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The accuracy prediction for the crop yield is conducive to the food security in regions and/or nations. To some extent, the prediction model for crop yields combining the crop mechanism model with statistical regression model (SRM) can improve the timeliness and robustness of the final yield prediction. In this study, the accumulated biomass (AB) simulated by the Agricultural Production Systems sIMulator (APSIM) model and multiple climate indices (e.g., climate suitability indices and extreme climate indices) were incorporated into SRM to predict the wheat yield in the North China Plain (NCP). The results showed that the prediction model based on the random forest (RF) algorithm outperformed the prediction models using other regression algorithms. The prediction for the wheat yield at SM (the period from the start of grain filling to the milky stage) based on RF can obtain a higher accuracy (r = 0.86, RMSE = 683 kg ha−1 and MAE = 498 kg ha−1). With the progression of wheat growth, the performances of yield prediction models improved gradually. The prediction of yield at FS (the period from flowering to the start of grain filling) can achieve higher precision and a longer lead time, which can be viewed as the optimum period providing the decent performance of the yield prediction and about one month's lead time. In addition, the precision of the predicted yield for the irrigated sites was higher than that for the rainfed sites. The APSIM-simulated AB had an importance of above 30% for the last three prediction events, including FIF event (the period from floral initiation to flowering), FS event (the period from flowering to the start of grain filling) and SM event (the period from the start of grain filling to the milky stage), which ranked first in the prediction model. The climate suitability indices, with a higher rank for every prediction event, played an important role in the prediction model. The winter wheat yield in the NCP was seriously affected by the low temperature events before flowering, the high temperature events after flowering and water stress. We hope that the prediction model can be used to develop adaptation strategies to mitigate the negative effects of climate change on crop productivity and provide the data support for food security. [ABSTRACT FROM AUTHOR]

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Future extreme climate events will become more intense and frequent with global warming, which is a great threat to wheat productivity in the North China Plain (NCP). Projecting future changes in extreme climate events is an important prerequisite for exploring crop adaptation measures to climate variation. In this study, we calculated 11 extreme climate indices at different wheat growth stages that are sensitive to wheat yield across NCP. The future climate projections were sourced from thirteen Global Climate Models (GCMs) from the Coupled Model Inter‐comparison Project phase 6 (CMIP6) under two emission scenarios of future societal development pathway (SSP) 245 and SSP585. Daily climate data of thirteen GCMs were generated by using a statistically downscaled method. Two multi‐model ensemble methods, that is, arithmetic mean and independence weighted mean (IWM), were used to assess the performance of thirteen GCMs in reproducing historical changes of extreme climate indices. The IWM ensemble results could better reproduce historical changes of extreme climate indices than multi‐model arithmetic mean and any individual GCM. We found that the frequency and intensity of heat extremes were projected to increase over the 21st century for both scenarios, but those of cold extremes will decrease. Heat stress intensity (HSI) increases around 1.0°C for SSP245 scenario and 1.6°C for SSP585 scenario by the end of the 21st century. There was no significant change in extreme precipitation indices across the NCP, but the changes of extreme precipitation had strong spatial heterogeneity. Overall, wheat production in the NCP might have a higher frequency of exposure to extreme climate, especially under heat stress. Therefore, adopting effective adaptation measures to mitigate heat stress for wheat is the first priority in the NCP. [ABSTRACT FROM AUTHOR]

Agriculture and pasture ecotone are typical ecologically vulnerable areas. Crop productivities and the agricultural sustainability are greatly affected by climate change in these areas. Field experiments were conducted from 2011 to 2013 in Inner Mongolia (41°06'N, 111°28'E) to investigate the influence of planting dates on yield and phenology of spring wheat and potential of improving resource (water and nitrogen) use efficiencies. The experimental field was located in arid and semiarid climate zone, where mean annual temperature was 2.7°C, annual growing degree days (>0 ) was about 2553 (°C•d), the average annual rainfall was only 354 mm mainly in July and August accounting for 80% precipitation in the whole growing season. Five sowing dates was designed from 26 April to 5 June with a 10-day interval. The results showed that the yields after 6 May were significantly (p<0.05) decreased due to delayed sowing dates. Compared to early sowing date (26 April), the yield of the late sowing date was reduced by 63.3% to 72.3% due to the obvious reduction in daily temperature from the growth stages of heading to maturity. Harvest index (HI) and grain weight per 1000 seeds were also affected by sowing dates. The early sowing (26 April and 5 May) treatments had higher HI. But after 16 May, the HI decreased significantly (p<0.05). The grain weight per 1000 seeds decreased from 44.65 to 20.73 g with the delaying of the seeding dates, which had a trend similar with HI. It indicated that low values of HI and grain weight contributed to yield decrease when sowing after 26 May. Phenology of wheat as expressed in calendar days were different between years and sowing dates, especially from sowing to emergency, but the phenology based on physiological development time (PDT) was similar with a constant value of 95.3 days (the days required to complete whole development stage at optimal temperature condition) between years and sowing dates. Water use efficiency (WUE) was significantly (p<0.05) decreased by delaying sowing dates. Compared with early sowing treatments, the WUE of late sowing dates were reduced by 68.8% to 74.3% probably due to the reduction of ratio of spike over above-ground dry matter in late sowing treatments (i.e. more vegetative growth and bigger canopy than early sowing treatments). Total nitrogen uptake by above-ground biomass ranged from 14.0 to 17.0 g/m2 for different sowing dates, however, the difference was not statistically significant (p>0.05). As the early sowing treatments showed higher (p<0.05) yield and HI, nitrogen use efficiency (NUE), the transformation of nitrogen uptake to the economic yield was significantly (p<0.05) higher in early sowing treatments than in late sowing treatments. Therefore, the best sowing date should be earlier than 6 May, and early sowing before 6 May could improve yield, water and nitrogen use efficiencies of wheat in the agriculture and pasture ecotone. [ABSTRACT FROM AUTHOR]