Potential benefits of cropping pattern change in the climate-sensitive regions of rice production in China.

Rice production is a primary contributor to global greenhouse gas emissions, with unclear pathways towards carbon neutrality. Here, through a comprehensive assessment of direct greenhouse gas (GHG) emission using DNDC model and indirect GHG emission using emission factor methods, we estimated the annual crop yield, GHG emission amount and intensity, and economic benefits of different cropping patterns in the climate-sensitive regions of rice production in China. Through the expansion of single-rice and cropping pattern change from the wheat-rice to wheat-rice-rice in the climate-sensitive regions of single and triple-cropping cultivations, the total grain yield increased by 4.4 % and 4.5 % compared with the current national grain production, the GHG emission would increase by 2.4 % and 5.4 % of the current national GHG emissions from rice and wheat production, the net economic benefits could increase 0.9 % and decrease 2.0 % of the national output value of rice and wheat production. The study takes the entire-life cycle of crop growth as the principal line, and could provide a valuable reference for the regulation of the cropping pattern and the formulation of carbon reduction policies in the climate-sensitive region.

Adaptation measures of the potential double cropping region in Northern China to future climate change.

In the context of climate change, the northern climate-based boundaries of the winter wheat-summer maize double cropping system (DCS) have moved northward and westward. The selection of spring maize single cropping system (SCS) or DCS in the potential DCS region in northern China directly affects the annual crop yield, resource use efficiency, and greenhouse gas (GHG) emissions. Reducing GHG emissions while improving yield and resource use efficiency is essential to green agricultural development. We used future climate data (2021-2060, SSP2-4.5 and SSP5-8.5), along with crop and soil data, to assess the applicability of the Denitrification-Decomposition Model (DNDC) for simulating crop yield and GHG emissions. Through simulation of DNDC, we identified a cropping system that prioritized high yield, resource use efficiency, and GHG emissions reduction, adapting to future climate change. Under this cropping system, we quantified the effects of various straw incorporation rates, irrigation, and nitrogen input on crop yield, resource use efficiency, and GHG emissions. We proposed optimal measures to adapt to future climate change while aiming for high yield, resource use efficiency, and GHG emissions reduction. The results show that the DNDC reliably simulated yield and GHG emissions for the (SCS) and the DCS. In counting for greenhouse gas emission intensity (GHGI) as GHG emissions normalized by crop yield, the GHGI was reduced by 86.4% and 89.2% in DCS than in SCS under the SSP2-4.5 and SSP5-8.5, respectively. In the study area, the DCS should be adopted for high yield, resource use efficiency, and GHG emissions reduction (increased by 28.4% and 34.4%) in the SSP2-4.5 and SSP5-8.5 with 1) straw incorporation rate for 100% of winter wheat and for 60% of summer maize; 2) total irrigating 240 mm for winter wheat at pre-sowing, jointing, booting, and filling stages; and 3) applying nitrogen of 168 kg·N/ha for both crops.

Impacts of mean climate and extreme climate indices on soybean yield and yield components in Northeast China.

Soybean is an important oil crop in China, and the national focus of soybean production is in Northeast China. In order to achieve high-stable yield, it is crucial to acknowledge the impacts of mean climate and extreme climate indices on soybean yield and yield components. In this study, based on the weather data from 61 counties from 1981 to 2017 in Northeast China, we assessed the impacts of mean climate and extreme climate indices on soybean observed yield and simulated yield. Mean climate include effective growing degree days (GDD10), precipitation (Pre), and solar radiation (SR); extreme climate indices include the number of cool days during seed-filling period (C15), the number of cool days during 15 days before anthesis (C17), the number of hot days (H30), maximum amount of 5 Day accumulated precipitation (P5), and consecutive dry days (CDD)). We used the DSSAT-CROPGRO-Soybean model to identify the main yield components for soybean. The results showed that observed soybean yield reduced by 3.57% due to the collective changes in the eight study climate indices. Increases in GDD10, decreases in Pre, and decreases in SR caused a 3.96%, -3.89%, and - 0.48% change in soybean yield, respectively. Decreases in C15 and C17 led to a 5.36% increase in soybean yield; increases in H30, P5, and CDD caused a 5.75%, 0.30%, and 1.14% reduction in soybean yield, respectively. By comparing the response of observed and simulated soybean yield to climate indices (excluding P5) in the DSSAT-CROPGRO-Soybean model, we identified the key yield components for soybean as the number of pods and seed weight. The negative impacts on the number of pods and seed weight were mainly attributed to changes in Pre and H30 from anthesis to podding and during seed-filling period. Our results could be used to assist the local soybean community adapt to climate change.