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Analyzing adaptation strategies for maize production under future climate change in Guanzhong Plain, China
https://orcid.org/0000-0002-6822-7741
Abstract Agricultural adaptation is crucial for sustainable farming amid global climate change. By harnessing projected climate data and using crop modeling techniques, the future trends of food production can be predicted and better adaptation strategies can be assessed. The main objective of this study is to analyze the maize yield response to future climate projections in the Guanzhong Plain, China, by employing multiple crop models and determining the effects of irrigation and planting date adaptations. Five crop models (APSIM, AquaCrop, DSSAT, EPIC, and STICS) were used to simulate maize (Zea mays L.) yield under projected climate conditions during the 2030s, 2050s, and 2070s, based on the combination of 17 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs 6.0 and 8.5). Simulated scenarios included elevated and constant $ CO_{2} $ levels under current adaptation (no change from current irrigation amount, planting date, and fertilizer rate), irrigation adaptation, planting date adaptation, and irrigation-planting date adaptations. Results from both maize-producing districts showed that current adaptation practices led to a decrease in the average yield of 19%, 27%, and 33% (relative to baseline yield) during the 2030s, 2050s, and 2070s, respectively. The future yield was projected to increase by 1.1–23.2%, 1.0–22.3%, and 2–31% under irrigation, delayed planting date, and double adaptation strategies, respectively. Adaptation strategies were found effective for increasing the future average yield. We conclude that maize yield in the Guanzhong Plain can be improved under future climate change conditions if irrigation and planting adaptation strategies are used in conjunction.
Analyzing adaptation strategies for maize production under future climate change in Guanzhong Plain, China
https://orcid.org/0000-0002-6822-7741
Abstract Agricultural adaptation is crucial for sustainable farming amid global climate change. By harnessing projected climate data and using crop modeling techniques, the future trends of food production can be predicted and better adaptation strategies can be assessed. The main objective of this study is to analyze the maize yield response to future climate projections in the Guanzhong Plain, China, by employing multiple crop models and determining the effects of irrigation and planting date adaptations. Five crop models (APSIM, AquaCrop, DSSAT, EPIC, and STICS) were used to simulate maize (Zea mays L.) yield under projected climate conditions during the 2030s, 2050s, and 2070s, based on the combination of 17 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs 6.0 and 8.5). Simulated scenarios included elevated and constant $ CO_{2} $ levels under current adaptation (no change from current irrigation amount, planting date, and fertilizer rate), irrigation adaptation, planting date adaptation, and irrigation-planting date adaptations. Results from both maize-producing districts showed that current adaptation practices led to a decrease in the average yield of 19%, 27%, and 33% (relative to baseline yield) during the 2030s, 2050s, and 2070s, respectively. The future yield was projected to increase by 1.1–23.2%, 1.0–22.3%, and 2–31% under irrigation, delayed planting date, and double adaptation strategies, respectively. Adaptation strategies were found effective for increasing the future average yield. We conclude that maize yield in the Guanzhong Plain can be improved under future climate change conditions if irrigation and planting adaptation strategies are used in conjunction.
Analyzing adaptation strategies for maize production under future climate change in Guanzhong Plain, China
https://orcid.org/0000-0002-6822-7741
Abstract Agricultural adaptation is crucial for sustainable farming amid global climate change. By harnessing projected climate data and using crop modeling techniques, the future trends of food production can be predicted and better adaptation strategies can be assessed. The main objective of this study is to analyze the maize yield response to future climate projections in the Guanzhong Plain, China, by employing multiple crop models and determining the effects of irrigation and planting date adaptations. Five crop models (APSIM, AquaCrop, DSSAT, EPIC, and STICS) were used to simulate maize (Zea mays L.) yield under projected climate conditions during the 2030s, 2050s, and 2070s, based on the combination of 17 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs 6.0 and 8.5). Simulated scenarios included elevated and constant $ CO_{2} $ levels under current adaptation (no change from current irrigation amount, planting date, and fertilizer rate), irrigation adaptation, planting date adaptation, and irrigation-planting date adaptations. Results from both maize-producing districts showed that current adaptation practices led to a decrease in the average yield of 19%, 27%, and 33% (relative to baseline yield) during the 2030s, 2050s, and 2070s, respectively. The future yield was projected to increase by 1.1–23.2%, 1.0–22.3%, and 2–31% under irrigation, delayed planting date, and double adaptation strategies, respectively. Adaptation strategies were found effective for increasing the future average yield. We conclude that maize yield in the Guanzhong Plain can be improved under future climate change conditions if irrigation and planting adaptation strategies are used in conjunction.
Analyzing adaptation strategies for maize production under future climate change in Guanzhong Plain, China
Saddique, Qaisar (author) / Cai, Huanjie (author) / Xu, Jiatun (author) / Ajaz, Ali (author) / He, Jianqiang (author) / Yu, Qiang (author) / Wang, Yunfei (author) / Chen, Hui (author) / Khan, Muhammad Imran (author) / Liu, De Li (author)
2020
Article (Journal)
Electronic Resource
English
BKL:
43.47
Globale Umweltprobleme
/
43.47$jGlobale Umweltprobleme
Ecological Sensitivity of Urban Agglomeration in the Guanzhong Plain, China
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