Quantifying the GCM-related uncertainty for climate change impact assessment of rainfed rice production in Cambodia by a combined hydrologic - rice growth model

The detection of degraded soil distribution areas is an urgent task. It is difficult and very time consuming to solve this problem using ground methods. The modeling of degradation processes based on digital elevation models makes it possible to construct maps of potential degradation, which may differ from the actual spatial distribution of degradation. The use of remote sensing data (RSD) for soil degradation detection is very widespread. Most often, vegetation indices (indicative botany) have been used for this purpose. In this paper, we propose a method for constructing soil maps based on a multi-temporal analysis of the bare soil surface (BSS). It is an alternative method to the use of vegetation indices. The detection of the bare soil surface was carried out using the spectral neighborhood of the soil line (SNSL) technology. For the automatic recognition of BSS on each RSD image, computer vision based on deep machine learning (neural networks) was used. A dataset of 244 BSS distribution masks on 244 Landsat 4, 5, 7, and 8 scenes over 37 years was developed. Half of the dataset was used as a training sample (Landsat path/row 173/028). The other half was used as a test sample (Landsat path/row 174/027). Binary masks were sufficient for recognition. For each RSD pixel, value “1” was set when determining the BSS. In the absence of BSS, value “0” was set. The accuracy of the machine prediction of the presence of BSS was 75%. The detection of degradation was based on the average long-term spectral characteristics of the RED and NIR bands. The coefficient Cmean, which is the distance of the point with the average long-term values of RED and NIR from the origin of the spectral plane RED/NIR, was calculated as an integral characteristic of the mean long-term values. Higher long-term average values of spectral brightness served as indicators of the spread of soil degradation. To test the method of constructing soil degradation maps based on deep machine learning, an acceptance sample of 133 Landsat scenes of path/row 173/026 was used. On the territory of the acceptance sample, ground verifications of the maps of the coefficient Cmean were carried out. Ground verification showed that the values of this coefficient make it possible to estimate the content of organic matter in the plow horizon (R2 = 0.841) and the thickness of the humus horizon (R2 = 0.8599). In total, 80 soil pits were analyzed on an area of 649 ha on eight agricultural fields. Type I error (false positive) of degradation detection was 17.5%, and type II error (false negative) was 2.5%. During the determination of the presence of degradation by ground methods, 90% of the ground data coincided with the detection of degradation from RSD. Thus, the quality of machine learning for BSS recognition is sufficient for the construction of soil degradation maps. The SNSL technology allows us to create maps of soil degradation based on the long-term average spectral characteristics of the BSS.

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Recognition of the Bare Soil Using Deep Machine Learning Methods to Create Maps of Arable Soil Degradation Based on the Analysis of Multi-Temporal Remote Sensing Data

Red crown rot (RCR) is a soil-borne disease that damages soybean growth and decreases yield. Infected plants show earlier defoliation and pencil-like roots, sometimes resulting in mortality. This disease became common relatively recently, and information about its field-scale appearance is insufficient. Insufficient data is a major constraint when planning countermeasures. In this study, unmanned aerial vehicle (UAV)-acquired images were used to visualize the spatial and time series variation in the area damaged by RCR in the same farmer fields in 2018 and 2020. Field investigation showed that RCR severely damaged soybean production. The reductions of yield were estimated at 17.5% and 12.7% in 2018 and 2020, respectively. The visualized damage clarified the difference in the increasing rate and patterns of RCR between the 2 years. In 2018, the damaged area expanded along the planting row to the whole field, but in 2020, the expansion along the planting row was not great, and half of the fields remained sparsely damage. This difference implies that various factors are associated with damage occurrence and pathogen distribution. The method applied in this study is effective in visualizing RCR damage, but further improvement is required in the evaluation of intermediate damage and the generalization of the evaluation procedure. 

Monitoring spatial and time-series variations in red crown rot damage of soybean in farmer fields based on UAV remote sensing

Drought is increasingly threatening smallholder farmers in Southeast Asia. The crop insurance system is one of the promising countermeasures that was implemented in Indonesia in 2015. Because the damage assessment in the present system is conducted through direct investigations based on appearance, it is not objective and needs a long time to cover large areas. In this study, we investigated a rapid assessment method for paddy fields using a vegetation index (VI) taken by an unmanned aerial vehicle (UAV) with a multispectral camera in 2019 and 2021. Then, two ways of assessment for drought damage were tested: linear regression (LR) based on a visually assessed drought level (DL), and k-means clustering without an assessed DL. As a result, EVI2 could represent the damage level, showing the tendency of the decrease in the value along with the increasing DL. The estimated DL by both methods mostly coincided with the assessed DL, but the concordance rates varied depending on the locations and the number of assessed fields. Differences in the growth stage and rice cultivars also affected the results. This study revealed the feasibility of the UAV-based rapid and objective assessment method. Further data collection and analysis would be required for implementation in the future.

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Drought Damage Assessment for Crop Insurance Based on Vegetation Index by Unmanned Aerial Vehicle (UAV) Multispectral Images of Paddy Fields in Indonesia

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.

https://www.mdpi.com/2072-4292/13/10/1971/pdf

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

: Future expansion of salt-damaged areas is anticipated in Northeast Thailand. We conducted a field investigation of paddy fields from 2016 to 2019 in Ban Phai district, Khon Kaen province in Northeast Thailand to evaluate yearly changes in the effect of salinity damage on rice production. The investigation area was classified into severely salt-affected areas (2 nd of 5 classes) based on the definition used in Thailand. Since salinity severely damages rice produc‐ tion, rice cultivation was abandoned in some fields, although some were still planted. The soil electrical con‐ ductivity (EC) in the rice-planted paddy fields changed yearly in association with the amount of precipitation. The effect of the difference in EC on rice yield was moderate, suggesting that rice yield was mediated by surface water. Some areas in the abandoned fields did not have any vege‐ tation, and quite high soil EC values were observed. The non-vegetated areas evaluated based on yearly unmanned aerial vehicle (UAV) images changed partly due to the amount of precipitation. However, some non-vegetated areas decreased in contrast to the decrease in precipitation, probably because of the effect of groundwater. Although the continuous expansion of severely salt-damaged areas was not observed, the monitoring of salinity levels is rec‐ ommended for the future.

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Yearly change in severely salt-damaged areas in paddy fields in Ban Phai in Northeast Thailand

Northeast Thailand is the largest rice cultivation region in Thailand, but the rice yield there is quite low. Soil salinity is one of the major yield restricted factors, is derived from underground rock salt, and is predicted to expand in the future. This study focused on evaluating rice productivity related to salinity conditions in Khon Kaen Province, Northeast Thailand. The field investigations were conducted from 2017 to 2019 in farmer fields in severe, moderate, and slight soil salinity classes determined by the Land Development Department of Thailand. The soil salinity on the basis of the electric conductivity of saturated soil extract (ECe) varied year to year, which seemed to be associated with precipitation. The difference in soil salinity between classes was obvious only in the drought year 2018, and reflected in the rice yield, although severe drought devastated rice yield in some fields. Plenty of rainfall may have alleviated soil salinity and rice yield reduction in other years, causing differences in rice yield that were not significant among soil salinity classes. However, salinity level evaluation by the USDA based on ECe showed that rice yield was damaged depending on the level. This study indicates that ECe-based evaluation is recommended for soil salinity in relation to rice productivity. The spatial and temporal evaluation for rice production may benefit farmers. The results in this study also showed rice production largely varied even in similar salinity levels, implying that salinity damage can be alleviated by farmer management.

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Rice Production in Farmer Fields in Soil Salinity Classified Areas in Khon Kaen, Northeast Thailand

Improving agricultural research and education is highly recommended to control agricultural development and environmental sustainability in Cambodia. Agricultural research mostly focuses on interviews with farmers as a first measure in developing countries, but a lack of quantitative accuracy remains one of the major constraints. In this situation, we conducted educational activities for master’s degree students of the Royal University of Agriculture (RUA) to append agronomic information with popular equipment in interdisciplinary fieldwork in Pursat Province, Cambodia. For the popular equipment, an RGB camera, a reflectometer as well as pH and EC meters were selected. The agronomic information collected by the students supported the results obtained during the interviews. For example, the difference in fertilizer application between the irrigated and nonirrigated areas was confirmed by the soil ammonium concentration evaluated with a reflectometer; the difference in rice growth among water conditions was confirmed by the leaf area percentage evaluated with an RGB camera. Since the majority of the students lacked agricultural and statistical knowledge, the agronomic information quantified by popular equipment provided proper educational materials. The interdisciplinary fieldwork also indicated serious problems in the study area, such as low beneficial crop production and environmental sustainability. To overcome these problems, improving agricultural education is required to foster skillful agricultural professionals, and the quantification of agronomic information is an essential issue.

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Educational Trials to Quantify Agronomic Information in Interdisciplinary Fieldwork in Pursat Province, Cambodia

In crop production, which is largely dependent on environmental conditions, various attempts at environmental or social changes have been highlighted, and many field experiments are needed for them. However, since field experiments in agricultural production are constrained by high labor and time consumption, alternative methods to respond to these constraints are required. In this study, to establish a new method for application to field experiments, we proposed the evaluation of the leaf area index (LAI) of all individual plants in an experimental sweetcorn field using an unmanned aerial vehicle (UAV). Small-scale field experiments were conducted over two years. In the first year, the nitrogen fertilizer level was changed, and the plant density and additional nitrogen fertilizer application time were changed in the next year. Three vegetation indices (VIs), namely, the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), and simple ratio (SR), were validated to quantify the LAI estimation using a UAV for individual plants. For the evaluation of the individual plants, we used a plant-based method, which created all of the plant buffers based on the points of existing plants and the plant distance. To confirm the impact of the method, we additionally demonstrated the relationship between the LAI and yield, the results of statical analyses, and the difference of the center and the border of the field. Among the three VIs, index SR was found the most promising in the estimation of the LAI of the individual sweetcorn plants, providing the strongest correlation of yield with SR. Because a lot of data were obtained using the plant-based method, the statical differences in the LAI and yield were more easily detected for the plant density and fertilizer treatments. Furthermore, interesting differences between the center and the border of the field were found. These results indicate the availability and impact of plant-based evaluations using UAVs in near future field experiments.

https://www.mdpi.com/2077-0472/13/3/561/pdf?version=1677577740

Rongling Ye

Papers

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

Yearly change in severely salt-damaged areas in paddy fields in Ban Phai in Northeast Thailand

Rice Production in Farmer Fields in Soil Salinity Classified Areas in Khon Kaen, Northeast Thailand

Educational Trials to Quantify Agronomic Information in Interdisciplinary Fieldwork in Pursat Province, Cambodia

Utilization of UAV Remote Sensing in Plant-Based Field Experiments: A Case Study of the Evaluation of LAI in a Small-Scale Sweetcorn Experiment