The Effect of Climate Change on Spring Maize (Zea mays L.) Suitability across China

Abstract

Spring maize (Zea mays L.) is a thermophilic C4 crop which is sensitive to climate change. This paper provides a detailed assessment of the effect of climate change on the crop from a new perspective, by predicting the probability of the potential distribution of spring maize across China. The affected area of spring maize suitability was identified, and then the affected area was subdivided into the improved area and the deteriorated area. Our results confirmed that there was a detrimental consequence for spring maize suitability under observed climate change from 1961–1990 to 1981–2010. However, our results revealed that warming scenarios of 1.5 °C and 2 °C were helpful for the suitable area expansion of spring maize. The affected area was smaller under warming scenarios than under historical climate change, revealing that temperature rise alone was not enough to trigger a “tipping point” (a threshold value after which abrupt shifts occur) for spring maize, even if warming is 2 °C above the level of 1961–1990. Our results not only benefit China in the design of mitigation and adaptation strategies, but also provide a theoretical judgement that the impact of global warming on the crop ecosystem is not serious if other climate factors remain unchanged.

Yield Data Provide New Insight into the Dynamic Evaluation of Maize’s Climate Suitability: A Case Study in Jilin Province, China

“…Our studies used high-stability of meteorological yield to improve the traditional methods for the more statistically significant climate indicators, and it is more meaningful to capture the impact of climate change on crops in specific regions. Comparing the research results for maize suitability, based on the MaxEnt model in China [49], it is found that the climate suitability in Jilin Province is basically consistent with our study. However, combined with the actual maize yield data (Figure 3h), the result of Ye et al, is relatively high in the western region, and our study is closer to the actual maize production in Jilin Province.…”

Modelling Climate Suitability for Rainfed Maize Cultivation in Kenya Using a Maximum Entropy (MaxENT) Approach

“…This is in agreement with the findings of related studies that found the model is applicable in assessing crop suitability. Examples are studies on rice by Duan and Zhou [20] and Liu et al [21]; maize by Machovina and Feeley [57], Ji et al [58] and He and Zhou [18]; and banana by Machovina and Feeley [57]. This then confirms MaxENT's capacity, which is based on machine learning and the principle of maximum entropy in estimating the multivariate distribution of habitat suitability conditions for crop suitability.…”

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