Relationship of population migration, crop production pattern, and socioeconomic development: evidence from the early 21st century

Abstract

Global crop production and population distributions have undergone great changes under climate change and socioeconomic development, and have drawn considerable public attention. How to explain the similarity of the migration patterns of crop yield and population density for different countries/regions is still uncertain and worth studying. Here, we estimated the similarity between migrations of main crop caloric yield (i.e. maize, rice, wheat, and soybean) and population density using Fréchet distance, and investigated the regression relationship between Fréchet distance and related climatic and socioeconomic variables for countries/regions with different economic development stages. The results indicated that different countries/regions showed different Fréchet distances during 2000–2015, with a maximum value of 24.44 for Russia and a minimum value of 0.11 for Georgia. For countries/regions with different economic development stages, the built regression models can explain 39%–93% of the variability in the Fréchet distance. Log(land area), log(GDP), and log(land area under cereal production) were always included in regression models and had higher importance in explaining the variability of Fréchet distance. For the model for all countries/regions, both the log(land area) and log(GDP per capita) may positively link to the Fréchet distance. Possible reasons for these results are that countries/regions with high GDP (or GDP per capita) may ease the conflict of land resources between humans and crops to achieve agricultural industrialization, which causes the far connection of the migrations for crop caloric yield and population density. The complicated interactions of crop production, population dynamic, and socioeconomic development should be given greater attention in the future.

Quantifying effects of climate change and farmers' information demand on wheat yield in India: a deep learning approach with regional clustering

“…Lv et al (2013) analyzed the spatial pattern of wheat yield using cluster analysis and emphasized the need to address the spatial gap and improve production over time. To identify the factors affecting wheat production, techniques such as the spatial Durbin model (Zhang and Li, 2022), stepwise regression analysis (Zhang et al, 2021), farmer field surveys (Zhang and Li, 2022), and machine learning (Yu et al, 2022) are utilized. The consensus is that the impact of crop yield is multidimensional, and climate factors have the most direct effect on regional differences in yield (Fen et al, 2020;Lin and Shao, 2020;Twizerimana et al, 2020).…”

Detection of the Spatio-Temporal Differentiation Patterns and Influencing Factors of Wheat Production in Huang-Huai-Hai Region

“…Guo et al [ 11 ] analyzed the spatial pattern of wheat yield using cluster analysis and found that while wheat production is improving in the time dimension, it is also necessary to pay attention to the gap in the space dimension. In the detection of factors affecting wheat production, research techniques mainly include the spatial Durbin model [ 12 ], stepwise regression analysis [ 13 ], farmer field surveys [ 14 ], and machine learning [ 15 ]. A more consistent conclusion is obtained: the impact of a crop yield is multifaceted and three-dimensional [ 16 ]; Climatic factors are the most direct factors affecting wheat yield and causing its regional differences [ 17 , 18 ].…”

Population distribution within the human climate niche

“…Several studies have looked at human migration and its relation to climate during the original radiation of humans from Africa [13][14][15], and during prehistory [16][17][18][19][20], but technological innovations may have made human populations less sensitive to environmental conditions than they were during prehistory. For instance, while suitability for agriculture may be a key prerequisite for maintaining high population densities, in recent generations population centers and agricultural centers are growing increasingly decoupled [9,21]. Other papers have looked at the role of interannual-to-decadal climate change in instigating migration and civil conflict [22][23][24][25][26], but it is difficult to connect such episodes of population change to long-term population trends.…”