Sanyam, R. K. Pal, P. Kingra, Anureet Kaur, S. K. Mishra, Tirath Singh, Abhishek Dhir
In order to assess the potential of the medium-range weather forecast in predicting the cotton productivity using crop simulation model, the CROPGRO-cotton model was calibrated and validated with the experimental data which was collected during kharif 2021 in an experiment that was carried out with two Bt cotton hybrid (RCH 776 and RCH 773) and one non-Bt (F2228), and sown at five dates i.e., April 25th, May 05th, May 15th, May 25th and June 04th in split-plot design with three replications at Punjab Agricultural University (PAU) Regional Research Station, Bathinda. The validated model was further used to assess the cotton productivity under different sowing dates using medium range weather forecast data on rainfall, maximum temperature and minimum temperature obtained for the period 2013-2021. The results showed that simulated values with medium range weather forecast were in close agreement with the simulated values for phenology and yield of cotton. The simulated cotton yield using daily medium range weather forecast data showed more or less significant efficiency to capture year-to-year as well as datewise variability in simulated cotton yield.
{"title":"Predicting the seed cotton yield with value added medium range weather forecast data using CROPGRO-Cotton model at Bhathinda, Punjab","authors":"Sanyam, R. K. Pal, P. Kingra, Anureet Kaur, S. K. Mishra, Tirath Singh, Abhishek Dhir","doi":"10.54386/jam.v26i1.2244","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2244","url":null,"abstract":"In order to assess the potential of the medium-range weather forecast in predicting the cotton productivity using crop simulation model, the CROPGRO-cotton model was calibrated and validated with the experimental data which was collected during kharif 2021 in an experiment that was carried out with two Bt cotton hybrid (RCH 776 and RCH 773) and one non-Bt (F2228), and sown at five dates i.e., April 25th, May 05th, May 15th, May 25th and June 04th in split-plot design with three replications at Punjab Agricultural University (PAU) Regional Research Station, Bathinda. The validated model was further used to assess the cotton productivity under different sowing dates using medium range weather forecast data on rainfall, maximum temperature and minimum temperature obtained for the period 2013-2021. The results showed that simulated values with medium range weather forecast were in close agreement with the simulated values for phenology and yield of cotton. The simulated cotton yield using daily medium range weather forecast data showed more or less significant efficiency to capture year-to-year as well as datewise variability in simulated cotton yield.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"42 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With continuous increase of population and demand for nutritional food, analyzing potential yield of world maize affected by global warming is of great significance to direct the crop production in the future. Thus, in this paper both average and top (national) yields of world maize between 2021 and 2030 are projected creatively using ARIMA-TR (Auto-regressive Integrated Moving Average and Trend Regression) model based on historic yields since 1961. The impact of global warming on the yields of world maize from 1961 to 2020 was analyzed using unary regression model. Our study concludes that between 2021 and 2030, average yield of world maize is projected to be from 5989 kg ha-1 to 6703 kg ha-1 while the top yield from 36530 kg ha-1 to 44271 kg ha-1, or the average ranging from 16.39% decreasingly to 15.14% of the top; from 1961 to 2020 global warming exerts positive effect on average yield of world maize less than on the top, which partly drives the gap between these two yields widened gradually; for world maize by 2030, the opportunities for improving global production should be mainly dependent on the advantage of high-yield countries.
{"title":"Potential yield of world maize under global warming based on ARIMA-TR model","authors":"Chengzhi Cai, Tingting Deng, Wenfang Cao","doi":"10.54386/jam.v26i1.2483","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2483","url":null,"abstract":"With continuous increase of population and demand for nutritional food, analyzing potential yield of world maize affected by global warming is of great significance to direct the crop production in the future. Thus, in this paper both average and top (national) yields of world maize between 2021 and 2030 are projected creatively using ARIMA-TR (Auto-regressive Integrated Moving Average and Trend Regression) model based on historic yields since 1961. The impact of global warming on the yields of world maize from 1961 to 2020 was analyzed using unary regression model. Our study concludes that between 2021 and 2030, average yield of world maize is projected to be from 5989 kg ha-1 to 6703 kg ha-1 while the top yield from 36530 kg ha-1 to 44271 kg ha-1, or the average ranging from 16.39% decreasingly to 15.14% of the top; from 1961 to 2020 global warming exerts positive effect on average yield of world maize less than on the top, which partly drives the gap between these two yields widened gradually; for world maize by 2030, the opportunities for improving global production should be mainly dependent on the advantage of high-yield countries.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"12 18","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140083691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Malav, Brijesh Yadav, Sunil B. H., Gopal Tiwari, A. Jangir, M. Nogiya, R. L. Meena, P. C. Moharana, R. P. Sharma, B. L. Mina
Land surface temperature (LST) and its interaction with normalized difference vegetation index (NDVI) is crucial for better understanding of environmental changes in current scenario. However, very few or scanty research on the interrelationship between LST, NDVI and topographic elements has been done in India. Therefore, the purpose of conducting this study was to examine, how LST and NDVI change as a function of elevation in Rajasthan. In present study, MODIS derived NDVI and LST and digital elevation model (DEM) from shuttle radar topography mission (SRTM) have been used. Results revealed that the LST and NDVI both were significantly influenced by elevation. Elevation, NDVI and LST varied from -6 to 1698 m, -0.09 to 0.65 and 24 to 45°C throughout the study region. In contrast to LST, which has a decreasing gradient from western to eastern portions, the spatial variability of NDVI has decreasing gradients from southern and eastern to western regions. The highest mean LST value (39.76 ± 0.2.9 0C) was obtained at an elevation range of -6 to 168 m, whereas NDVI value (0.38 ± 0.06) at elevation ranges of 589 – 1698 m. The analysis of the correlations between LST, NDVI and elevation indicated that the elevation has strong positive correlation with NDVI (r2 = 0.26) and negative correlation with LST (r2 = 0.28). Findings from this kind of research can be utilized as a platform for environmental and land use planning for sustainable ecosystem management.
{"title":"Assessing the influence of elevation on satellite derived normalized difference vegetation index and land surface temperature in Rajasthan","authors":"L. Malav, Brijesh Yadav, Sunil B. H., Gopal Tiwari, A. Jangir, M. Nogiya, R. L. Meena, P. C. Moharana, R. P. Sharma, B. L. Mina","doi":"10.54386/jam.v26i1.2370","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2370","url":null,"abstract":"Land surface temperature (LST) and its interaction with normalized difference vegetation index (NDVI) is crucial for better understanding of environmental changes in current scenario. However, very few or scanty research on the interrelationship between LST, NDVI and topographic elements has been done in India. Therefore, the purpose of conducting this study was to examine, how LST and NDVI change as a function of elevation in Rajasthan. In present study, MODIS derived NDVI and LST and digital elevation model (DEM) from shuttle radar topography mission (SRTM) have been used. Results revealed that the LST and NDVI both were significantly influenced by elevation. Elevation, NDVI and LST varied from -6 to 1698 m, -0.09 to 0.65 and 24 to 45°C throughout the study region. In contrast to LST, which has a decreasing gradient from western to eastern portions, the spatial variability of NDVI has decreasing gradients from southern and eastern to western regions. The highest mean LST value (39.76 ± 0.2.9 0C) was obtained at an elevation range of -6 to 168 m, whereas NDVI value (0.38 ± 0.06) at elevation ranges of 589 – 1698 m. The analysis of the correlations between LST, NDVI and elevation indicated that the elevation has strong positive correlation with NDVI (r2 = 0.26) and negative correlation with LST (r2 = 0.28). Findings from this kind of research can be utilized as a platform for environmental and land use planning for sustainable ecosystem management.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"17 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140083326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dher I. Bakr, Jasim Al-Khalidi, Bashar Talib Hamid
Climate changes have a direct or indirect impact on many vital systems, including human and animal, as well as vegetation. The monthly precipitation and temperature for the period (1981-2021) and vegetation images (NDVI) for the period (2000-2022) from the satellite (NASA) for the regions of Ninevah and Wasit of Iraq were used to find out their variations over the space and time. It was found that the temperature was increasing with time, but the precipitation was in a state of turbulent increase in the two study areas. The distribution of vegetation was also in a state of change with time as well as within a region. The vegetation area increased with increase in precipitation which was greater in the Ninevah region than in the Wasit region. When there was a lack of precipitation, the vegetation cover area decreased in the two study areas. The increase in temperature also resulted in a decrease in the density and area of vegetation. It was found that the change in the amount of precipitation was more influential than the change in temperature on the vegetative distribution.
{"title":"Climate changes impact on the distribution of vegetation in Wasit and Nineveh regions of Iraq","authors":"Dher I. Bakr, Jasim Al-Khalidi, Bashar Talib Hamid","doi":"10.54386/jam.v26i1.2417","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2417","url":null,"abstract":"Climate changes have a direct or indirect impact on many vital systems, including human and animal, as well as vegetation. The monthly precipitation and temperature for the period (1981-2021) and vegetation images (NDVI) for the period (2000-2022) from the satellite (NASA) for the regions of Ninevah and Wasit of Iraq were used to find out their variations over the space and time. It was found that the temperature was increasing with time, but the precipitation was in a state of turbulent increase in the two study areas. The distribution of vegetation was also in a state of change with time as well as within a region. The vegetation area increased with increase in precipitation which was greater in the Ninevah region than in the Wasit region. When there was a lack of precipitation, the vegetation cover area decreased in the two study areas. The increase in temperature also resulted in a decrease in the density and area of vegetation. It was found that the change in the amount of precipitation was more influential than the change in temperature on the vegetative distribution.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"24 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140083789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Trend analysis of agricultural drought and crop yield in Eastern Thrace provinces of Turkey","authors":"Çayan Alkan","doi":"10.54386/jam.v26i1.2381","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2381","url":null,"abstract":"","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"9 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140083822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A laboratory experiment was conducted to study the biology of pink bollworm Pectinophora gossypiella (Saunders) on cotton at four different temperature levels. It was found that males have a considerably shorter total life cycle duration on cotton at 35±1°C (29.5 days) followed by 30±1°C (37.2 days), 25±1°C (46.9 days) and highest at 20±1°C (50.8 days). Similarly, minimum total life cycle duration of female P. gossypiella was recorded at 35±1°C (30.2 days) followed by 30±1°C (38.0 days), 25±1°C (47.3 days) and maximum at 20±1°C (51.7 days). The highest fecundity was observed at 30±1°C (106.2) followed by 25±1°C (100.1), 35±1°C (60.1) and lowest at 20±1°C (55.2). Male as to female sex ratio was highest at 35±1°C (1:1.5) followed by 25±1°C (1:1.4), 30±1°C (1:1.3) and lowest at 20±1°C (1:1.2). These findings revealed that variation in temperature significantly influences the life cycle duration, fecundity and sex ratio of pink bollworms on cotton, with higher temperatures accelerating development and enhancing reproductive success.
{"title":"Biology of pink bollworm Pectinophora gossypiella (Saunders) on cotton as influenced by temperature","authors":"K. Deshmukh, V. K. Bhamare","doi":"10.54386/jam.v26i1.2367","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2367","url":null,"abstract":"A laboratory experiment was conducted to study the biology of pink bollworm Pectinophora gossypiella (Saunders) on cotton at four different temperature levels. It was found that males have a considerably shorter total life cycle duration on cotton at 35±1°C (29.5 days) followed by 30±1°C (37.2 days), 25±1°C (46.9 days) and highest at 20±1°C (50.8 days). Similarly, minimum total life cycle duration of female P. gossypiella was recorded at 35±1°C (30.2 days) followed by 30±1°C (38.0 days), 25±1°C (47.3 days) and maximum at 20±1°C (51.7 days). The highest fecundity was observed at 30±1°C (106.2) followed by 25±1°C (100.1), 35±1°C (60.1) and lowest at 20±1°C (55.2). Male as to female sex ratio was highest at 35±1°C (1:1.5) followed by 25±1°C (1:1.4), 30±1°C (1:1.3) and lowest at 20±1°C (1:1.2). These findings revealed that variation in temperature significantly influences the life cycle duration, fecundity and sex ratio of pink bollworms on cotton, with higher temperatures accelerating development and enhancing reproductive success.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"57 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140085850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AMIT BIJLWAN, SHWETA POKHRIYAL, Rajeev Ranjan, R.K. SINGH, ANKITA JHA
Precise estimation of evapotranspiration is crucial for optimizing crop water uses particularly in the context of agriculture and horticultural production. In this study, various machine learning techniques was used to determine reference evapotranspiration by leveraging historical weather data. The models tested include artificial neural networks (ANN), Lasso, Ridge, Random Forest, LGBM regressor, and Gradient boosting regressor. LGBM regressor emerged as the top-performing model, exhibiting exceptional accuracy with a testing R-squared of 1.0. ANN also demonstrated notable performance, achieving a testing R-squared of 0.99. Moreover, the Random Forest and Gradient boosting regressor models showcased strong predictive capabilities, with R2 values of 0.99 and 0.98, respectively. These models offer valuable alternatives for estimating evapotranspiration, providing robustness and adaptability to diverse environmental datasets.
精确估算蒸散量对于优化作物用水至关重要，尤其是在农业和园艺生产中。本研究利用各种机器学习技术，通过历史气象数据确定参考蒸散量。测试的模型包括人工神经网络（ANN）、Lasso、Ridge、随机森林、LGBM 回归器和梯度提升回归器。LGBM 回归器成为表现最佳的模型，其测试 R 方为 1.0，表现出卓越的准确性。ANN 也表现突出，测试 R 方为 0.99。此外，随机森林和梯度提升回归模型也展示了强大的预测能力，R2 值分别为 0.99 和 0.98。这些模型为估算蒸散量提供了有价值的替代方法，具有稳健性和对不同环境数据集的适应性。
{"title":"Machine learning methods for estimating reference evapotranspiration","authors":"AMIT BIJLWAN, SHWETA POKHRIYAL, Rajeev Ranjan, R.K. SINGH, ANKITA JHA","doi":"10.54386/jam.v26i1.2462","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2462","url":null,"abstract":"Precise estimation of evapotranspiration is crucial for optimizing crop water uses particularly in the context of agriculture and horticultural production. In this study, various machine learning techniques was used to determine reference evapotranspiration by leveraging historical weather data. The models tested include artificial neural networks (ANN), Lasso, Ridge, Random Forest, LGBM regressor, and Gradient boosting regressor. LGBM regressor emerged as the top-performing model, exhibiting exceptional accuracy with a testing R-squared of 1.0. ANN also demonstrated notable performance, achieving a testing R-squared of 0.99. Moreover, the Random Forest and Gradient boosting regressor models showcased strong predictive capabilities, with R2 values of 0.99 and 0.98, respectively. These models offer valuable alternatives for estimating evapotranspiration, providing robustness and adaptability to diverse environmental datasets.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":" 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140091556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study was carried out to understand the pattern of surface energy fluxes over a periodical scale and energy balance closure using Large Aperture Scintillometer and Micrometeorological tower. The standalone technique as ‘Scintillometry’ which observes the structure parameter of refractive index based on Monin-Obukhov Similarity theory, has the potential to measure the sensible heat flux precisely. This paper discusses the surface energy balance components and energy balance closure over a period of August 2017 to June 2018. The maximum mean energy fluxes Rn, G, H and LE were observed in September (98.6 Wm-2), May (13.9 Wm-2), June (53.3 Wm-2) and August (82.1 Wm-2), respectively. The overall mean ET was observed at the rate of 1.36 mm day-1 during the study period. This scintillometry technique may further use in evapotranspiration modelling from polar orbiting satellite to geostationary satellite over a heterogeneous and undulated landscape.
{"title":"Surface energy fluxes and energy balance closure using large aperture scintillometer-based ET station on heterogeneous agricultural landscape in north India","authors":"A. Danodia, N. Patel, V. K. Sehgal, R. Singh","doi":"10.54386/jam.v26i1.2447","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2447","url":null,"abstract":"This study was carried out to understand the pattern of surface energy fluxes over a periodical scale and energy balance closure using Large Aperture Scintillometer and Micrometeorological tower. The standalone technique as ‘Scintillometry’ which observes the structure parameter of refractive index based on Monin-Obukhov Similarity theory, has the potential to measure the sensible heat flux precisely. This paper discusses the surface energy balance components and energy balance closure over a period of August 2017 to June 2018. The maximum mean energy fluxes Rn, G, H and LE were observed in September (98.6 Wm-2), May (13.9 Wm-2), June (53.3 Wm-2) and August (82.1 Wm-2), respectively. The overall mean ET was observed at the rate of 1.36 mm day-1 during the study period. This scintillometry technique may further use in evapotranspiration modelling from polar orbiting satellite to geostationary satellite over a heterogeneous and undulated landscape.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"61 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. A. Jolly, G. T. Uddin, M. S. ALIM, R. KUMAR, A. DUTTA, M.M.K. REYA, N. TASNIM
At present greenhouse farming has become more popular in contrast to traditional farming because of its adjustment capability of the environmental parameters such as temperature, humidity, light intensity, and soil moisture according to the requirements of the crops. Continuous monitoring and controlling facilities of the greenhouse system allow the farmers a good maintenance system with good quality and high yield of the crops. In this paper, an Arduino microcontroller was used in a greenhouse system for an automatic monitoring system for cultivation incorporating various sensors such as a temperature-humidity sensor, and soil moisture sensor to collect parameters for monitoring the environment of the greenhouse. The collected data were used to control the temperature using cooling fans which facilitated the greenhouse controlling the environment. For storage and processing the data the controller code was generated in the Arduino programming language, and finally inserted into the Arduino UNO R3 microcontroller. A solar power system with a rechargeable battery was installed as a source of energy to ensure continuous power supply to the greenhouse system. Implementation of a greenhouse with a microclimatic parameter monitoring and controlling system will result in mitigating land and labor requirement problems for small-scale farmers, and gardeners as well as supplying suitable data for agricultural researchers.
与传统农业相比，温室种植因其能够根据作物的需求调节温度、湿度、光照强度和土壤湿度等环境参数而变得更加流行。温室系统的连续监测和控制设施使农民能够很好地维护系统，保证作物的优质高产。本文在温室系统中使用了 Arduino 微控制器，该系统结合了各种传感器（如温湿度传感器和土壤水分传感器），用于采集温室环境监测参数，从而实现种植自动监控系统。收集到的数据用于利用冷却风扇控制温度，从而促进温室对环境的控制。为了存储和处理数据，用 Arduino 编程语言生成了控制器代码，最后将其插入 Arduino UNO R3 微控制器。为确保温室系统的持续供电，安装了带充电电池的太阳能发电系统作为能源来源。实施带有微气候参数监测和控制系统的温室，将减轻小规模农户和园艺师的土地和劳动力需求问题，并为农业研究人员提供合适的数据。
{"title":"Analyzing the efficiency of Arduino UNO microcontroller in monitoring and controlling the microclimatic parameters of greenhouse","authors":"F. A. Jolly, G. T. Uddin, M. S. ALIM, R. KUMAR, A. DUTTA, M.M.K. REYA, N. TASNIM","doi":"10.54386/jam.v26i1.2520","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2520","url":null,"abstract":"At present greenhouse farming has become more popular in contrast to traditional farming because of its adjustment capability of the environmental parameters such as temperature, humidity, light intensity, and soil moisture according to the requirements of the crops. Continuous monitoring and controlling facilities of the greenhouse system allow the farmers a good maintenance system with good quality and high yield of the crops. In this paper, an Arduino microcontroller was used in a greenhouse system for an automatic monitoring system for cultivation incorporating various sensors such as a temperature-humidity sensor, and soil moisture sensor to collect parameters for monitoring the environment of the greenhouse. The collected data were used to control the temperature using cooling fans which facilitated the greenhouse controlling the environment. For storage and processing the data the controller code was generated in the Arduino programming language, and finally inserted into the Arduino UNO R3 microcontroller. A solar power system with a rechargeable battery was installed as a source of energy to ensure continuous power supply to the greenhouse system. Implementation of a greenhouse with a microclimatic parameter monitoring and controlling system will result in mitigating land and labor requirement problems for small-scale farmers, and gardeners as well as supplying suitable data for agricultural researchers.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"118 33","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140090620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
P. ANBAZHAGAN, M. THERADIMANI, V. RAMAMOORTHY, S. VELLAIKUMAR, S. JULIET HEBZIBA, R. OVIYA
False smut of rice is an upcoming menace to rice production in India. In order to understand the intricate relationship between disease incidence and weather parameters, field experiments were conducted for three years (2019, 2020 and 2021) in two cropping seasons viz., late kharif (August to November) and rabi (October to January) at the Agricultural College and Research Institute (AC & RI), Madurai, Tamil Nadu. Results revealed that the disease severity had positive correlation with relative humidity (RH), wind speed (WS) and bright sunshine hours (BSS) and negative correlation with heavy rainfall (RF), evaporation (EP) and temperature. The pooled data analysis (2019 and 2020) for the late kharif and rabi cropping season revealed that disease severity was perfectly showed positive correlation with relative humidity (0.80) and wind speed (0.83) and negatively correlated with weekly maximum temperature (-0.78) and minimum temperature (-0.84). The step wise linear regression analysis was performed which revealed that among the six weather factors minimum temperature influenced the false smut disease severity up to 92%.
{"title":"Influence of weather parameters on rice false smut disease progression in Tamil Nadu, India","authors":"P. ANBAZHAGAN, M. THERADIMANI, V. RAMAMOORTHY, S. VELLAIKUMAR, S. JULIET HEBZIBA, R. OVIYA","doi":"10.54386/jam.v26i1.2334","DOIUrl":"https://doi.org/10.54386/jam.v26i1.2334","url":null,"abstract":"False smut of rice is an upcoming menace to rice production in India. In order to understand the intricate relationship between disease incidence and weather parameters, field experiments were conducted for three years (2019, 2020 and 2021) in two cropping seasons viz., late kharif (August to November) and rabi (October to January) at the Agricultural College and Research Institute (AC & RI), Madurai, Tamil Nadu. Results revealed that the disease severity had positive correlation with relative humidity (RH), wind speed (WS) and bright sunshine hours (BSS) and negative correlation with heavy rainfall (RF), evaporation (EP) and temperature. The pooled data analysis (2019 and 2020) for the late kharif and rabi cropping season revealed that disease severity was perfectly showed positive correlation with relative humidity (0.80) and wind speed (0.83) and negatively correlated with weekly maximum temperature (-0.78) and minimum temperature (-0.84). The step wise linear regression analysis was performed which revealed that among the six weather factors minimum temperature influenced the false smut disease severity up to 92%.","PeriodicalId":56127,"journal":{"name":"Journal of Agrometeorology","volume":"35 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140087483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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