Survey and surveillance on rice false smut disease severity in Tamil Nadu and the influence of wind velocity on disease progression under field conditions

The false smut disease of rice incited by Ustilaginoidea virens is a major constraint on rice production. The main aim of the present study was to ascertain the false smut disease severity in both delta and non-delta districts of Tamil Nadu through survey and surveillance and assess the correlation between the disease severity and wind velocity for the past three years, 2019, 2020 and 2021. Moreover, the present study addressed the disease distribution pattern of false smut diseases under field conditions. The results obtained from survey results revealed that the maximum disease severity was recorded in Nagapattinam district (Nagapattinam block) with 27.45% and the minimum disease severity was recorded in the district Theni (Bodinayakanur block) with 8% in 2021. Similarly in 2019 and 2020 maximum disease severity was recorded in the following districts Thanjavur district (Orathanadu block) with 19.91% and Thanjavur district (Peravurani block) with 18.54% and the minimum disease severity was recorded in the following districts Madurai district (Madurai north block) with 4.78% and Madurai district (Usilampatti block) with 4.78% respectively. The obtained R2 values through regression analysis were 0.70, 0.79 and 0.76 in the following years, 2019, 2020 and 2021, respectively. Besides the relationship between the false smut disease development, the pattern wind direction was also assessed. By assessing the false smut disease distribution pattern under field conditions, more disease distribution was observed around the surrounding area of the paddy field as well as the diagonal path of the field which clearly revealed that wind direction influences the disease development. [ FROM AUTHOR] Copyright of Journal of Applied & Natural Science is the property of Applied & Natural Science Foundation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Remote Sensing-Based Evaluation of Heat Stress Damage on Paddy Rice Using NDVI and PRI Measured at Leaf and Canopy Scales

Extremely high air temperature at the heading stage of paddy rice causes a yield reduction due to the increasing spikelet sterility. Quantifying the damage to crops caused by high temperatures can lead to more accurate estimates of crop yields. The remote sensing technique evaluates crop conditions indirectly but provides information related to crop physiology, growth, and yield. In this study, we aim to assess the crop damage caused by heat stress in paddy rice examined under elevated air temperatures in a temperature gradient field chamber from 2016 to 2019, using remote-sensed vegetation indices. A leaf-spectrometer, field-spectrometers, and a multi-spectral camera were used to monitor the conditions of paddy rice. Although, in the leaf- and canopy-scales, the values of normalized difference vegetation index (NDVI) and photochemical reflectance index (PRI) decreased after the heading of rice under normal conditions, the decreasing sensitivity of NDVI and PRI was different depending on the degree of physiological heat stress by high temperature conditions. The NDVI after the heading under extremely high air temperature was not dropped and remained the value before heading. The PRI decreased at all air temperature conditions after the heading;the PRI of the plot exposed to the elevated air temperature was higher than that under ambient air temperature. Further, the relative change in NDVI and PRI after the heading exhibited a strong relationship with the ripening ratio of paddy rice, which is the variable related to crop yield. These remote-sensing results aid in evaluating the crop damage caused by heat stress using vegetation indices. [ FROM AUTHOR] Copyright of Agronomy is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Radiation estimation and crop growth trajectory reconstruction by novel algorithms improve MOD16 evapotranspiration predictability for global multi-site paddy rice ecosystems

• MOD16 products indicated significant underestimations in all paddy rice ET observations. • R n estimation in overcast conditions and LAI reconstruction were two key causes. • Daily R n estimations under all-sky conditions by a global cloudy index algorithm were improved by 40.6%. • Daily LAI dynamics estimated by the LTDG_PhenoS algorithm were improved by 818.7%. • Daily ET estimations were improved by 68.7%. Reliable estimations in evapotranspiration (ET) of paddy rice ecosystems by satellite products are critical because of their important roles in regional hydrological processes and climate change. However, the NASA MODIS ET products (MOD16A2) and its derivatives do not have good correlations with all global paddy rice ET observations. In this research, MOD16 model sensitivity analyses and parameter optimization strategies were conducted in order to solve the problem. Results suggested that underestimation of daily net radiation (R n) in overcast conditions and less satisfactory reconstruction of field-scale leaf area index (LAI) growth trajectory from the start date of field flooding and transplanting (FFTD) to the end of growing seasons by MODIS coarse vegetation index were identified as two major causes. A Light and Temperature-Driven Growth model and a Phenology-based LAI temporal Smoothing method fusion algorithm (LTDG_PhenoS) and an improved R n estimation method were introducted and evaluated in paddy rice fields in South Korea, Japan, China, Philippines, India, Spain, Italy, and the USA from 2002 to 2019. The LTDG_PhenoS algorithm considers Landsat and MODIS EVI observations and meteorological data as input variables and 30-m LAI daily time series as outcomes. Introducing the global cloudy index algorithm resulted in improved estimations of daily R n under all-sky conditions, with a significant decrease of root mean square error (RMSE) from 1.87 to 1.11 MJ m−2 day−1. The LTDG_PhenoS algorithm well reconstructed crop LAI growth dynamics from the FFTD to the end of rice growing seasons, with a substantial decline of RMSE from 1.49 to 0.27 m2/m−2. The FFTD estimations by the LTDG_PhenoS algorithm had an R2 of 0.97 and a small RMSE of less than 12-days. Daily ET rates estimated by novel algorithms had a substantial decline in RMSE from 2.88 to 0.90 mm day−1. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Spatiotemporal dynamics of rice–crayfish field in Mid-China and its socioeconomic benefits on rural revitalisation

Agricultural sustainability has important value for boosting regional growth. In recent years, the unprecedented expansion of rice–crayfish field (RCF) in the rural areas of mid-China has raised great concerns in terms of its spatiotemporal dynamics and socioeconomic impact. With Jianli City in mid-China as a case, this study aimed to (1) comprehensively investigate the land-use change in RCF with combined remote sensing and geospatial data analysis, (2) delineate the variations of RCF and socioeconomic benefits from 2010 to 2019 and (3) explore the influencing factors and driving mechanism by using a multiscale geographically weighted regression model. Results illustrated that the RCF development in Jianli City showed an overall uptrend between 2000 and 2019. The area of RCF in 2019 expanded by 599.95% from 2015 levels (from 10,350 ha to 72,445 ha). These extensively expanded RCFs were mainly converted from paddy fields and are distributed around the water area. In terms of socioeconomic benefits, the economic income of villagers increased, whilst the number of out-migrant workers decreased. RCF development effectively contributed to regional economic growth and reduced rural depopulation, thereby facilitating rural transformation from traditional agricultural to characteristic agriculture. The findings clearly showed the spatiotemporal dynamics of RCF and its positive impact on the socioeconomic development of rural areas, thus providing evidence for formulating targeted rural revitalisation policies to achieve rural sustainability. • The spatiotemporal dynamics of rice–crayfish field (RCF) in mid-China is explored. • The positive relationship between RCF and socioeconomic is illustrated. • Multiscale geographically weighted regression uncovers the scale effect and spatial heterogeneity of influencing factors. • RCF injects vitality into sustainable agriculture and rural revitalisation. [ FROM AUTHOR];Copyright of Applied Geography is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Mapping Paddy Rice Distribution and Cropping Intensity in China from 2014 to 2019 with Landsat Images, Effective Flood Signals, and Google Earth Engine

Paddy rice cropping systems play a vital role in food security, water use, gas emission estimates, and grain yield prediction. Due to alterations in the labor structure and the high cost of paddy rice planting, the paddy rice cropping systems (single or double paddy rice) have drastically changed in China in recent years;many double-cropping paddy rice fields have been converted to single-cropping paddy rice or other crops, especially in southern China. Few maps detect single and double paddy rice and cropping intensity for paddy rice (CIPR) in China with a 30 m resolution. The Landsat-based and effective flooding signal-based phenology (EFSP) method, which distinguishes CIPR with the frequency of the effective flooding signal (EFe), was proposed and tested in China. The cloud/ice/shadow was excluded by bit arithmetic, generating a good observation map, and several non-paddy rice masks were established to improve the classification accuracy. Threshold values for single and double paddy rice were calculated through the mapped data and agricultural census data. Image processing (more than 684,000 scenes) and algorithm implementation were accomplished by a cloud computing approach with the Google Earth Engine (GEE) platform. The resultant maps of paddy rice from 2014 to 2019 were evaluated with data from statistical yearbooks and high-resolution images, with producer (user) accuracy and kappa coefficients ranging from 0.92 to 0.96 (0.76–0.87) and 0.67–0.80, respectively. Additionally, the determination coefficients for mapped and statistical data were higher than 0.88 from 2014 to 2019. Maps derived from EFSP illustrate that the single and double paddy rice systems are mainly concentrated in the Cfa (warm, fully humid, and hot summer, 49% vs. 56%) climate zone in China and show a slightly decreasing trend. The trend of double paddy rice is more pronounced than that of single paddy rice due to the high cost and shortages of rural household labor. However, single paddy rice fields expanded in Dwa (cold, dry winter, and hot summer, 11%) and Dwb (cold, dry winter, and warm summer, 9%) climate zones. The regional cropping intensity for paddy rice coincides with the paddy rice planting area but shows a significant decrease in south China, especially in Hunan Province, from 2014 to 2019. The results demonstrate that EFSP can effectively support the mapping of single and double paddy rice fields and CIPR in China, and the combinations of Landsat 7 and 8 provide enough good observations for EFSP to monitor paddy rice agriculture. [ FROM AUTHOR];Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)