Probabilistic assessment of drought impacts on wheat yield in south-eastern Australia

Xiang, Keyu; Wang, Bin; Liu, De Li; Chen, Chao; Waters, Cathy; Huete, Alfredo; Yu, Qiang

doi:10.1016/j.agwat.2023.108359

Cited by 7 publications

(2 citation statements)

References 70 publications

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“…Yield data obtained from the Changchun Statistical Yearbook (Changchun Bureau of Statistics & Changchun Investigation Team of the National Bureau of Statistics, 2011–2021) were employed to evaluate the response of crop yields to chilling damage. As maize yields vary substantially due to the regional climatic and economic conditions (Xiang et al., 2023), the effects of technological progress on yield should be corrected before investigation and analyses, such as the local‐specific characteristics of soil, strategy and management conditions, which may cause variation in the final yield in different counties over time (Zhang, Hao, et al., 2023). The detrending yield data were used and calculated by the 3‐y moving average method (Meng et al., 2022) to represent the yield affected by meteorological factors.…”

Section: Methodsmentioning

confidence: 99%

Regional detection and assessment of chilling damage on maize considering land surface temperature, crop growth status and solar radiation changes

Zhang,

Cai,

et al. 2024

J Agronomy Crop Science

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Increased frequency and severity of chilling damage events pose potential risks to crop performance and productivity due to climate change. Accurate and real‐time access to chilling damage is important for crop growth and yield stability based on field's actual environment. To precisely identify regional chilling events and evaluate the impacts on crops, this study presents a model to estimate field air temperature in view of field crop situations. Land surface temperature, enhanced vegetation index, solar‐induced chlorophyll fluorescence and solar declination were involved in the model. With field simultaneous continuous monitoring and multisource fused remote sensing data, the model was calibrated and validated in Jiefangzha Irrigation Area (JIA) and Changchun City (CC) in North China, accompanied by the determination coefficient ≥0.756, root mean square error ≤0.782°C, relative error ≤0.041 and consistency index ≥0.902. Meanwhile, sensitivities of the model factors were determined through path analysis, where the factors performed according to the order solar‐induced chlorophyll fluorescence >solar declination >land surface temperature > enhanced vegetation index. Using the validated model, chilling damage to maize was further detected in JIA and CC from 2010 to 2020. Results showed that the severity of chilling damage was greater in CC than in JIA, along with the sterile‐type occurring three events in JIA and seven in CC, while the delayed‐type only twice in JIA in 2012 and 2016, but five times in CC in 2013, 2014, 2016, 2017 and 2019, respectively, being consistent with local statistics. In response to chilling damage, enhanced vegetation index and solar‐induced chlorophyll fluorescence demonstrated the negative chilling effects on greenness and light use efficiency for fluorescence. Serious yield losses were caused, with yield‐reducing by 5.00% (Dehui, 2013), 19.00% (Jiutai, 2014), 21.65% (Suburban district, 2016), 8.83% (Shuangyang, 2017) and 2.19% (Jiutai, 2019) in CC. The linear relationship between yield and growing degree days was a bit weakened by chilling damage, with the determination coefficient varying from 0.614 to 0.531. The increasing rate of yield with growing degree days decreased from 20.365 kg/(°C·d) in non‐chilling damage years to 9.670 kg/(°C·d) in chilling damage years. These findings indicate that the presented model is especially adaptive for agricultural field environments, enabling rapid precision detection of chilling damage on crops at regional scales. It will provide references for gauging the impact of chilling damage on crops, finding efficient solutions to the stress and ensuring sustainable development of agriculture.

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Section: Methodsmentioning

confidence: 99%

Regional detection and assessment of chilling damage on maize considering land surface temperature, crop growth status and solar radiation changes

Zhang,

Cai,

et al. 2024

J Agronomy Crop Science

View full text Add to dashboard Cite

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“…Studies are becoming more specialized, ranging from the provincial [12] to arable land levels [13]. Research crops include rice [14], corn [15], wheat [16,17], soybean [18], and kiwifruit [19]. The research spans countries such as China [20], India [21,22], the United States [23], Thailand [24], the Philippines [25], and Nepal [26].…”

Section: Introductionmentioning

confidence: 99%

Spatial Analysis and Risk Assessment of Meteorological Disasters Affecting Cotton Cultivation in Xinjiang: A Comprehensive Model Approach

Zhang,

Chen,

Ding

et al. 2024

Sustainability

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A systematic understanding of the spatial distribution of meteorological disasters that affect cotton growth, such as rainstorms, gales, and hail, is important for reducing plant losses and promoting sustainable development. Our study aimed to evaluate the risk of meteorological disasters during cotton growth and analyze their spatial distribution and driving factors. A risk assessment model for major meteorological disasters during cotton cultivation in Xinjiang was established by integrating entropy weight methods and an analytic hierarchy process. A cotton meteorological disaster risk assessment index system, including the vulnerability of disaster-bearing bodies, hazards of disaster-causing factors, and exposure of disaster-bearing bodies, was constructed using Google Earth Engine. We determined the comprehensive risk levels of major meteorological disasters for cotton in various regions of Xinjiang. Research shows that the selection of indicators is very important, and crop risk assessment with a clear disaster-bearing body can make the results more accurate. It is necessary to consider the risk assessment of multiple disaster species for meteorological disaster risk assessment. The results revealed spatial differences in the meteorological disaster risk for cotton in 2020. The very high and high risks for cotton accounted for 42% of the cotton planting area, mainly distributed in Karamay, Tacheng, Kashgar, Changjizhou, Kezhou, and Ilizhou. Consequently, this study provides a scientific basis for cotton cultivation in Xinjiang, China.

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Prediction of drought trigger thresholds for future winter wheat yield losses in China based on the DSSAT-CERES-Wheat model and Copula conditional probabilities

Yang,

Liu,

Liu

et al. 2024

Agricultural Water Management

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Probabilistic assessment of drought impacts on wheat yield in south-eastern Australia

Cited by 7 publications

References 70 publications

Regional detection and assessment of chilling damage on maize considering land surface temperature, crop growth status and solar radiation changes

Regional detection and assessment of chilling damage on maize considering land surface temperature, crop growth status and solar radiation changes

Spatial Analysis and Risk Assessment of Meteorological Disasters Affecting Cotton Cultivation in Xinjiang: A Comprehensive Model Approach

Prediction of drought trigger thresholds for future winter wheat yield losses in China based on the DSSAT-CERES-Wheat model and Copula conditional probabilities

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