Research on the Relationships Between Rainfall and Meteorological Yield in Irrigation District

Zhang, Jinping; Zhao, Yong; Ding, Zhihong

doi:10.1007/s11269-014-0577-3

Cited by 16 publications

(7 citation statements)

References 24 publications

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“…To analyse the applicability of the ACDI in agricultural drought identification, the relationship between the ACDI and grain yields is analysed below. The most direct result of agricultural production is grain, so grain production can naturally reflect the state of which is generally called the trend yield (Liu et al, 2015;Zhang et al, 2014). Natural factors include climatic factors, topographic factors and geomorphological factors.…”

Section: Comparative Analysismentioning

confidence: 99%

Study on the agricultural crop drought index based on weights of growth stages

Pei

Ren

et al. 2022

Hydrological Processes

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Drought is one of the main natural disasters affecting economic development and food security. Drought monitoring and identification are the premise of disaster risk management, and the drought index is the main means of monitoring and identifying drought. The paper proposes a new drought index, the agricultural crop drought index (ACDI), which is suitable for rainfed agriculture in arid areas. ACDI calculates water deficit from precipitation and evapotranspiration and considers the weight of crop growth stage. The weight of growth stage and water deficit constitute the weighted data series. The fitting distribution is optimized by Kolmogorov-Smirnov test, and the data are standardized to obtain ACDI. The weight of growth stage is determined by the multiple correlation coefficient between meteorological factors and grain yield. A large weight is equivalent to a large coefficient, large coefficient means that meteorological factors in this growth stage will have a greater impact on grain yield, so the water deficit in this growth stage should play a more important role in defining drought index to better identify agricultural drought from the perspective of crops. Finally, Qiqihar, a semiarid area in Northeast China, is selected as the research area for the case study. Comparing ACDI with standard precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI) and crop water deficit index (CWDI), the standard deviation of ACDI is larger, which indicates that the difference between index values is larger and it is relatively good in distinguishing the degree of drought. In addition, the correlation coefficient between ACDI and climate grain yield passed the test with a significance level of 0.05, while the other three indexes did not pass the test, which shows that the relationship between ACDI and grain yield is higher than the other three drought indexes. For arid areas, grain yield can reflect the impact of drought. Therefore, ACDI looks more promising to identify agricultural drought from the perspective of crops. ACDI provides a reference for monitoring agricultural drought from the perspective of crop growth stage, and enriches the drought index theory.

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Section: Comparative Analysismentioning

confidence: 99%

Study on the agricultural crop drought index based on weights of growth stages

Pei

Ren

et al. 2022

Hydrological Processes

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“…Because the canola flower is a key indicator of seed yield, the census canola yield of Qujing, Haibei, Jingmen, and Hulun Buir from 2003 to 2017 was used for validating temporal variation of the EAYI, whereas the census yield of Yili from 2006 to 2017 was used for validation due to the lack of census data before 2006 in Yili. In the long-term analysis of crop yield, the actual crop yield (Y) is generally decomposed into three parts [17][18][19]: trend yield (τ), meteorological yield (W), and random error (ε) as follows:…”

Section: Mapping and Evaluation Of The Eayimentioning

confidence: 99%

Remote Sensing Index for Mapping Canola Flowers Using MODIS Data

Zang

Chen

et al. 2020

Remote Sensing

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Mapping and tracing the changes in canola planting areas and yields in China are of great significance for macro-policy regulation and national food security. The bright yellow flower is a distinctive feature of canola, compared to other crops, and is also an important factor in predicting canola yield. Thus, yellowness indices were previously used to detect the canola flower using aerial imagery or median-resolution satellite data like Sentinel-2. However, it remains challenging to map the canola planting area and to trace long-term canola yields in China due to the wide areal extent of cultivation, different flowering periods in different locations and years, and the lack of high spatial resolution data within a long-term period. In this study, a novel canola index, called the enhanced area yellowness index (EAYI), for mapping canola flowers and based on Moderate Resolution Imaging Spectroradiometer (MODIS) time-series data, was developed. There are two improvements in the EAYI compared with previous studies. First, a method for estimating flowering period, based on geolocation and normalized difference vegetation index (NDVI) time-series, was established, to estimate the flowering period at each place in each year. Second, the EAYI enhances the weak flower signal in coarse pixels by combining the peak of yellowness index time-series and the valley of NDVI time-series during the estimated flowering period. With the proposed EAYI, canola flowering was mapped in five typical canola planting areas in China, during 2003-2017. Three different canola indices proposed previously, the normalized difference yellowness index (NDYI), ratio yellowness index (RYI) and Ashourloo canola index (Ashourloo CI), were also calculated for a comparison. Validation using the samples interpreted through higher resolution images demonstrated that the EAYI is better correlated with the reference canola coverage with R2 ranged from 0.31 to 0.70, compared to the previous indices with R2 ranged from 0.02 to 0.43. Compared with census canola yield data, the total EAYI was well correlated with actual yield in Jingmen, Yili and Hulun Buir, and well correlated with meteorological yields in all five study areas. In contrast, previous canola indices show a very low or even a negative correlation with both actual and meteorological yields. These results indicate that the EAYI is a potential index for mapping and tracing the change in canola areas, or yields, with MODIS data.

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“…The grain yield can be decomposed into the trend yield and fluctuation yield (Liu et al 2015;Zhang et al 2014). The trend yield is a component of the crop yield influenced by productivity, which is related to social and economic factors, scientific and technological factors, etc.…”

Section: Adaptive Capacitymentioning

confidence: 99%

A Novel Method for Agricultural Drought Risk Assessment

Pei

et al. 2019

Water Resour Manage

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Climate change, increased temperatures and imbalanced precipitation distributions will potentially increase the local drought risk in certain areas. Drought assessment can identify the hidden dangers of drought and provide a theoretical basis for disaster prevention and mitigation. In this paper, a new agricultural drought risk assessment method proposed from the perspective of grain yield. The first principal components of precipitation, temperature, humidity and soil moisture represent hazard factors. The sensitive yield, which represents the sensitivity, was separated from the grain yield using a regression method. Additionally, the trend component of the grain yield represents the adaptive capacity, and the crop planting area represents exposure. Based on these definitions, the concepts of unit drought risk and regional drought risk are proposed. Four cities in Heilongjiang Province, which has the highest grain yield of any province in China, were used as application examples, and the spatial and temporal variation in the agricultural drought risk were analyzed. Application example show that the method for evaluating agricultural drought presented in this paper is reasonable in a statistical sense. The process for calculating sensitivity and adaptability shows that this method is suitable for arid and semi-arid areas, where grain yield is sensitive to hazard factors, and areas where grain yield has a certain trend.

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Research on the Relationships Between Rainfall and Meteorological Yield in Irrigation District

Cited by 16 publications

References 24 publications

Study on the agricultural crop drought index based on weights of growth stages

Study on the agricultural crop drought index based on weights of growth stages

Remote Sensing Index for Mapping Canola Flowers Using MODIS Data

A Novel Method for Agricultural Drought Risk Assessment

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