Search

Spring wheat–summer maize (SWSM) annual crop systems were formed to satisfy the maize grain mechanized harvest thermal requirement in the thermal–resource–limited region of the North China Plain. However, the nitrogen (N) application rate effect on SWSM annual yield formation, N accumulation and utilization were barely evaluated. Two–year field experiments were conducted to evaluate the effects of the N application rate on the annual yield of SWSM, observe N accumulation and utilization, and identify the optimized N application. The experiments were conducted under 5 N levels of 0 (N0), 180 (N180), 240 (N240), 300 (N300), and 360 (N360) kg ha−1. The results showed that spring wheat, summer maize and annual cereal yield under the N240 and N480 treatments obtained the highest grain yield (GY) of 5038, 1282 and 16,320 kg ha−1, respectively, and the optimal N application rate was estimated using a linear–plateau model to be 231–307, 222–337 and 463–571 kg ha−1 with maximum GY of 4654–5317, 11,727–12,003 and 16,349–16,658 kg ha−1, respectively. With the increase in the N application rate, the dry matter accumulation (DM) were significantly increased by 16.9–173.5% for spring wheat and 11.1≈–76.8% for summer maize, respectively; and the annual cereal DM was 15.1–179.7% greater than that with N0 treatment, respectively. Spring wheat, summer maize and the annual cereal total N accumulation (TN) under N360 and N720 treatments were significantly increased by 5.4–19.1%, 16.6–32.3% and 11.5–26.2%, respectively, compared to the other treatments; however, N use efficiency for biomass and grain production (NUEbms and NUEg) were decreased significantly by 10.9–13.6% and 8.9–20.7%, 6.8–13.8% and 12.2–15.6%, and 5.5–11.7% and 10.0–16.0%, respectively. Meanwhile, the N partial factor productivity (PFPN), N agronomy use efficiency (ANUE), N recovery efficiency (NRE) and N uptake efficiency (NEupk) under the N240 treatment for spring wheat and summer maize obtained high levels of 20.99 and 47.01 kg−1, 9.27 and 16.35 kg−1, 32.53% and 32.44%, and 0.85 and 0.72 kg−1, respectively. Correlation analysis showed that the N application rate, TN and NEupk played significantly positive roles on GY, spring wheat spilke grain number, summer maize ear grain number and 1000–grain weight, DM LAImax and SPADmax, while NUEbms, NUEg, PFPN and ANUE always played negative effects. These results demonstrate that spring wheat, summer maize and annual cereal obtained the highest GY being 4654–5317, 11,727–12,003 and 16,349–16,658 kg ha−1 with the optimal N application rate 231–307, 222–337 and 463–571 kg ha−1, respectively, which provide N application guidance to farmer for spring wheat–summer maize crop systems to achieve annual mechanical harvesting in the thermal–resource–limited region of the North China Plain. [ABSTRACT FROM AUTHOR]

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]

Nitrogen (N) and water irrigation are two vital factors influencing the agriculture sustainability in various regions across the world, such as the North China Plain (NCP). Exploring optimal N application and water-efficient irrigation methods are needed for achieving greater crop productivity benefits and increasing the efficiency of inputs (N and water) in winter wheat (Triticum aestivum L.) production in the NCP. For this reason, we conducted a two-year field experiment with four N application rates interacting with three irrigation methods to examine the effects of N fertilization and water-efficient irrigation on grain yield, biomass production, economic benefits, and N- and water-use efficiencies of winter wheat in the NCP. The optimal N fertilization rate was ≈200 kg N ha−1, achieving a high grain yield of winter wheat (≈6000 kg ha−1). At this N dose, the highest net economic benefit was also achieved by the local farmer due to the increased grain yield, which was accompanied by more water-efficient irrigation. N recovery efficiency, agronomy efficiency, and the partial factor productivity of wheat decreased sharply with the N application rate. Water-use efficiency was significantly increased through drip irrigation and sprinkler irrigation. Considering the wheat productivity, input (N and water) efficiencies, and economic performance, water-efficient irrigation accompanied with an N application rate of 200 kg N ha−1 is optimal for achieving high economic returns for local farmers in the NCP. [ABSTRACT FROM AUTHOR]

Soil water content (SWC) is an important factor restricting crop growth and yield in cropland ecosystems. The observation and simulation of soil moisture contribute greatly to improving water-use efficiency and crop yield. This study was conducted at the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The study period was across the winter wheat (Triticum aestivum L.) growth stages from 2017 to 2019. A cosmic-ray neutron probe was used to monitor the continuous daily SWC. Furthermore, the crop leaf area index (LAI), yield, and aboveground biomass of winter wheat were determined. The root zone quality model 2 (RZWQM2) was used to simulate and validate the SWC, crop LAI, yield, and aboveground biomass. The results showed that the simulation errors of SWC were minute across the wheat growth stages and mature stages in 2017–2019. The root mean square error (RMSE) and relative root mean square error (RRMSE) of the SWC simulation at the jointing stage of winter wheat were 0.0296 and 0.1605 in 2017–2018, and 0.0265 and 0.1480 in 2018–2019, respectively. During the rain-affected days, the RMSE (0.0253) and RRMSE (0.0980) for 2017–2018 were significantly lower than those of 2018–2019 (0.0301 and 0.1458, respectively), indicating that rain events decreased the model accuracy in the dry years compared to the wet years. The simulated LAIs were significantly higher than the measured values. The simulated yield value of winter wheat was 5.61% lower and 3.92% higher than the measured yield in 2017–2018 and in 2018–2019, respectively. The simulated value of aboveground biomass was significantly (45.48%) lower than the measured value in 2017–2018. This study showed that, compared with the dry and cold wheat growth period of 2018–2019, the higher precipitation and temperature in 2017–2018 led to a poorer simulation of SWC and crop-growth components. This study indicated that annual abnormal rainfall and temperature had a significant influence on the simulation of SWC and wheat growth, especially under intensive climate-change stress conditions. [ABSTRACT FROM AUTHOR]