimbabwe. Climate scenarios were based on simulations from the NCC-NorESM1-M, CCCma-CanESM2 and MOHC-HadGEM2-ES General Climate Models downscaled using three Regional Climate Models (RCA4, RegCM4 and CRCM5) under two Representative Concentration Pathways (RCP4.5 and RCP8.5). The results showed that, compared to the baseline climate (1980–2010), yield may increase by 22.60, 29.47 27.80, and 53.85% for the RCP4.5 2040 s, RCP4.5 2080 s, RCP8.5 2040 s and RCP8.5 2080 s scenarios, respectively. Crop water use may decrease by 1.68, 1.25, 3.7 and 6.47%, respectively, under the four scenarios, respectively. Consequently, the blue water footprint may decrease by 19, 23, 24 and 38%, respectively, under the four scenarios. Sensitivity analysis attributed the increase in yields and the decrease in crop water use to the CO2 fertilization effect, which had a dominant effect over high-temperature effects. The results suggest that future wheat yields could be enhanced while crop water use is reduced because of climate change. However, the realization of these benefits requires farmers to adapt to climate change by adopting recommended agronomic practices and farm input rates that are consistent with those used in the modelling approach of this study.

Understanding the impacts of climate change on crop yield is important for improving crop growth and yield formation in northwestern China. In this study, we evaluated the relationship between meteorological dryness/wetness conditions and spring wheat yield in the Ili river basin (IRB). The climate and yield data from 1961 to 2013 were collected to analyze characteristics and correlations between these two variables using the standardized precipitation evapotranspiration index (SPEI), yield detrending method, modified Mann-Kendall test and Spearman correlation analysis. Main results were as follows: (1) correlations between monthly SPEI values (MSV) and climatic yield of spring wheat indicated that the dryness/wetness condition in May was a key factor affecting yield in the whole region; (2) although the MSV in May and yield fluctuated from negative to positive values in time, the severely and extremely dryness events were in good agreement with the higher yield losses; (3) each increase of 0.5 MSV in May promoted over 3% increase of yield in most part of IRB; however, the larger variability of MSV in May resulted in larger yield fluctuations; and (4) the Tibetan Plateau index in April showed significant correlations with the MSV in May and yield, which provided a precursory signal for decision-makers to better understand potential yield fluctuations.

Conservation agriculture (CA), defined as minimal soil disturbance (no-till) and permanent soil cover (mulch) combined with rotations, is a more sustainable cultivation system for the future than those presently practised. The present paper first introduces the reasons for tillage in agriculture and discusses how this age-old agricultural practice is responsible for the degradation of natural resources and soils. The paper goes on to introduce conservation tillage (CT), a minimum tillage and surface mulch practice that was developed in response to the severe wind erosion caused by mouldboard tillage of grasslands and referred to as the American dust bowl of the 1930s. CT is then compared with CA, a suggested improvement on CT, where no-till, mulch, and rotations significantly improve soil properties (physical, biological, and chemical) and other biotic factors, enabling more efficient use of natural resources. CA can improve agriculture through improvement in water infiltration and reducing erosion, improving soil surface aggregates, reducing compaction through promotion of biological tillage, increasing surface soil organic matter and carbon content, moderating soil temperatures, and suppressing weeds. CA also helps reduce costs of production, saves time, increases yield through more timely planting, reduces diseases and pests through stimulation of biological diversity, and reduces greenhouse gas emissions. Availability of suitable equipment is a major constraint to successful CA, but advances in design and manufacture of seed drills by local manufacturers are enabling farmers to experiment and accept this technology in many parts of the world. Estimates of farmer adoption of CA are close to 100 million ha in 2005, indicating that farmers are convinced of the benefits of this technology. The paper concludes that agriculture in the next decade will have to produce more food, sustainably, from less land through more efficient use of natural resources and with minimal impact on the environment in order to meet growing population demands. This will be a significant challenge for agricultural scientists, extension personnel, and farmers. Promoting and adopting CA management systems can help meet this complex goal.

The climate of the Earth is changing. The Earth’s temperature is projected to maintain its upward trend in the next few decades. Temperature and precipitation are two very important factors affecting crop yields, especially in arid and semi-arid regions. There is a need for future climate predictions to protect vulnerable sectors like agriculture in drylands. In this study, the downscaling of two important climatic variables—temperature and precipitation—was done by the CanESM2 and HadCM3 models under five different scenarios for the semi-arid province of Qazvin, located in Iran. The most efficient scenario was selected to predict the dryland winter wheat yield of the province for the three periods: 2010–2039, 2040–2069, and 2070–2099. The results showed that the models are able to satisfactorily predict the daily mean temperature and annual precipitation for the three mentioned periods. Generally, the daily mean temperature and annual precipitation tended to decrease in these periods when compared to the current reference values. However, the scenarios rcp2.6 and B2, respectively, predicted that the precipitation will fall less or even increase in the period 2070–2099. The scenario rcp2.6 seemed to be the most efficient to predict the dryland winter wheat yield of the province for the next few decades. The grain yield is projected to drop considerably over the three periods, especially in the last period, mainly due to the reduction in precipitation in March. This leads us to devise some adaptive strategies to prevent the detrimental impacts of climate change on the dryland winter wheat yield of the province.