Rainfall variability and its effects on growing period and grain yield for rainfed lowland rice under transplanting system in Northeast Thailand

Sujariya, Sukanya; Jongrungklang, Nuntawoot; Jongdee, Boonrat; Inthavong, Thavone; Budhaboon, Chitnucha; Fukai, S.

doi:10.1080/1343943x.2019.1698970

Cited by 15 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of note is that outliers and seasonality typically characterise a time series of NDVI data in regions characterised by dry and wet season [40]. The single modal annual rainfall pattern of the study area shows that rainfall (or agricultural season) starts in October and ends in April, indicating a time lag between rainfall and crop phenophases [41] (Figure 5). As rainfall is a crucial factor in the seasonal crop cycle, there is a positive correlation between rainfall and the seasonal crop growth pattern [42].…”

Section: Ndvi Trend Analysis Over Cultivated Landmentioning

confidence: 94%

Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

et al. 2021

View full text Add to dashboard Cite

Improvements in irrigated areas' classification accuracy are critical to enhance agricultural water management and inform policy and decision-making on irrigation expansion and land use planning. This is particularly relevant in water-scarce regions where there are plans to increase the land under irrigation to enhance food security, yet the actual spatial extent of current irrigation areas is unknown. This study applied a non-parametric machine learning algorithm, the random forest, to process and classify irrigated areas using images acquired by the Landsat and Sentinel satellites, for Mpumalanga Province in Africa. The classification process was automated on a big-data management platform, the Google Earth Engine (GEE), and the R-programming was used for post-processing. The normalised difference vegetation index (NDVI) was subsequently used to distinguish between irrigated and rainfed areas during 2018/19 and 2019/20 winter growing seasons. High NDVI values on cultivated land during the dry season are an indication of irrigation. The classification of cultivated areas was for 2020, but 2019 irrigated areas were also classified to assess the impact of the Covid-19 pandemic on agriculture. The comparison in irrigated areas between 2019 and 2020 facilitated an assessment of changes in irrigated areas in smallholder farming areas. The approach enhanced the classification accuracy of irrigated areas using ground-based training samples and very high-resolution images (VHRI) and fusion with existing datasets and the use of expert and local knowledge of the study area. The overall classification accuracy was 88%.

show abstract

Section: Ndvi Trend Analysis Over Cultivated Landmentioning

confidence: 94%

Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The groundtruth points used were randomly chosen, and information on each was compared between Outliers and seasonality typically characterise a time series of NDVI data in regions characterised by dry and wet seasons [49]. The single modal annual rainfall pattern of the study area shows that rainfall (or agricultural season) starts in October and ends in April, indicating a time lag between rainfall and seasonal crop phenophases [50] (Figure 3). As rainfall is a key factor in the seasonal crop cycle, there is a positive correlation between rainfall and the seasonal crop growth pattern [51].…”

Section: Mapping Irrigated and Rainfed Areasmentioning

confidence: 99%

Informing Equitable Water and Food Policies through Accurate Spatial Information on Irrigated Areas in Smallholder Farming Systems

Magidi

Koppen

Nhamo

et al. 2021

Water

View full text Add to dashboard Cite

Accurate information on irrigated areas’ spatial distribution and extent are crucial in enhancing agricultural water productivity, water resources management, and formulating strategic policies that enhance water and food security and ecologically sustainable development. However, data are typically limited for smallholder irrigated areas, which is key to achieving social equity and equal distribution of financial resources. This study addressed this gap by delineating disaggregated smallholder and commercial irrigated areas through the random forest algorithm, a non-parametric machine learning classifier. Location within or outside former apartheid “homelands” was taken as a proxy for smallholder, and commercial irrigation. Being in a medium rainfall area, the huge irrigation potential of the Inkomati-Usuthu Water Management Area (UWMA) is already well developed for commercial crop production outside former homelands. However, information about the spatial distribution and extent of irrigated areas within former homelands, which is largely informal, was missing. Therefore, we first classified cultivated lands in 2019 and 2020 as a baseline, from where the Normalised Difference Vegetation Index (NDVI) was used to distinguish irrigated from rainfed, focusing on the dry winter period when crops are predominately irrigated. The mapping accuracy of 84.9% improved the efficacy in defining the actual spatial extent of current irrigated areas at both smallholder and commercial spatial scales. The proportion of irrigated areas was high for both commercial (92.5%) and smallholder (96.2%) irrigation. Moreover, smallholder irrigation increased by over 19% between 2019 and 2020, compared to slightly over 7% in the commercial sector. Such information is critical for policy formulation regarding equitable and inclusive water allocation, irrigation expansion, land reform, and food and water security in smallholder farming systems.

show abstract

“…Other scholars place greater emphasis on the impact of rainfall characteristics during specific rice growth periods on rice cultivation [23][24][25]. Furthermore, the impact of climate change-induced alterations in rainfall patterns on rice production was investigated, and models were developed to simulate how these changes will influence the growth period and rice production under future climatic scenarios [26][27][28]. The studies above primarily concentrate on the correlation between the rainfall and crop yield.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Rainfall Pattern and Rainfall Utilization Efficiency during the Growth Period of Paddy Rice

Chen,

Shen,

Wang

et al. 2024

Agronomy

View full text Add to dashboard Cite

Rainfall is one of the most important water sources for rice production in China. However, its temporal and spatial variability is leading to water shortages. The present study collected a long series of historical rainfall data from research sites during the rice growth period to analyze the characteristics of rainfall distribution and the correlation with rainfall utilization efficiency, aiming to investigate its impact on rice irrigation practices. It is found that the rainfall distribution varied greatly between the different locations and growth periods. The average rainfall of the whole growth period ranges from 135.5 mm to 694.5 mm. The rainfall curve exhibits a typical unimodal pattern with variations in the intensity, duration, and timing of peak precipitation across different growth periods. During the rice growth period, the cases in southern China are more prone to waterlogging for a high probability of continuous rainfall, and the cases in northern China are more prone to drought. The rainfall utilization efficiency of all cases exhibits a significant inter-year fluctuation range, negatively influenced by the rainfall amount and rainfall inhomogeneity. The efficiency in utilizing precipitation is diminished with greater and more uneven rainfall experienced during the growth period. These findings can provide a decision-making basis for optimizing rice irrigation strategies and enhancing rainfall utilization efficiency in diverse regions across China.

show abstract

Rainfall variability and its effects on growing period and grain yield for rainfed lowland rice under transplanting system in Northeast Thailand

Cited by 15 publications

References 21 publications

Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

Informing Equitable Water and Food Policies through Accurate Spatial Information on Irrigated Areas in Smallholder Farming Systems

Analysis of the Rainfall Pattern and Rainfall Utilization Efficiency during the Growth Period of Paddy Rice

Contact Info

Product

Resources

About