Decoding Cropland Mask Effects on the Explanatory Power of Remote Sensing and Reanalyzed Climate Data on Yield Anomalies in Africa DOI Creative Commons
Wanxue Zhu, Ting Yang, Jundong Wang

и другие.

Earth s Future, Год журнала: 2025, Номер 13(6)

Опубликована: Июнь 1, 2025

Abstract Ensuring crop yield stability is crucial for food security in Africa, where agriculture faces increasing demand amid considerable vulnerabilities. Remote sensing and reanalyzed data products offer the potential capturing growth dynamics understanding their drivers. However, impacts of cropland masks on relative anomalies (RYA) contributions variables across Africa crops remain unclear. This study explores explanatory power air land surface temperatures (AT LST), precipitation, evapotranspiration, soil moisture maize, millet, sorghum RYA 2001–2020 under seven with distinct configurations temporal, type, water supply systems. Results indicate that (a) North was particularly affected by variation West strongly impacted Central East were highly influenced mean AT total South mainly high LST, precipitation variation. (b) Interactions between LST improved multiple stepwise regression model from 67% to 73%, while random forest considering complex variable interactions reached 83%. (c) Variables less choice masks. Mask broader coverage compensated limitations temporally static masks, type identification enhanced when using year‐specific crop‐specific maps. Future research should integrate process‐based models better understand mechanisms behind diverse drivers at regional scale Africa.

Язык: Английский

Performance of different corn hybrids and their corresponding water consumption analysis under various water management practices: Insights from experiments conducted under rain-shelters based on the TOPSIS method DOI

Lei Wang,

Jing Chen, Zhenyu Chu

и другие.

European Journal of Agronomy, Год журнала: 2025, Номер 168, С. 127587 - 127587

Опубликована: Март 25, 2025

Язык: Английский

Процитировано

1
Decoding Cropland Mask Effects on the Explanatory Power of Remote Sensing and Reanalyzed Climate Data on Yield Anomalies in Africa DOI Creative Commons
Wanxue Zhu, Ting Yang, Jundong Wang

и другие.

Earth s Future, Год журнала: 2025, Номер 13(6)

Опубликована: Июнь 1, 2025

Abstract Ensuring crop yield stability is crucial for food security in Africa, where agriculture faces increasing demand amid considerable vulnerabilities. Remote sensing and reanalyzed data products offer the potential capturing growth dynamics understanding their drivers. However, impacts of cropland masks on relative anomalies (RYA) contributions variables across Africa crops remain unclear. This study explores explanatory power air land surface temperatures (AT LST), precipitation, evapotranspiration, soil moisture maize, millet, sorghum RYA 2001–2020 under seven with distinct configurations temporal, type, water supply systems. Results indicate that (a) North was particularly affected by variation West strongly impacted Central East were highly influenced mean AT total South mainly high LST, precipitation variation. (b) Interactions between LST improved multiple stepwise regression model from 67% to 73%, while random forest considering complex variable interactions reached 83%. (c) Variables less choice masks. Mask broader coverage compensated limitations temporally static masks, type identification enhanced when using year‐specific crop‐specific maps. Future research should integrate process‐based models better understand mechanisms behind diverse drivers at regional scale Africa.

Язык: Английский

Процитировано

0