Spatial-temporal distribution of climate suitability of winter wheat in North China Plain for current and future climate scenarios

Assessing Changes in Climatic Suitability for Sesame Cultivation in China (1978–2019) Based on Fuzzy Mathematics

“…Since the 1980s, researchers have used the crop climatic suitability model to evaluate the climatic suitability of crops in specific regions, which can improve their ability to adapt to current and future climate change. Tang et al [25,26] analyzed the impact of current and future climate scenarios on the suitability of summer maize and winter wheat in the North China Plain. Wei et al [27] established a peanut climatic suitability model to optimize peanut planting structure.…”

Refined Evaluation of Climate Suitability of Maize at Various Growth Stages in Major Maize-Producing Areas in the North of China

“…Zhao et al, based on future meteorological data, evaluated the suitability of maize in the North China Plain for the nutritive growth period, reproductive growth period, and the whole reproductive period [12]. The assessment of environmental conditions for crop growth based on fuzzy mathematical methods to build relevant climate models is one of the most commonly used methods in climate suitability studies [13]. For example, Zhao et al established a composite climate suitability index of 0-1 as an assessment criterion based on this method and analyzed the climate suitability of potatoes at three growth stages in North China [14].…”

Modeling the impacts of projected climate change on wheat crop suitability in semi-arid regions using the AHP-based weighted climatic suitability index and CMIP6

“…A wide variety of methods have been developed for estimating the agro-climatic indices needed to feed models for wheat yield simulation and suitability during the crop growth cycle and assessing the impact of climate change. For instance, the analog and empirical models for durum wheat yield forecasting (Ferrise et al 2015), the water stress index (WSI) as a daily water balance model (Chourghal et al 2016), the drought and overwhelmed water key indicator (DOWKI) (Kapsambelis et al 2019), STICS soil-crop model by coupling gridded datasets of soil and climate (Brisson et al 2002;Yang et al 2020), the integrated climatic suitability, which reflects the influence of solar radiation, temperature, and precipitation on the winter wheat suitability in the entire crop growth cycle (Tang and Liu 2021), and several other agro-climatic indices, such as agricultural reference index for drought (ARID) (Woli et al 2012). More recently, several studies have applied machine learning and deep learning models to predict wheat yield based on meteorological, crop phenological, and remote sensing data (Murakami et al 2021;Srivastava et al2022;Wang et al 2020;Lischeid et al 2022).…”

“…These models fit the direct relationship between meteorological parameters and crop yield and can be considered to have good efficiency and satisfying output (Mathieu and Aires 2018;Srivastava et al 2022), but they cannot assess specifically the climatic crop suitability of a single meteorological parameter at each phenological stage (e.g., the meteorological drought indices). The functional-based models, which define the agro-meteorological requirements based on crop suitability theory and the analytic process methods and fuzzy logic, can be used to analyze the suitability of a single meteorological parameter at each phenological stage and play an important role in examining the climatic suitability for a specific crop (Tang and Liu 2021). But these models cannot deal with the possible discrepancy in agro-meteorological criteria priorities, mainly when selecting a weight for each indicator.…”