paddy rice
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2022 ◽  
Vol 118 ◽  
pp. 101-111
Author(s):  
Zhe Liu ◽  
Qi-qi Wang ◽  
Si-yu Huang ◽  
Ling-xuan Kong ◽  
Zhong Zhuang ◽  
...  

Author(s):  
Jie Wang ◽  
Danyang Wang ◽  
Tingyao Zhan ◽  
Shuo Qiu ◽  
Dongbing Tao ◽  
...  

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hadi Shams Esfandabadi ◽  
Mohsen Ghamary Asl ◽  
Zahra Shams Esfandabadi ◽  
Sneha Gautam ◽  
Meisam Ranjbari

PurposeThis research aims to monitor vegetation indices to assess drought in paddy rice fields in Mazandaran, Iran, and propose the best index to predict rice yield.Design/methodology/approachA three-step methodology is applied. First, the paddy rice fields are mapped by using three satellite-based datasets, namely SRTM DEM, Landsat8 TOA and MYD11A2. Second, the maps of indices are extracted using MODIS. And finally, the trend of indices over rice-growing seasons is extracted and compared with the rice yield data.FindingsRice paddies maps and vegetation indices maps are provided. Vegetation Health Index (VHI) combining average Temperature Condition Index (TCI) and minimum Vegetation Condition Index (VCI), and also VHI combining TCImin and VCImin are found to be the most proper indices to predict rice yield.Practical implicationsThe results serve as a guideline for policy-makers and practitioners in the agro-food industry to (1) support sustainable agriculture and food safety in terms of rice production; (2) help balance the supply and demand sides of the rice market and move towards SDG2; (3) use yield prediction in the rice supply chain management, pricing and trade flows management; and (4) assess drought risk in index-based insurances.Originality/valueThis study, as one of the first research assessing and mapping vegetation indices for rice paddies in northern Iran, particularly contributes to (1) extracting the map of paddy rice fields in Mazandaran Province by using satellite-based data on cloud-computing technology in the Google Earth Engine platform; (2) providing the map of VCI and TCI for the period 2010–2019 based on MODIS data and (3) specifying the best index to describe rice yield through proposing different calculation methods for VHI.


2022 ◽  
Vol 11 (1) ◽  
pp. 11-20 ◽  
Author(s):  
Montri Singhavara ◽  
Kamoltip Panyasit ◽  
Sakkarin Nonthapot

This research aimed to study the approach of the community toward the decision to grow rice and economic crops, including appropriate resource allocation for use on a farm under a large plot agricultural system. The study areas were in Phan district, Chiang Rai province, Thailand, and the data were collected from a sampling of 400 field agriculturalists. The method used was to develop a mathematical model for growing crops with multi-objectives and in multi-periods, together with an agriculturist representative and experts in multiple-criteria decision-making (MCDM). This was to prioritize the importance of alternative crops and find the appropriate allocation of the resources to achieve the targeted goal. The results showed that agriculturists prioritized most toward the criteria for growing Japanese rice with a weight of 0.179 Kg., followed by transplanted rice, transplanted glutinous rice, garlic, sown paddy rice, and sown glutinous paddy rice, respectively. The study’s results also showed that the price fluctuation of the crop products resulted in more use of land and labor in order to increase the production to compensate for the low price, and this also resulted in the higher opportunity cost of growing transplanted rice. Therefore, growing transplanted rice during in season planting was considered the most effective way, while during the off season, either garlic or Japanese rice could be grown. A collective pattern for planning for using resources together in large plot agricultural areas, together with a clear marketing target would bring about effective use of the resources and reduce the risk in revenue from the fluctuation in prices and uncertainty of yields from drought. Moreover, technology development to solve the problem of the lack of labor would be deemed an important approach toward the enhancement of the competitiveness of agriculturists in the future as well.


2021 ◽  
Vol 13 (12) ◽  
pp. 5969-5986
Author(s):  
Jichong Han ◽  
Zhao Zhang ◽  
Yuchuan Luo ◽  
Juan Cao ◽  
Liangliang Zhang ◽  
...  

Abstract. An accurate paddy rice map is crucial for ensuring food security, particularly for Southeast and Northeast Asia. MODIS satellite data are useful for mapping paddy rice at continental scales but have a mixed-pixel problem caused by the coarse spatial resolution. To reduce the mixed pixels, we designed a rule-based method for mapping paddy rice by integrating time series Sentinel-1 and MODIS data. We demonstrated the method by generating annual paddy rice maps for Southeast and Northeast Asia in 2017–2019 (NESEA-Rice10). We compared the resultant paddy rice maps with available agricultural statistics at subnational levels and existing rice maps for some countries. The results demonstrated that the linear coefficient of determination (R2) between our paddy rice maps and agricultural statistics ranged from 0.80 to 0.97. The paddy rice planting areas in 2017 were spatially consistent with the existing maps in Vietnam (R2=0.93) and Northeast China (R2=0.99). The spatial distribution of the 2017–2019 composite paddy rice map was consistent with that of the rice map from the International Rice Research Institute. The paddy rice planting area may have been underestimated in the region in which the flooding signal was not strong. The dataset is useful for water resource management, rice growth, and yield monitoring. The full product is publicly available at https://doi.org/10.5281/zenodo.5645344 (Han et al., 2021a). Small examples can be found from the following DOI: https://doi.org/10.17632/cnc3tkbwcm.1 (Han et al., 2021b).


Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2544
Author(s):  
Jinsil Choi ◽  
Jonghan Ko ◽  
Kyu-Nam An ◽  
Saeed A. Qaisrani ◽  
Jong-Oh Ban ◽  
...  

This study sought to simulate regional variation in staple crop yields in Chonnam Province, Republic of Korea (ROK), in future environments under climate change based on the calibration of crop models in the Decision Support System for Agricultural Technology Transfer 4.6 package. We reproduced multiple-year yield data for paddy rice (2013–2018), barley (2000–2018), and soybean (2004–2018) grown in experimental fields at Naju, Chonnam Province, using the CERES-Rice, CERES-Barley, and CROPGRO-Soybean models. A geospatial crop simulation modeling (GCSM) system developed using the crop models was then applied to simulate the regional impacts of climate change on the staple crops according to the Representative Concentration Pathway 4.5 and 8.5 scenarios. Simulated crop yields agreed with the corresponding measured crop yields, with root means square deviations of 0.31 ton ha−1 for paddy rice, 0.29 ton ha−1 for barley, and 0.27 ton ha−1 for soybean. We also demonstrated that the GCSM system could effectively simulate spatiotemporal variations in the impact of climate change on staple crop yield. The CERES and CROPGRO models seem to reproduce the effects of climate change on region-wide staple crop production in a monsoonal climate system. Added advancements of the GCSM system could facilitate interpretations of future food resource insecurity and establish a sustainable adaption strategy.


Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8463
Author(s):  
Jonghan Ko ◽  
Jaeil Cho ◽  
Jinsil Choi ◽  
Chang-Yong Yoon ◽  
Kyu-Nam An ◽  
...  

Agro-photovoltaic systems are of interest to the agricultural industry because they can produce both electricity and crops in the same farm field. In this study, we aimed to simulate staple crop yields under agro-photovoltaic panels (AVP) based on the calibration of crop models in the decision support system for agricultural technology (DSSAT) 4.6 package. We reproduced yield data of paddy rice, barley, and soybean grown in AVP experimental fields in Bosung and Naju, Chonnam Province, South Korea, using CERES-Rice, CERES-Barley, and CROPGRO-Soybean models. A geospatial crop simulation modeling (GCSM) system, developed using the crop models, was then applied to simulate the regional variations in crop yield according to solar radiation reduction scenarios. Simulated crop yields agreed with the corresponding measured crop yields with root mean squared errors of 0.29-ton ha−1 for paddy rice, 0.46-ton ha−1 for barley, and 0.31-ton ha−1 for soybean, showing no significant differences according to paired sample t-tests. We also demonstrated that the GCSM system could effectively simulate spatiotemporal variations in crop yields due to the solar radiation reduction regimes. An additional advancement in the GCSM design could help prepare a sustainable adaption strategy and understand future food supply insecurity.


Proteomes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 48
Author(s):  
Tossaporn Incharoen ◽  
Sittiruk Roytrakul ◽  
Wirot Likittrakulwong

Germinated paddy rice (GPR) could be a good alternative feed source for poultry with stocking density and heat stress problems. A total of 72 Hy-line Brown laying hens raised under low (LSD, 0.12 m2/bird) and high stocking densities (HSD, 0.06 m2/bird) were investigated. Three dietary GPR levels (0, 74 and 148 g/kg) were used. It was found that average daily feed intake, hen-day egg production, and egg mass significantly decreased in the HSD group. The levels of serum glucose (GLU), phosphorous (P), corticosterone (CORT), total Ig, lysozyme (LZY), and superoxide dismutase activities (SOD) in the HSD group were higher than those in the LSD group. Dietary GPR significantly affected GLU, P, alternative complement haemolytic 50 (ACH50), total Ig, and LZY. Moreover, CORT level significantly decreased in 74 and 148 g/kg dietary GPR groups, whereas SOD significantly increased only in the 148 g/kg dietary GPR group. Serum samples were analyzed using liquid chromatography-tandem mass spectrometry, and 8607 proteins were identified. Proteome analysis revealed 19 proteins which were enriched in different stocking densities and dietary GPR levels. Quantitative real-time reverse transcription-PCR technique was successfully used to verify the differentiated abundant protein profile changes. The proteins identified in this study could serve as appropriate biomarkers.


2021 ◽  
Author(s):  
◽  
Longlao Nyianu

<p>Laos is a small and developing nation in Southeast Asia that is vulnerable to climate change. Some of the more severe effects of climate change in Laos are droughts, flooding and insect pests which are impacting rice production. Many paddy rice plantations throughout the country are facing large shortages of rice production for commercial sale and subsistence use. This thesis explores how paddy rice farmers may adapt to climate change effects by focusing on a village in Luang Prabang province, Laos. Drawing on the climate adaptation framework, Climate – Smart Agriculture (CSA) and qualitative interviews with farmers in Thongphiengvilay village, I explore how CSA may help farmers adapt to climate change.   The results of this study show that CSA could help Thongphiengvilay farmers cope with increased drought and pests. I also argue that CSA could build on or complement existing Traditional Ecological Knowledge (TEK) already used by farmers. Furthermore, my results indicate that CSA could help farmers who currently use synthetic approaches to tackle their decreasing rice price production. For example, synthetic fertilisers that are currently being used by farmers could be replaced with organic CSA approaches and produce similar yields and also ensure the environmentally sustainability of farmers’ lands for future seasons. Therefore, this thesis recommends a CSA approach for adapting to climate change in Thongphiengvilay village by implementing Climate – Smart Villages (CSVs).   Key words: climate change adaptation, CSA, TEK, Laos</p>


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