Evaluation of cultivation environment and management based on LAI measurement in farmers’ paddy fields in Pursat province, Cambodia

Hirooka, Yoshihiro; Homma, Koki; Kodo, Taisuke; Shiraiwa, Tatsuhiko; Kim, Soben; Chann, Mithona; Tsujimoto, Kumiko; Tamagawa, Katsunori; Koike, Takao

doi:10.1016/j.fcr.2016.08.031

Cited by 7 publications

(6 citation statements)

References 23 publications

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“…In addition, MODIS data are affected by the mixture of components, such as houses and roads, because of their resolution. For example, some LAI values obtained in this study were relatively low compared with those measured directly in the farmers' fields [18]. Furthermore, changes in LAI values were difficult to quantify in pixels, which had low proportions of paddy fields.…”

Section: Discussionmentioning

confidence: 58%

“…The results were used to understand the LAI changes at each location because the farmers' field did not exactly match the analyzed pixel. 18 August 2020 for 2019). MCD15A2H contains a combined product, including the fraction of photosynthetically active radiation (FPAR) and leaf area index (LAI), of an 8-day composite dataset with a 500-m pixel size.…”

Section: Interview With Farmers On Cultivation Managementmentioning

confidence: 99%

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Quantification of Changes in Rice Production for 2003–2019 with MODIS LAI Data in Pursat Province, Cambodia

Iwahashi¹,

Ye²,

Kobayashi

et al. 2021

Remote Sensing

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Rice is not merely a staple food but an important source of income in Cambodia. Rapid socioeconomic development in the country affects farmers’ management practices, and rice production has increased almost three-fold over two decades. However, detailed information about the recent changes in rice production is quite limited and mainly obtained from interviews and statistical data. Here, we analyzed MODIS LAI data (MCD152H) from 2003 to 2019 to quantify rice production changes in Pursat Province, one of the great rice-producing areas in Cambodia. Although the LAI showed large variations, the data clearly indicate that a major shift occurred in approximately 2010 after applying smoothing methods (i.e., hierarchical clustering and the moving average). This finding is consistent with the results of the interviews with the farmers, which indicate that earlier-maturing cultivars had been adopted. Geographical variations in the LAI pattern were illustrated at points analyzed along a transverse line from the mountainside to the lakeside. Furthermore, areas of dry season cropping were detected by the difference in monthly averaged MODIS LAI data between January and April, which was defined as the dry season rice index (DSRI) in this study. Consequently, three different types of dry season cropping areas were recognized by nonhierarchical clustering of the annual LAI transition. One of the cropping types involved an irrigation-water-receiving area supported by canal construction. The analysis of the peak LAI in the wet and dry seasons suggested that the increase in rice production was different among cropping types and that the stagnation of the improvements and the limitation of water resources are anticipated. This study provides valuable information about differences and changes in rice cropping to construct sustainable and further-improved rice production strategies.

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Section: Discussionmentioning

confidence: 58%

Section: Interview With Farmers On Cultivation Managementmentioning

confidence: 99%

Quantification of Changes in Rice Production for 2003–2019 with MODIS LAI Data in Pursat Province, Cambodia

Iwahashi¹,

Ye²,

Kobayashi

et al. 2021

Remote Sensing

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“…The parameterization method described by Hirooka et al (2016b) might be important in cultivated rice paddy fields in less productive areas such as Southeast Asia and Africa. Growth characteristics of the paddy fields were evaluated using LAI measurements (Hirooka, Homma, Kodo, et al, 2016;Hirooka et al, 2017). The results quantitatively show the limiting factors of productivity in these areas; however, the errors reported were not negligible and were likely due to cultivar differences.…”

Section: Discussionmentioning

confidence: 99%

A leaf area‐based non‐destructive approach to predict rice productivity

2021

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Leaf canopy dynamicsare associated with crop productivity. Frequent, nondestructive measurements of the leaf canopy using a plant canopy analyzer, followed by parameterization using a mathematical model, enabled the quantification of leaf area growth characteristics. However, an understanding of the rice (Oryza sativa L.) cultivar effect under various field conditions is limited. Therefore, this study aimed to compare leaf area growth parameters with rice productivity and analyze the cultivar differences in these relationships. In a 3-yr field experiment, six rice cultivars were grown under environments with different nutrient provisions. Two leaf area growth parameters (maximum leaf area index growth rate and maximum interception rate) were determined from the measurements obtained using a plant canopy analyzer.They were compared with five crop growth parameters for rice productivity (yield, total dry weight, crop growth rate, nitrogen uptake rate, and radiation use efficiency).A logarithmic relationship exists between leaf area growth and crop growth parameters, which varied among rice cultivars. In addition, the leaf nitrogen content at the heading stage was associated with the cultivar variations. After considering the cultivar differences, yield prediction accuracy was improved using leaf area growth parameters (from R 2 = .428 to R 2 = .752). This indicates that these parameters are considered efficient indicators of rice productivity with regard to cultivar differences and leaf nitrogen content. The evaluation method using the parameters calculated with non-destructive measurements could be a standard for crop monitoring in field experiments and will be useful for estimating crop productivity.

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“…1b) around Tonle Sap Lake in this study: the northeastern side of the lake (subarea 1), the area north of Battambang (subarea 2), the area south of Battambang (subarea 3), Pursat (subarea 4), and the area south of Phnom Penh (subarea 5). Paddy cultivation dominates these areas, and field surveys (Hirooka et al (2016) and our unpublished works) have been carried out in these regions on agricultural management and rice crop growth.…”

Section: Study Areamentioning

confidence: 99%

Multiyear analysis of the dependency of the planting date on rainfall and soil moisture in paddy fields in Cambodia, 2003–2019

Tsujimoto

Ono

Ohta³

et al. 2021

Paddy Water Environ

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The dependencies of the planting date on rainfall and soil moisture in paddy fields in Cambodia were analyzed to quantify farmers’ empirical knowledge regarding their decision of the planting date. Remote sensing data from multiple satellites covering the 2003–2019 period were analyzed. The planting dates in rain-fed paddies ranged from April to August, with large spatial variations and year-to-year fluctuations. In years when planting was suppressed in April and May, planting was extensively enhanced in June and August compared to normal years, and vice versa. Over the northeastern side of Tonle Sap Lake and south of Phnom Penh city, the areas planted in April and May were found to have positive correlations with rainfall and soil moisture, suggesting that wetter-than-average conditions encouraged farmers to plant earlier in the season. In contrast, this relationship was unclear on the western side of Tonle Sap Lake, where the rainfall amounts were larger throughout the year than in other areas in Cambodia. In this region, the relationship between the planting area and soil water availability was either unclear or was even slightly negative from June to August. Since more frequent dry spells have been detected after the onset of the rainy season in recent years, further studies and disseminations of potential changes in dry spells are important for the agronomic adaptation of planting dates under climate change.

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Evaluation of cultivation environment and management based on LAI measurement in farmers’ paddy fields in Pursat province, Cambodia

Cited by 7 publications

References 23 publications

Quantification of Changes in Rice Production for 2003–2019 with MODIS LAI Data in Pursat Province, Cambodia

Quantification of Changes in Rice Production for 2003–2019 with MODIS LAI Data in Pursat Province, Cambodia

A leaf area‐based non‐destructive approach to predict rice productivity

Multiyear analysis of the dependency of the planting date on rainfall and soil moisture in paddy fields in Cambodia, 2003–2019

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