Corrigendum to "Correlating grain yield with irrigation in a spatio-temporal context on the North China Plain"[Heliyon Volume 10, Issue 12, June 2024, Article e32745].

[This corrects the article DOI: 10.1016/j.heliyon.

Correlating grain yield with irrigation in a spatio-temporal context on the North China Plain.

Irrigation plays a crucial role in enhancing agricultural productivity. The spatiotemporal variability and correlation between historical irrigation and grain yield not only illuminate existing challenges in irrigation, but also offer valuable insights for formulating effective irrigation strategies, which have been previously overlooked. Taking the North China Plain (NCP) as a case study, this study aims to elucidate regional divergence patterns and the dynamic evolution of the spatiotemporal relationship between grain yield and irrigation through time series analysis, GIS spatial analysis, and geographically weighted regression (GWR).

Global wheat planting suitability under the 1.5°C and 2°C warming targets.

The potential distribution of crops will be impacted by climate change, but there is limited research on potential wheat distributions under specific global warming targets. This study employed the Maxent model to predict the potential distribution of wheat under the 1.5°C and 2°C warming targets based on data from the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) multimodel ensemble, and the effect of global warming on wheat planting suitability was analyzed.

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.

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.

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.

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.

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.

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.

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.