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Journal articles on the topic 'Meteorology, Agricultural'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 July 2024

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1

Sivakumar, M. V. K. "Handbook of agricultural meteorology." Agricultural Systems 54, no. 1 (May 1997): 129–31. http://dx.doi.org/10.1016/s0308-521x(97)86670-x.

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2

Hugh-Jones, Martin. "Handbook of agricultural meteorology." Preventive Veterinary Medicine 24, no. 2 (August 1995): 141–43. http://dx.doi.org/10.1016/0167-5877(95)90008-x.

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3

Rijks, D. "WMO Agricultural Meteorology Programme." Agricultural and Forest Meteorology 59, no. 3-4 (July 1992): 319–24. http://dx.doi.org/10.1016/0168-1923(92)90100-i.

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4

TAKAGI, Kentaro, Seiji SHIMODA, Reiji KIMURA, Tomoko NAKANO, Manabu NEMOTO, and Weiguo CHENG. "Special Collection: Agricultural Meteorology." Journal of Agricultural Meteorology 80, no. 1 (January 10, 2024): 1. http://dx.doi.org/10.2480/agrmet.d-24-00101.

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5

Monteith, J. L. "Agricultural Meteorology: evolution and application." Agricultural and Forest Meteorology 103, no. 1-2 (June 2000): 5–9. http://dx.doi.org/10.1016/s0168-1923(00)00114-3.

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6

Stigter, Kees. "Basic Principles of Agricultural Meteorology." Agricultural and Forest Meteorology 123, no. 1-2 (May 2004): 119–20. http://dx.doi.org/10.1016/j.agrformet.2003.11.004.

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7

von Wilamowitz-Moellendorff, Tello. "19th-century preliminaries to agricultural meteorology." Meteorologische Zeitschrift 5, no. 3 (July 2, 1996): 124–28. http://dx.doi.org/10.1127/metz/5/1996/124.

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8

Monteith, J. L. "History of the commission for agricultural meteorology." Agricultural and Forest Meteorology 65, no. 1-2 (June 1993): 139–42. http://dx.doi.org/10.1016/0168-1923(93)90042-g.

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9

Narasimha, M. Raja, P. Venkata Subbiah, I. Venkata Reddy, P. N. Siva Prasad, and N. Raja Shekar. "Impact of Agrometerology Advisory Services (AAS) for Assessment of Cotton Cropping System in NTR District of Andhra Pradesh, India." International Journal of Environment and Climate Change 13, no. 7 (May 12, 2023): 495–502. http://dx.doi.org/10.9734/ijecc/2023/v13i71902.

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The survey was conducted from June 2022 to January 2023 to study the impact of Agro meteorology Advisory Services and to know the increases of production of cotton crops concerning the effect of weather parameters in selected villages under the DAMU project in NTR district, Andhra Pradesh. District Agromet Units (DAMU) which was established in Krishi Vigyan Kendra’s by Andhra Pradesh cooperative program of India Meteorology Department and Indian Council of Agricultural Research. The Main theme of DAMU is to provide timely location specific Agro-met advisories to the farmers at the sub divisional and district level. The agro meteorology advisory services were provided weekly twice among the sub divisions (Tuesday and Friday) and disseminated to farmers by including cotton growers using Whats App, emails and other print media. The impact assessment was based on feedback to come at significant illation in terms of using of Agro meteorology Advisory Service (AAS) by farmers. The assessment study revealed that the farmers who adopted agro advisory services on real-time basis obtained 18 % higher net return in cotton compared to Non-AAS farmers which were benefited by forecasting of rainfall for timely agricultural operations, the recommended dose of fertilizers, and efficient use of pesticides majors in a required support manner during different crop growth stages were advised in bi-weekly bulletins. AAS farmers benefited by timely application of insecticides and fertilizers, timely accurate weather forecasting and timely agricultural operations to gain more yield in cotton crop as compared to Non-AAS farmers. AAS might be used to be helpful to the farmers in managing and changing weather, finally decreased input cost in agriculture and acquiring cost-effective agricultural production by adopting of weather-based Agromet Advisory.
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10

OMOTO, Yukio, and Hidenori TAKAHASHI. "International Symposium on Agricultural Meteorology in Beijing, 1987." Journal of Agricultural Meteorology 43, no. 4 (1988): 325–29. http://dx.doi.org/10.2480/agrmet.43.325.

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11

XIN, Naiquan. "Development and Duties of Agricultural Meteorology in China." Journal of Agricultural Meteorology 45, no. 2 (1989): 117–19. http://dx.doi.org/10.2480/agrmet.45.117.

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12

Seguin, B., D. Courault, and M. Guérif. "Satellite thermal infrared data applications in agricultural meteorology." Advances in Space Research 13, no. 5 (May 1993): 207–17. http://dx.doi.org/10.1016/0273-1177(93)90547-o.

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13

Novak, V. G., and À. V. Novak. "AGRICULTURAL METEOROLOGY TERMS 2017–2018 AGRICULTURAL YEAR FROM DATA OF WEATHERSTATION UMAN." Bulletin of Uman National University of Horticulture, no. 2 (2018): 73–75. http://dx.doi.org/10.31395/2310-0478-2018-21-73-75.

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14

HATFIELD, J. "Future needs in agricultural meteorology: basic and applied research☆." Agricultural and Forest Meteorology 69, no. 1-2 (June 1994): 39–45. http://dx.doi.org/10.1016/0168-1923(94)90079-5.

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15

Lee, Xuhui. "Advanced Short Course on Agricultural, Forest and Micro Meteorology." Agricultural and Forest Meteorology 125, no. 3-4 (October 2004): 315–16. http://dx.doi.org/10.1016/j.agrformet.2004.04.001.

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16

Новак, В. Г. "AGRICULTURAL METEOROLOGY TERMS 2020–2021 AGRICULTURAL YEAR FROM DATA OF WEATHER-STATION UMAN." Bulletin of Uman National University of Horticulture 1 (August 2022): 23–26. http://dx.doi.org/10.31395/2310-0478-2022-1-23-26.

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The article presents data on air temperature and precipitation from October 2020 to September 2021, as well as analyzes them in comparison with the average long-term data (for 30 years - from 1991 to 2020). A characteristic feature of this agricultural year was a favorable temperature background and sufficient rainfall. The average air temperature in the agricultural year was 9.2° С, e it was only 0.4° С higher than the long-term average. At the same time, in the cold period (December – March) the total excess temperature was 1.4° С, and in the warm period (April – September) the total decrease was 1.9° С. The total amount of precipitation for the year was 655.7 mm, which is 69 mm higher than the long-term average.
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17

Novak, V. G., and À. V. Novak. "AGRICULTURAL METEOROLOGY TERMS 2018–2019 AGRICULTURAL YEAR FROM DATA OF WEATHER-STATION UMAN." Bulletin of Uman National University of Horticulture 1 (May 2020): 47–49. http://dx.doi.org/10.31395/2310-0478-2020-1-47-49.

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18

Новак, А. В. "AGRICULTURAL METEOROLOGY TERMS 2019–2020 AGRICULTURAL YEAR FROM DATA OF WEATHER-STATION UMAN." Bulletin of Uman National University of Horticulture 1 (September 2021): 27–29. http://dx.doi.org/10.31395/2310-0478-2021-1-27-29.

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19

Novak, A., and Y. W. Novak. "Agricultural meteorology terms 2020–2021 agricultural year from data of weather-station Uman." Collected Works of Uman National University of Horticulture 1, no. 103 (2023): 153–60. http://dx.doi.org/10.32782/2415-8240-2023-103-1-153-160.

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20

Udalov, A. A. "Usage of digital technologies in exploratory data analysis in agriculture." Buhuchet v sel'skom hozjajstve (Accounting in Agriculture), no. 11 (November 14, 2023): 692–701. http://dx.doi.org/10.33920/sel-11-2311-04.

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Exploratory Data Analysis (EDA) plays a pivotal role in agriculture, enabling the meaningful exploration of information and the identification of significant patterns, unusual phenomena, and interconnections among various aspects of agricultural activities. EDA contributes to uncovering new opportunities for process optimization, yield enhancement, cost reduction, and minimizing the adverse environmental impact in agriculture. Furthermore, it fosters the integration of diverse fields of knowledge, such as meteorology, agronomy, and ecology, for more effective management of agricultural resources. This article is dedicated to the examination of methods and the application of exploratory data analysis in the context of agriculture. We will delve into the fundamental stages of this analysis, key directions, and the advantages of implementing digital technologies to enhance production and sustainability in agriculture.
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21

HOLLINGER, S. "Future direction and needs in agricultural meteorology/climatology and modeling☆." Agricultural and Forest Meteorology 69, no. 1-2 (June 1994): 1–7. http://dx.doi.org/10.1016/0168-1923(94)90075-2.

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22

BLAD, B. "Future directions and needs for academic education in agricultural meteorology☆." Agricultural and Forest Meteorology 69, no. 1-2 (June 1994): 27–32. http://dx.doi.org/10.1016/0168-1923(94)90077-9.

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23

SUZUKI, Yoshinori, and Seiji HAYAKAWA. "A Report on the Conditions of Agricultural Meteorology in Taiwan." Journal of Agricultural Meteorology 45, no. 2 (1989): 111–15. http://dx.doi.org/10.2480/agrmet.45.111.

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24

OZAWA, Yukio. "A Report on the Study of Agricultural Meteorology in Taiwan." Journal of Agricultural Meteorology 47, no. 2 (1991): 117–21. http://dx.doi.org/10.2480/agrmet.47.117.

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25

Working Group of Meteorological Imp. "Symposiun on the Roles of Small Reservoir in Agricultural Meteorology." Journal of Agricultural Meteorology 52, no. 1 (1996): 47–50. http://dx.doi.org/10.2480/agrmet.52.47.

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26

HORIGUCHI, Ikuo, Hiroshi TANI, and Shunji MORIKAWA. "Applications of Satellite Data to the Studies of Agricultural Meteorology." Journal of Agricultural Meteorology 40, no. 4 (1985): 379–85. http://dx.doi.org/10.2480/agrmet.40.379.

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27

HORIGUCHI, Ikuo, Hiroshi TANI, and Toshihiro MOTOKI. "Applications of Satellite Data to the Studies of Agricultural Meteorology." Journal of Agricultural Meteorology 42, no. 2 (1986): 129–35. http://dx.doi.org/10.2480/agrmet.42.129.

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28

POIRIER, JEAN-PAUL. "The Names of the Months in Europe: Agricultural and Meteorological influences." European Review 15, no. 2 (April 4, 2007): 199–207. http://dx.doi.org/10.1017/s106279870700021x.

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The ancient Anglo-Saxon and Germanic month names related to agricultural activities and meteorology have left traces in names still used in Germany, Holland and Denmark in the 19th century. Nowadays, in a number of East European languages (Croatian, Czech, Ukrainian, Polish, Byelorussian, Lithuanian, Finnish) the names of the months still refer to seasonal agricultural labours or meteorological conditions.
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29

KUMAR, VANTIPALLI ARAVIND AND PRASANN. "Revolutionizing Agriculture with Satellite Technology for Farmers: A Review." BIOPESTICIDES INTERNATIONAL 19, no. 02 (December 2023): 97. http://dx.doi.org/10.59467/bi.2023.19.97.

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Farmers in India have benefited considerably from the combination of satellite-enabled services and data obtained on the ground. The India Meteorological Department, Ministry of Earth Sciences, provides weather forecasting, agro-advisory services, agromet services, soil moisture monitoring, and agricultural extension initiatives to encourage agricultural operations in India. Indian Space Research Organization?s (ISRO) also partners with the Ministry of Agriculture and Farmers Welfare on several satellite data and geographic information systems-based agricultural applications. ISRO, in collaboration with the Ministry of Agriculture and Farmers Welfare, has developed applications including horticultural crop inventory and site suitability for expansion in unutilized places; crop assessment using medium- and high-resolution satellite data; field information gathering with field photos using a smartphone application; and crop cutting experiments based on satellite-derived crop vigor information. ISRO has provided technology for FASAL and the National Agricultural Drought Assessment and Surveillance System to the Department of Agriculture Cooperation and Farmer Welfare. ISRO has also incorporated the Central Water Commission?s monitoring of irrigation systems. Overall, satellite-enabled services have transformed agricultural operations in India by providing farmers with precise and timely data that enable them to make educated decisions about their crops, resulting in increased crop yields and financial returns.. KEYWORDS :Agriculture, Farming, Satellite technology, Meteorology, Space organization
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30

Katkar, M. G., S. B. Kharbade, S. Y. Wankhede, A. A. Shaikh, and V. A. Sthool. "Effect of Micrometeorological Parameters on Growth and Yield of Brinjal (Solanum melongena L.) under Different Planting Windows." International Journal of Environment and Climate Change 13, no. 10 (August 12, 2023): 112–24. http://dx.doi.org/10.9734/ijecc/2023/v13i102621.

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An experiment was carried out at Faculty of Agriculture Department of Agricultural Meteorology Farm, Centre for Advanced Agricultural Meteorology, College of Agriculture, Pune during Kharif seasons of 2014 and 2015.The experiment was laid out in split plot design with three replications. The treatment comprised of three brinjal hybrids viz.,V1:Phule Arjun, V2: Krishna, V3: Panchganaga as main plot and four planting windows viz., P1: 31st MW (30 July-5 August), P2: 32ndMW (6-12August), P3:33rdMW (13-19 August) and P4: 34thMW (20-26 August) as sub plot treatments. In micrometeorological studies of the higher radiation absorptions and lower reflection was absorbed under hy. Phule Arjun as compared to hy. Krishana and hy Panchganga. The maximum Incident PAR (1270 u mol m-2s-1) Intercepted PAR (86.47u mol m-2s-1),Absorbed PAR (1094.4u mol m-2s-1) and Radiation use efficiency (2.43gmMJ m-2) was observed in hy .Phule Arjun. Cumulative GDD, HTU and PTU at the end of each growth stages showed that numerically higher requirement was observed in hy. Phule Arjun over hy. Krishana and hy. Panchganaga hybrids during both year 2014 and 2015 experimentation period. Whereas, the lowest canopy temperature was found in hy. Phule Arjun (29.0 0C) than rest of the brinjal hybrids. Canopy reflected PAR and transmitted PAR was higher in (191.54 and 188.62 µ mol m-2s-1) Panchganaga hybrids among the brinjal hybrids.
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31

Toro, Ivan Mauricio Cely, Ricardo Acosta Gotuzzo, Débora Regina Roberti, and Jackson Ernani Fiorin. "Avaliação de modelos de footprint para análise de fluxos obtidos por Eddy-Covariance em pequenas-áreas." Ciência e Natura 40 (March 22, 2018): 93. http://dx.doi.org/10.5902/2179460x30701.

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Two models for footprint calculations are compared employing flux measurements in the planetary boundary layer. The calculationsare based on the analytical models by Kormann e Meixner (2001) [An analytical footprint model for non-neutral stratification.Boundary-Layer Meteorology 99, 207–224] and by Schuepp et al. (1990) [Footprint prediction of scalar fluxes from analytical solutions of the difussion equation. Boundary-Layer Meteorology 50, 355-373]. The footprint density functions of a flux sensor are determined using eddy-covariance data. Those functions are integrated over surfaces given by quadrangular rectangles, in this case an agricultural field. This work ilustrates the features of each footprint model employing flux measurements with an eddy-covariance system of the SULFLUX network, installed on a agricultural field. Finally, it is presented the model that describes in a better way the flux measurements in small fields.
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32

Estévez, J., P. Gavilán, and A. P. García-Marín. "Data validation procedures in agricultural meteorology – a prerequisite for their use." Advances in Science and Research 6, no. 1 (May 20, 2011): 141–46. http://dx.doi.org/10.5194/asr-6-141-2011.

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Abstract. Quality meteorological data sources are critical to scientists, engineers, climate assessments and to make climate related decisions. Accurate quantification of reference evapotranspiration (ET0) in irrigated agriculture is crucial for optimizing crop production, planning and managing irrigation, and for using water resources efficiently. Validation of data insures that the information needed is been properly generated, identifies incorrect values and detects problems that require immediate maintenance attention. The Agroclimatic Information Network of Andalusia at present provides daily estimations of ET0 using meteorological information collected by nearly of one hundred automatic weather stations. It is currently used for technicians and farmers to generate irrigation schedules. Data validation is essential in this context and then, diverse quality control procedures have been applied for each station. Daily average of several meteorological variables were analysed (air temperature, relative humidity and rainfall). The main objective of this study was to develop a quality control system for daily meteorological data which could be applied on any platform and using open source code. Each procedure will either accept the datum as being true or reject the datum and label it as an outlier. The number of outliers for each variable is related to a dynamic range used on each test. Finally, geographical distribution of the outliers was analysed. The study underscores the fact that it is necessary to use different ranges for each station, variable and test to keep the rate of error uniform across the region.
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33

Lomas, J., J. R. Milford, and E. Mukhala. "Education and training in agricultural meteorology: current status and future needs." Agricultural and Forest Meteorology 103, no. 1-2 (June 2000): 197–208. http://dx.doi.org/10.1016/s0168-1923(00)00112-x.

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34

HARAZONO, Yoshinobu, Akira MIYATA, Tsuneo KUWAGATA, and Takahiro HAMASAKI. "Report of 23rd Conference on Agricultural and Forest Meteorology/AMS 98." Journal of Agricultural Meteorology 55, no. 1 (1999): 47–52. http://dx.doi.org/10.2480/agrmet.55.47.

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35

von Wilamowitz-Moellendorff, T. "“Weather Science for Agriculture” by Guiseppe Toaldo, 1774, the First Comprehensive Specialist Book on Agricultural Meteorology." Meteorologische Zeitschrift 3, no. 1 (March 8, 1994): 39–41. http://dx.doi.org/10.1127/metz/3/1994/39.

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36

Silva, Madson Tavares, José Nildo da Nóbrega, Oseas Machado Gomes, and José Ivaldo B. de Brito. "Estudo da Relação entre Monitoramento Climático e a Produção Agrícola de Grãos nos Estados da Paraíba, Rio Grande do Norte e Ceará (Study of the Relationship Between Agricultural Production and Climate Monitoring Grain in the States of Paraiba, Rio...)." Revista Brasileira de Geografia Física 4, no. 2 (September 23, 2011): 322. http://dx.doi.org/10.26848/rbgf.v4i2.232723.

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O objetivo do presente estudo é verificar se o monitoramento do clima que vem sendo observado no estado do Ceará através da FUNCEME (Fundação Cearense de Meteorologia e Recursos Hídricos) nos últimos vinte anos, para o melhoramento da produção agrícola de grão apresenta resultados satisfatórios. Para tanto, foi feita uma comparação da variabilidade inter-anual da produção agrícola e da precipitação pluvial para os estados do Ceará, Rio Grande do Norte e Paraíba. Observou-se que a variabilidade inter-anual da precipitação é a mesma nos três Estados e que não ocorreu tendência de aumento ou diminuição dos totais anuais de chuva. Verificou-se que nos anos mais secos a produção agrícola de grãos foi baixa em todos os Estados. Entretanto, durante o período analisado ocorreu uma tendência de aumento substancial da produção agrícola de grãos no Ceará e uma tendência de diminuição na Paraíba e Rio Grande do Norte. Conclui-se que o monitoramento climático realizado pelo Governo do Ceará tem sido relevante para o melhoramento da produção agrícola de grãos no Estado.Palavras - chave: Climatologia, Precipitação, Agricultura Study of the Relationship Between Agricultural Production and Climate Monitoring Grain in the States of Paraiba, Rio Grande do Norte and Ceará ABSTRACTThe objective is to verify that the climate monitoring being done in the state of Ceará by FUNCEME (Ceará Foundation for Meteorology and Water Resources) in the last twenty years, to improve the agricultural production of grain produces satisfactory results. To this end, a comparison was made of inter-annual variability of agricultural production and rainfall for the states of Ceará, Rio Grande do Norte and Paraiba. It was observed that the inter-annual variability of rainfall is the same in all three countries and that there was no tendency to increase or decrease the total annual rainfall. It was found that the driest year in agricultural production of grain was low in all states. However, during the period under review there was a trend of substantial increase in grain farming in Ceará and a downward trend in Paraiba and Rio Grande do Norte. We conclude that climate monitoring conducted by the Government of Ceará has been relevant to the improvement of agricultural production of grain in the state. Keywords: Climatology, Precipitation, Agriculture
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37

Platt, Michael. "Animal Cognition: Monkey Meteorology." Current Biology 16, no. 12 (June 2006): R464—R466. http://dx.doi.org/10.1016/j.cub.2006.05.033.

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38

Katkar, M. G., S. B. Kharbade, S. Y. Wankhede, A. A. Shaikh, and V. A. Sthool. "Agrometeorological Indices Influenced by Varying Planting Windows and Varieties of Brinjal (Solanum melongena L.) in Maharashtra, India." International Journal of Environment and Climate Change 13, no. 10 (August 23, 2023): 933–37. http://dx.doi.org/10.9734/ijecc/2023/v13i102738.

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An experiment was carried out at the Faculty of Agriculture Department of Agricultural Meteorology Farm, Centre for Advanced Agricultural Meteorology,College of Agriculture, Pune during Kharif seasons of 2014 and 2015.The experiment was laid out a split plot design with three replications.The treatment comprised of three brinjal hybrids viz.,V1:Phule Arjun, V2: Krishna, V3: Panchganaga as main plot and four planting windows viz., P1: 31st MW (30 July-5 August), P2: 32ndMW (6-12August), P3:33rdMW (13-19 August) and P4: 34thMW (20-26 August) as subplot treatments. Micrometeorological studies of Cumulative GDD, HTU, and PTU at the end of each growth stage showed that the numerically higher requirement was observed in hy.Phule Arjun over hy.Krishna and hy.Panchganga hybrids during both the years 2014 and 2015 experimentation period.Whereas, the lowest canopy temperature was found in hy.Phule Arjun (29.0 0C) than the rest of the brinjal hybrids. Canopy reflected PAR and transmitted PAR was higher in (191.54 and 188.62 µ mol m-2s-1) Panchganaga hybrids among the brinjal hybrids. Amongst all the brinjal hybrids, Phule Arjun hybrids were found significantly superior under extended planting windows followed by Krishna and Panchganga. Planting during 31st MW (1st week of August) was observed to be most suitable and optimum for brinjal considering the growth and yield attributes. This planting window was at par with the 32nd MW planting window. Linear correlation analysis for brinjal fruit yield with weather parameters was significantly positively correlated with maximum temperature and minimum temperature.
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39

Liu, Xueying, Amos P. K. Tai, and Ka Ming Fung. "Responses of surface ozone to future agricultural ammonia emissions and subsequent nitrogen deposition through terrestrial ecosystem changes." Atmospheric Chemistry and Physics 21, no. 23 (December 3, 2021): 17743–58. http://dx.doi.org/10.5194/acp-21-17743-2021.

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Abstract. With the rising food demands from the future world population, more intense agricultural activities are expected to cause substantial perturbations to the global nitrogen cycle, aggravating surface air pollution and imposing stress on terrestrial ecosystems. Much less studied, however, is how the terrestrial ecosystem changes induced by agricultural nitrogen deposition may modify biosphere–atmosphere exchange and further exert secondary feedback effects on global air quality. Here we examined the responses of surface ozone air quality to terrestrial ecosystem changes caused by year 2000 to year 2050 changes in agricultural ammonia emissions and the subsequent nitrogen deposition by asynchronously coupling between the land and atmosphere components within the Community Earth System Model framework. We found that global gross primary production is enhanced by 2.1 Pg C yr−1, following a 20 % (20 Tg N yr−1) increase in global nitrogen deposition by the end of the year 2050 in response to rising agricultural ammonia emissions. Leaf area index was simulated to be higher by up to 0.3–0.4 m2 m−2 over most tropical grasslands and croplands and 0.1–0.2 m2 m−2 across boreal and temperate forests at midlatitudes. Around 0.1–0.4 m increases in canopy height were found in boreal and temperate forests, and there were ∼0.1 m increases in tropical grasslands and croplands. We found that these vegetation changes could lead to surface ozone changes by ∼0.5 ppbv (part per billion by volume) when prescribed meteorology was used (i.e., large-scale meteorological responses to terrestrial changes were not allowed), while surface ozone could typically be modified by 2–3 ppbv when meteorology was dynamically simulated in response to vegetation changes. Rising soil NOx emissions, from 7.9 to 8.7 Tg N yr−1, could enhance surface ozone by 2–3 ppbv with both prescribed and dynamic meteorology. We, thus, conclude that, following enhanced nitrogen deposition, the modification of the meteorological environment induced by vegetation changes and soil biogeochemical changes are the more important pathways that can modulate future ozone pollution, representing a novel linkage between agricultural activities and ozone air quality.
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40

O. Rauff, Kazeem, and Rasaq Bello. "A Review of Crop Growth Simulation Models as Tools for Agricultural Meteorology." Agricultural Sciences 06, no. 09 (2015): 1098–105. http://dx.doi.org/10.4236/as.2015.69105.

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41

TORITANI, Hitoshi, and Yoshinori SUZUKI. "To Students and Young Scholars Who Intend to Study about Agricultural Meteorology." Journal of Agricultural Meteorology 46, no. 4 (1991): 239–43. http://dx.doi.org/10.2480/agrmet.46.239.

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42

IIZUMI, Toshichika, Yuji MASUTOMI, Takahiro TAKIMOTO, Tomoyoshi HIROTA, Akiyo YATAGAI, Kenichi TATSUMI, Kazuhiko KOBAYASHI, and Toshihiro HASEGAWA. "Emerging research topics in agricultural meteorology and assessment of climate change adaptation." Journal of Agricultural Meteorology 74, no. 1 (2018): 54–59. http://dx.doi.org/10.2480/agrmet.d-17-00021.

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Lambkin, Keith. "Agricultural Meteorology in Ireland – a historical perspective from the Irish Meteorological Service." Biological Rhythm Research 50, no. 2 (October 3, 2018): 298–308. http://dx.doi.org/10.1080/09291016.2018.1518870.

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Damini, Prof Miss Dhenge, Miss Kamble Ashwini, and Miss More Dipali. "Research Paper on What is Artificial Intelligence and Its Applications." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 02 (February 8, 2024): 1–10. http://dx.doi.org/10.55041/ijsrem28575.

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Artificial intelligence(AI) is a science that involves simulation of intelligent behaviors in machineries, like visual perception, decision making, speech recognition and so on. AI is a computational model that allows computers to learn from data and approximate solutions for complex functions. Due to their flexibility and robustness, AI has been widely applied in large scale fields ranging from robotics to airplane flight control. This chapter discusses the advances in all aspect of AI applied in several issues, such as hydrology, agronomy, meteorology, education, healthcare, action, and more. It focuses specifically on various AI applications related to water and soil management and states that AI achieves high performance, accuracy, and correlation with low statistical errors as a rapid decision tool under changing climate conditions. Brief introductions of AI with their adaptability to agricultural water and soil management are also interpreted. Furthermore, this chapter illustrates how the AI tool will help agricultural decision makers and water and soil managers achieve agricultural sustainability. Nowadays speech interfaces are becoming more common and popular becoming a part of daily lives. Speech interfaces have the ability to produce intelligible speech in cases where it is not possible for speech production. Keywords: Visual Perception, hydrology, agronomy, meteorology, computational.
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KUMAR, VINOD, O. P. BISHNOI, SURENDER SINGH, and V. U. M. RAO. "Studies of microclimatic conditions in summer moong as influenced by different mulches." MAUSAM 46, no. 4 (January 2, 2022): 423–26. http://dx.doi.org/10.54302/mausam.v46i4.3328.

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A field experiment was conducted during summer season of 1990 at research farm of Department of Agricultural Meteorology, Haryana Agricultural University, Hisar to study the microclimatic conditions in moong with the use of mulches. Latent heat enelgY and sensible heat energy were the main components of net energy. Among the various treatments, the latent heat energy use was found higher in black polythene sheet mulch. Soil temperature values were low in straw and white polythene mulch than black polythene mulch treatment
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Pandey, K. S., H. Shrestha, and L. P. Devkota. "Impacts of climate change on agricultural production in Nepal: Case of Kavre and Jumla districts." Nepal Journal of Environmental Science 2 (December 8, 2014): 43–50. http://dx.doi.org/10.3126/njes.v2i0.22740.

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The study the analyzed relationship of climate change with agricultural production in Kavre and Jumla districts. The specific objective of the study was to find out the dimension and linkage between agricultural production and climatic parameters in Kavre and Jumla. Time series data were analysed for the study. The data was sourced from the Department of Hydrology Meteorology, Department of Agriculture, and National Bureau of Statistics. Descriptive statistics, linear analysis test and back ward difference filter were the analytical tools used to determine the impact of climate change on productivity. During harvest period, the correlation of rice yield with temperature and rainfall was negative at Kavre but positive at Jumla. Similarly, the correlation of wheat yield with temperature and rainfall was positive at Kavre but negative at Jumla. The result showed that extreme fluctuation in weather caused negative impact on production in Jumla in compared to Kavre districts.
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Katkar, M. G., S. B. Kharbade, S. Y. Wankhede, A. A. Shaikh, and V. A. Sthool. "Thermal Indices Requirement of Brinjal Varieties (Solanum melongena L.) under Different Planting Windows." International Journal of Environment and Climate Change 13, no. 10 (August 17, 2023): 446–53. http://dx.doi.org/10.9734/ijecc/2023/v13i102665.

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An experiment was carried out at Faculty of Agriculture Department of Agricultural Meteorology Farm, Centre for Advanced Agricultural Meteorology, College of Agriculture, Pune during Kharif seasons of 2014 and 2015.The experiment was laid out in split plot design with three replications.The treatment comprised of three brinjal hybrids viz.,V1:Phule Arjun, V2: Krishna, V3: Panchganaga as main plot and four planting windows viz., P1: 31st MW (30 July-5 August), P2: 32ndMW (6-12August), P3:33rdMW (13-19 August) and P4: 34thMW (20-26 August) as sub plot treatments. Cumulative GDD, HTU and PTU at the end of each growth stages showed that numerically higher requirement was observed in hy.Phule Arjun over hy.Krishana and hy.Panchganaga hybrids during both year 2014 and 2015 experimentation period. Whereas, the lowest canopy temperature was found in hy.Phule Arjun (29.0 0C) than rest of the brinjal hybrids. Canopy reflected PAR and transmitted PAR was higher in (191.54 and 188.62 µ mol m-2s-1) Panchganaga hybrids among the brinjal hybrids. Heat unit requirement or GDD has been used for characterizing the thermal response in brinjal crop. GDD for entire crop growing period decreased with subsequent delay in planting. HTU and PTU were also decreased during later planting windows condition. GDD in different stages in that emergence (59.6 and 72.3), vegetative growth (481 and 478), 50% flowering (575 and 568), first harvesting (681 and 645), last harvesting (1178 and 1183) was observed in hybrid Phule Arjun during 2014 and 2015, respectively. Lower GDD was observed in hy.Panchaganaga during 2014 and 2015, respectively. The highest HTU observed in 31st MW planting windows in hybrids Phule Arjun (5376 and 9190.4).This was followed by hy.krishna and Panchganaga (5370 and 9086) during 2014 and 2015, respectively. Highest HTU was observed in 31st MW in hybrids Phule Arjun followed by hy.krishna and lower in panchganga.
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Turner, John. "Aspects of modern Antarctic meteorology and climatology." Archives of Natural History 32, no. 2 (October 2005): 334–45. http://dx.doi.org/10.3366/anh.2005.32.2.334.

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Great advances have been made in recent years in our understanding of the weather of the Antarctic and how the climate of the continent varies on a range of time-scales. The observations from the stations are still the most accurate meteorological measurements that we have, but satellites have been important in providing data for remote parts of the continent and the Southern Ocean. With the large amount of data that is available today weather forecasts are much more accurate than just a few years ago and can provide valuable guidance up to several days ahead over the Southern Ocean and Antarctic coastal region. However, predicting the weather for the interior of the Antarctic is still very difficult. Recent research has shown that the climate of the Antarctic is affected by tropical atmospheric and oceanic climate cycles, such as the El Niño-Southern Oscillation, but the links are complex. The picture of climate change across the Antarctic during the last 50 years is complex, with only the Antarctic Peninsula showing a significant warming. By the end of the twenty-first century near-surface air temperatures across much of the Antarctic continent are expected to increase by several degrees. A small increase in precipitation is also expected.
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Stoyanova, Natally. "The effect of climate change on agricultural production in Bulgaria." JOURNAL OF GLOBAL CLIMATE CHANGE 1, no. 1 (June 12, 2022): 33–38. http://dx.doi.org/10.56768/jytp.1.1.05.

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Plant organisms are phenological indicators of weather and climate and are often used as a non-instrumental tool for its analysis. The reactions of crops, their growth and development are a direct result of environmental conditions. Solar radiation, air temperature and precipitation are the main factors that determine their productivity. In search of the environment-plant connection, the science of agricultural meteorology emerged. This publication systematizes some of the main challenges facing agriculture and the main measures for adapting the sector to modern climatic conditions. Climate change and fluctuations lead to changes in the conditions of growth and development of agricultural crops. This has a direct bearing on the way the world produces, distributes and consumes food. Climate is directly related to the way and prospects for global production needed to sustain the human population. The population of people in the world is expected to reach to 10 billion by 2050. This poses a huge challenge to the global community on how to feed an additional 2.3 billion people through environmentally friendly methods and climate change.
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VIRK, HARDEV SINGH. "C.V. Raman's Student L.A. Ramdas - From Agricultural Meteorology to Discovery of Ramdas Layer." Journal of Agrometeorology 25, no. 4 (November 30, 2023): 616–18. http://dx.doi.org/10.54386/jam.v25i4.2393.

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Indian Physicist Dr C.V. Raman, the founder of the Raman Spectroscopy, is the only Indian who received Nobel Prize in Science. Raman trained almost 100 scientists in his laboratory who influenced the development of science and technology in India. Dr L A Ramdas was one of them who began his research career under Raman in the beginning of 1920s. Not only, he coined the term ‘Raman Effect’, but also studied the scattering of light in gases and vapours. The present book written by Dr Rajinder Singh, presents Ramdas’s work on light scattering in association with Raman, his venture in establishing a new field namely, Agricultural Meteorology, and subsequently the discovery of Ramdas Layer, named after him.
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