Cultivar selection can increase yield potential and resource use efficiency of spring maize to adapt to climate change in Northeast China

Su, Zhenge; Liu, Zhijuan; Bai, Fan; ZHANG, Zhen-tao; Sun, Shuang; Huang, Qiuwan; Liu, Tao; LIU, Xiao-qing; Yang, Xiaoguang

doi:10.1016/s2095-3119(20)63359-7

Cited by 7 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the USA, the relative ripening time is normally used to reflect the differences in ripening times among maize varieties and can also help farmers to select varieties with ripening times that correspond to the local environmental conditions. Previous studies have shown that that the average growth period of maize varieties should be extended from the current 123 days to 158 days to fill the yield gap caused by the increase in AcT, and it was proposed that the growth period of maize should be 180 days in the southern part of northeast China, 42 which was consistent with the growing period's distribution of varieties with 25% GMC at harvest (Fig. 8(a), (b)).…”

Section: Discussionsupporting

confidence: 70%

Improving maize quality from mechanical grain harvesting by matching maize varieties with accumulated temperature in northeast China

et al. 2023

View full text Add to dashboard Cite

BACKGROUND: Global warming has led to methods of planting late-maturing maize varieties in northeast China that have hindered the development of physiological maturity (PM) at harvest and the use of mechanical grain harvesting (MGH). Under these conditions it is difficult to balance the drying characteristics of maize varieties and to make full use of accumulated temperature resources in such a way as to reduce grain moisture content (GMC) at harvest. RESULTS:The effective accumulated temperature (AcT) and the drying rates of different varieties vary. In northeast China, with a GMC of 25%, the growth periods of a fast-drying variety (FDV) and a slow-drying variety (SDV) were 114-192 days and 110-188 days respectively. After PM, the FDV needed 47 days and the SDV needed 51 days to reduce the GMC to be ready for MGH. Harvested with a GMC of 20%, the growth period for the FDV was 97-175 days and for the SDV it was 90-171 days. After PM, the FDV required 64 days and the SDV needed 70 days to reduce the GMC to be ready for MGH.CONCLUSION: Matching cultivars with AcT can help farmers to choose suitable varieties. Promoting MGH may boost maize production, thus ensuring China's food security.

show abstract

Section: Discussionsupporting

confidence: 70%

Improving maize quality from mechanical grain harvesting by matching maize varieties with accumulated temperature in northeast China

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In this paper, the parameters in Table 2 were identified based on baseline information on the healthy growth of corn at different fertility stages, mainly including data from China Agrometeorology [53], Food Security Meteorological Services [54], and related literature [41,50,55]. Temperature suitability (S t ): Temperature is the critical determinant of crop velocity of progression.…”

Section: Climate Factormentioning

confidence: 99%

County Scale Corn Yield Estimation Based on Multi-source Data in Liaoning Province

Shuai

Shao

et al. 2023

Agronomy

View full text Add to dashboard Cite

Corn as a dominant and productive cereal crop has been recognized as indispensable to the global food system and industrial raw materials. China’s corn consumption reached 2.82 × 108 t in 2021, but its production was only 2.65 × 108 t, and China’s corn industry is still in short supply. Timely and reliable corn yield estimation at a large scale is imperative and prerequisite to prevent climate risk and meet the growing demand for corn. While crop growth models are well suited to simulate yield formation, they lack the ability to provide fast and accurate estimates of large-scale yields, owing to the sheer quantity of data they require for parameterization. This study was conducted in the typical rain-fed corn belt, Liaoning province, to evaluate the applicability of our modeling practices. We developed the factors using climate data and MCD43A4 production, and built a county-level corn yield estimation model based on correlation analysis and corn growth mechanisms. We used corn yield data from the county between 2007 and 2017, leaving out 2017 for verification. The results show that our model, with an R2 (the Coefficient of Determination) of 0.82 and an RMSE (Root Mean Square Error) of 279.33 kg/hm2, significantly improved estimation accuracy compared to only using historical records and climate data. Our model’s R2 was 0.34 higher than the trend yield estimation model and 0.27 higher than the climate yield estimation model. Additionally, RMSE was reduced by 300–400 kg/hm2 compared to the other two models. The improvement in performance achieved by adding remote sensing information to the model was due to the inclusion of variables such as monitored corn growth state, which corrected the model predictions. Our work demonstrates a simple, scalable, and accurate method for timely estimation of corn yield at the county level with publicly available multiple-source data, which can potentially be employed in situations with sparse ground data for estimating crop yields.

show abstract

Spatial‐temporal patterns of high‐temperature and drought during the maize growing season under current and future climate changes in northeast China

Zhao

Zhang

et al. 2023

J Sci Food Agric

View full text Add to dashboard Cite

Background Under the background of global warming, the intensity and frequency of extreme meteorological events in the maize growing season in northeast China are increasing. In order to clarify the occurrence characteristics of extreme meteorological events in northeast China, this study focused on three extreme agrometeorological events, i.e., high temperatures, drought, and the compound events of high‐temperature and drought during the maize growing season (May–September), impacting maize production. Results Based on historical (1981–2017) and future (2021–2060) climate data, we analyzed the spatial‐temporal patterns of these three events with different intensities in northeast China. The results indicated that slight high‐temperatures and moderate and severe droughts occurred more frequently in the study area during the historical period. The frequency of the different grades of the compound events of high‐temperature and drought will exhibit an increasing trend in the future, as will the frequency of the compound events of high‐temperature and drought. This is particularly evident in the northwest of the study area. Conclusion The compound events of high‐temperature and drought mainly occurred in late July and early August, encompassing the flowering stage of maize varieties. It is important to identify the area and time of major extreme weather events to implement the necessary preventive measures. © 2023 Society of Chemical Industry.

show abstract

Cultivar selection can increase yield potential and resource use efficiency of spring maize to adapt to climate change in Northeast China

Cited by 7 publications

References 19 publications

Improving maize quality from mechanical grain harvesting by matching maize varieties with accumulated temperature in northeast China

Improving maize quality from mechanical grain harvesting by matching maize varieties with accumulated temperature in northeast China

County Scale Corn Yield Estimation Based on Multi-source Data in Liaoning Province

Spatial‐temporal patterns of high‐temperature and drought during the maize growing season under current and future climate changes in northeast China

Contact Info

Product

Resources

About