Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations)

Abstract

Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) Authors: Xi Guo; Puying Zhang; Yaojie Yue This is the outcome data of our research which is under submission. Socioeconomic and climate change are both essential factors affecting the global cultivation distributions of crops. However, the role of socioeconomic factors in the prediction of future crop cultivation distribution under climate change has been rarely explored. Here, we proposed the MaxEnt-SPAM approach assuming that environmental conditions are the fundamental factors determining whether land is suitable for cultivating wheat, and socioeconomic factors are the driving forces that influence farmers’ crop choices. In short, the distribution of wheat cultivation depends on the maximization of potential revenue as well as satisfying wheat planting suitability. The proposed MaxEnt-SPAM approach for estimating the cultivation distribution of wheat in three combined Representative concentration pathway (RCP) -Shared socioeconomic pathway (SSP) scenarios, i.e., RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3). The steps were as follows: (1) Estimate wheat planting suitability under future RCP scenarios by the MaxEnt model. (2) Estimate farmers' crop choices under future SSP scenarios using Time series- Backpropagation (TS-BP) models. (3) Estimate global wheat cultivation distribution based on the SPAM model. Based on major known datasets on the distribution of wheat cultivation, the proposed MaxEnt-SPAM approach was carefully validated by comparing its prediction results with those known datasets. Satisfactory accuracy was achieved. It indicates that the predictive accuracy of the proposed approach could be over 85%, with a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets. Based on the above idea and approach, a grid (0.5 degree × 0.5 degree) global wheat cultivation distribution under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios were predicted. The results indicate that RCP8.5-SSP3 might be the most favorable for wheat cultivation. Moreover, socioeconomic development significantly restricts the potential distribution of wheat cultivation. The estimated wheat cultivation areas considering the effects of socioeconomic development average account for 77% of the potential wheat distribution determined by climatic factors under the selected RCP-SSP scenarios. Socio-economic development seems to benefit wheat cultivation in Africa. Our findings demonstrate the influence of socioeconomic factors on crop distribution from the perspective of the market economy, highlighting the necessity of coupling socioeconomic factors and climate change to accurately predict crop cultivation distribution. We argue that the global wheat cultivation distribution datasets under future climatic and socio-economic conditions (RCP-SSP combinations) are a valuable complement to currently available products. This wheat cultivation distribution prediction data is one of the few products to take into account both climate change and the drive for socio-economic development. We believe that it can provide a product that is more consistent with the logic of crop cultivation distribution than those that only consider climate change impacts. The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) is expected to allow us to better understand the dynamics and distribution of global wheat cultivation distribution under different climate change and socio-economic development paths in the future. These data can potentially provide support for relevant research. Such as but not limited to earth system simulation, and agricultural sciences. The Global Wheat Cultivation Distribution under Future Climatic and Socio-economic Conditions (RCP-SSP combinations) Datasets and the Maxent-SPAM approach code are stored in a zip package, that SPAM_MaxEnt.zip. This package consists of 2 folders (code, and data) shown as follows. code: This sub-folder provides the main program and example data for the MaxEnt-SPAM approach. Codes are written in Matlab language by Puying Zhang. There are also 'read me.txt' files under the code folder to provide the necessary information. The exampleData contains 1. h_pri.tif: prior data 2. h_res.tif: global C3 crop cultivation proportion Run the main programme: cross_entroy.m data: This sub-folder contains global wheat cultivation distribution stored in GeoTIFF file format. 1 Global distribution of the long-term wheat-cultivation area fraction: This sub-folder contains the data for the global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios. The value of each data ranges from 0 to 1, indicating the long-term wheat-cultivation area fraction in each grid, and the higher the value, the more wheat cultivated. r2s1f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP2.6-SSP1 scenario r4s2f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP4.5-SSP2 scenario r8s3f_sub.tif: the data for global distribution of the long-term wheat-cultivation area fraction in RCP8.5-SSP3 scenario 2 Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution: This sub-folder contains the data for Spatial overlap between the long-term period of land suitability for wheat planting and wheat cultivation distribution in multi-scenarios. The value of each data contains three values:{1, 2, 3}, 1 wheat cultivation existed but was predicted to be unsuitable to plant wheat; 2 presented a reduction in the wheat cultivation area compared to the land's suitability; 3 presented the region that wheat cultivation existed and was predicted to be suitable to plant wheat. com_suit_fra126.tif: the spatial overlap between the long-term period land suitability for wheat planting and wheat cultivation distribution in (a) RCP2.6-SSP1 scenario and RCP2.6 com_suit_fra245.tif: the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (b) RCP4.5-SSP2 scenario and RCP4.5 com_suit_fra385.tif: the spatial overlap between the long-term period land suitability for wheat cultivation and wheat cultivation distribution in (c) RCP8.5-SSP3 scenario and RCP8.5 3 Differences in the proportion of long-term wheat cultivation: This sub-folder contains the data for the difference in the proportion of long-term wheat cultivation under the RCP-SSP scenarios and the distribution of long-term wheat planting suitability under the same RCP scenarios. The value of each data ranges from -1 to 1, This data is obtained by using the wheat-cultivation area fraction minus planting suitability grid to grid. the negative value indicates that the proportion of wheat cultivation is lower than the wheat planting suitability, while this positive value indicates that the proportion of wheat cultivation is higher than the wheat planting suitability. r2s1_f.tif: Difference in the proportion of long-term wheat cultivation under the RCP2.6-SSP1 scenario and the distribution of long-term wheat planting suitability under the RCP2.6 scenario r4s2_f.tif: Differences between the proportion of long-term wheat cultivation in RCP4.5-SSP2 and the suitability of long-term wheat planting under the RCP4.5 scenario r8s3_f.tif: Differences between the proportion of long-term wheat cultivation in RCP8.5-SSP3 and the suitability of long-term wheat planting under the RCP8.5 scenario References: For more details on the MaxEnt (Maximum entropy) model, please refer to (Phillips et al., 2006; Elith et al., 2011). SPAM (spatial production allocation model) refers to (You et al., 2009; You et al., 2014). Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E., Yates, C.J., 2011. A statistical explanation of maxent for ecologists. Divers Distrib 17 (1), 43-57. https://coi.org/10.1111/j.1472-4642.2010.00725.x. Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol Model 190 (3-4), 231-259. https://coi.org/10.1016/j.ecolmodel.2005.03.026. You, L.Z., Wood, S., Wood-Sichra, U., 2009. Generating plausible crop distribution maps for sub-Saharan Africa using a spatially disaggregated data fusion and optimization approach. Agr Syst 99 (2-3), 126-140. https://coi.org/10.1016/j.agsy.2008.11.003. You, L.Z., Wood, S., Wood-Sichra, U., Wu, W.B., 2014. Generating global crop distribution maps: from census to grid. Agr Syst 127, 53-60. https://coi.org/10.1016/j.agsy.2014.01.00