Prediction of global wheat cultivation distribution under climate change and socioeconomic development.

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. Motivated by revealing the future global wheat cultivation distribution that coupling socioeconomic factors and climate change, the MaxEnt-SPAM approach was proposed by the present study. Furthermore, the spatial and temporal patterns of global wheat cultivation in the near-term (2011-2040), the mid-term (2041-2070), and long-term (2071-2100) under the scenarios of RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 were predicted. It indicated that the predictive accuracy of the proposed approach could be over 80 %, with a significant positive correlation (p < 0.01) between the predicted global wheat cultivation and multiple known datasets. Socioeconomic development significantly altered the potential distribution of global wheat cultivation driven by climate change. Socioeconomic development seems to benefit wheat cultivation in the Southern Hemisphere especially central and east Africa, while the Northern Hemisphere may have witnessed a decline in future cultivation areas. It was noteworthy that heightened profitability stimulated interest in expanding wheat cultivation efforts within pivotal countries/regions positioned in the Southern Hemisphere. In the long-term period, the potential wheat cultivation area was reduced by 7 % under the RCP2.6-SSP1 scenario, while it expanded by 8 % and 2 % under the RCP4.5-SSP2 and RCP8.5-SSP3 scenarios, respectively. A global decline in wheat production of 16 %, 3 %, and 3 % was observed in the long-term under the RCP2.6-SSP1, RCP4.5-SSP2, and RCP8.5-SSP3 scenarios respectively. The present study emphasized the importance of integrating socioeconomic factors into crop distribution predictions under climate change. Our findings indicated significant temporal adjustments in the future global distribution of wheat cultivation and offered a comprehensive perspective on how socioeconomic factors interacted with climate change to influence global wheat cultivation.

Projecting global fertilizer consumption under shared socioeconomic pathway (SSP) scenarios using an approach of ensemble machine learning.

Comprehensively projecting global fertilizer consumption is essential for providing critical datasets in related fields such as earth system simulation, the fertilizer industry, and agricultural sciences. However, since previous studies have not fully considered the socioeconomic factors affecting fertilizer consumption, huge uncertainties may remain in fertilizer consumption projections. Here, an approach ensembled six machine learning algorithms was proposed in this study to predict global fertilizer consumption from 2020 to 2100 by considering the impact of socioeconomic factors under shared socioeconomic pathway (SSP) scenarios. It indicates that the proposed approach provides a rational and reliable framework for fertilizer consumption prediction that stably outperforms the single algorithms with relatively high accuracy (Nash-Sutcliffe efficiency of 0.93, Kling-Gupta efficiency of 0.89, and mean absolute percentage error of 10.97 %). We found that global N and P fertilizer consumption may decrease from 2020 to 2100, while K fertilizer may buck the trend. N fertilizer consumption showed a declining trend of -1 %, -17.13 %, and -3.43 % under the SSP1, SSP2, and SSP3 scenarios in 2100, respectively. For P fertilizer, those were -0.68 %, -9.68 %, and -2.03 %. In contrast, global K fertilizer consumption may increase by 18.03 %, 9.18 %, and 6.74 %, respectively. On average, N, P, and K fertilizer consumption is highest in China, and the lowest is in Kazakhstan. However, the hotspots of N fertilizer consumption may shift from China to Latin America and the Caribbean. This study highlighted the ensemble machine learning approach could potentially be a robust method for predicting future fertilizer consumption. Our prediction product will not only contribute to a better understanding of global fertilizer consumption trends and dynamics but also provide flexible and accurate key data/parameters for related research. The Projected Global Fertilizers Consumption Datasets are available at doi:https://doi.org/10.5281/zenodo.8195593 (Gao et al., 2023).

The impact of climate change on aeolian desertification: A case of the agro-pastoral ecotone in northern China.

Land desertification, one of the gravest eco-environmental problems in the world, has been proven to be critically influenced by climate change. However, the information on the future spatial-temporal patterns of land desertification under climate change has been rarely explored, which restricts the proposal of reasonable desertification control countermeasures to adapt to climate change. The agro-pastoral ecotone in northern China (APENC) is the most critical eco-environmental barrier in China and is also a climate change-sensitive area prone to aeolian desertification. We quantitatively assessed the risk of aeolian desertification in the APENC to climate change and social-economic development in the near-term (2010-2039), mid-term (2040-2069) and long-term (2070-2099) by integrating the representative concentration pathway (RCP) scenarios and the shared socioeconomic pathway (SSP) scenarios using a data-mining approach. The C5.0 decision tree algorithm demonstrated acceptable reliability in aeolian desertification classification. Aeolian desertification in the APENC shows a significant persistent decreasing trend in 2010-2099 under RCP2.6-SSP1 and RCP8.5-SSP3 scenarios, whereas first increased in mid-term then decreased under RCP6.0-SSP2 scenarios. Aeolian desertification risk is lowest under the RCP2.6-SSP1 scenarios, while it is highest under the RCP6.0-SSP2 scenarios. With climate change and socioeconomic development, the risk of aeolian desertification in APENC was generally dominated by a slight grade, i.e., >70 %. While the moderate and severe grades still occupy vast areas, approximately 20 %, and 10 %, respectively, which mainly distributed in and around the Hulunbuir Sandy Land and the Horqin Sandy Land, showing the hot spots of desertification in the APENC. The reversal trend of aeolian desertification risk in the APENC might be initiated by the significant decrease of wind speed. This work highlights the great potential of data-mining approaches on climate change and social-economic development-related land desertification assessment.

The potential global distribution and dynamics of wheat under multiple climate change scenarios.

Accurately predicting changes in the potential distribution of crops resulting from climate change has great significance for adapting to and mitigating the impacts of climate change and ensuring food security. Based on very large datasets of wheat (Triticum aestivum L.) occurrence points and the main environmental factors that affect wheat growth, we used the Maxent model to predict the future global potential distribution and land suitability for wheat cultivation under multiple global climate change scenarios. Our results indicated that the suitability for wheat cultivation is primarily influenced by climatic factors and that the ≥0 °C accumulated temperature is especially important. The RCP4.5 scenario is more favourable for wheat cultivation, whereas the RCP8.5 scenario is the least favourable. Moreover, land suitability for wheat cultivation increased in Europe, Russia, the United States, Canada, China, and Pakistan, whereas a decreasing trend in suitability was found in Central and Eastern Africa, Australia, and South India. Overall, climate change is predicted to increase land suitability for wheat cultivation in middle- and high-latitude areas, and to decrease suitability in low latitude areas. Although the global distribution of wheat will not significantly alter with climate change, the risks to wheat cultivation may be significantly higher in the future because of high temperatures, heat waves, and droughts caused by climate change.

Spatial-Temporal Pattern of Human-Induced Land Degradation in Northern China in the Past 3 Decades-RESTREND Approach.

Land degradation is one of the world's most serious environmental issues. Human activities play an important role in it. Therefore, human-induced land degradation monitoring is of crucial scientific significance in revealing the evolution of land degradation and guiding its governance. Based on the residual trend (RESTREND) approach and using Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI) 3g and monthly precipitation as data sources, a quantitative evaluation is conducted on the conditions of human-induced land degradation during 1982-2012 in northern China. The results indicate that (1) the "optimal cumulative precipitation-NDVImax" regression model constructed herein can improve the capability of recognizing human-induced land degradation of arid and semiarid areas in the RESTREND approach. Moreover, long time-series NDVI and precipitation data may reduce the uncertainty of quantifying human-induced land degradation. (2) In the past 3 decades, northern China has experienced three stages of human-induced land degradation, i.e., rapid development, overall reversal with local development, and continuous reversion. Human-induced land degradation in the agro-pastoral ecotone of northern China has shown a rapid restoration trend since the 1990s. (3) It is believed that the dominant factor of land degradation has a significant spatial-temporal scale effect and spatial heterogeneity. Therefore, concrete issues should be specifically analyzed to improve our understanding of land degradation development and reversal, the spatial-temporal pattern and the driving forces of land degradation in the past 3 decades in northern China. Climate change may be the main driving force of land degradation. However, the influence of human activities on the development and reversal of land degradation in small areas and in a short time is more remarkable.

Vulnerability of cotton subjected to hail damage.

This paper establishes the quantitative relationships between hail fall parameters and crop damages by examining the impacts of 49 hail hazard scenarios on cotton in the bud stage and boll stage. This study utilizes simulated cotton hail hazard to analyze the following data: hail size, hail fall density, and crop damages (i.e., defoliation rate, branch breaking rate, and the fruit falling rate). The results are as follows: 1) cotton vulnerability increased via an increase in crop damages as the hail hazard magnitude increased; 2) crop damages exhibit significant logistic relationships with hail diameter and hail fall density, and the fit was better at the bud stage than at the boll stage; 3) cotton is more vulnerable to hail hazard at the bud stage than at the boll stage, and the bud stage is a critical period for cotton hail disaster prevention and mitigation; and 4) damages to cotton plant at the bud stage and boll stage were less sensitive to hail size from hail fall density. Thus, we suggest that hail diameter can be used as the priority indicator to predict hail-induced crop damages. These results provide a sound basis for developing a comprehensive evaluation of hail damage in cotton, which is crucial for promoting sustainable cotton production. We recommend that the impacts of hail-induced crop damages on yield and fiber quality need to be addressed further in future studies.

Temporal and Spatial Variations of Drought in China: Reconstructed from Historical Memorials Archives during 1689-1911.

In China, Zou Zhe (Memorials to the Throne, or Palace Memorials), an official communication to the emperors of China by local officials, offers an opportunity to reconstruct the spatial-temporal distributions of droughts at a high-resolution. A 223-year, 1689-1911, time series of drought events was reconstructed in this study based on 2494 pieces of Zou Zhe. The results show that: 1) on the temporal scale, the drought affected areas, i.e., number of affected counties, showed three peak periods during the last 223 years and nine extreme drought years with more than 300 counties affected have been identified; 2) on the spatial scale, there existed three drought-prone areas in China, i.e., Gansu province and Ningxia Hui Autonomous Region in Northwest China, Shandong, Hebei, and Henan provinces and Tianjin in the North China, and Anhui and Jiangsu provinces in Jianghuai area, respectively; 3) the drought-prone areas have been expanding from North China to South China since the second half of 19th century; 4) on the seasonal scale, summer witnessed the largest number of drought events. Meanwhile, the uncertainties of the results were also discussed, i.e. what caused the spatial-temporal distribution of drought. The results of this study can be used to mitigate the adverse effects of extreme weather events on food increasing and stable production.