An Overview of Meteorological Services, Challenges and Solutions in the Agricultural Sector to Reduce Vulnerability in Climate Change Conditions

Subject Areas : Farm water management with the aim of improving irrigation management indicators

Saeedeh Kamali ¹ , Ebrahim Asadi Oskouei ² , Morteza Pakdaman ³

1 - Ph. D Student in Agrometeorology, Department of Irrigation &amp, University of Tehran, Karaj, Iran.
2 - Assistant Professor, Atmospheric Science and Meteorological Research Center (RIMAS), Climate Research Institute (CRI), Mashhad, Iran.
3 - Assistant Professor, Atmospheric Science and Meteorological Research Center (RIMAS), Climate Research Institute (CRI), Mashhad, Iran.

Received: 2024-01-07 Accepted : 2024-04-24 Published : 2024-10-02

Keywords: climate change, agriculture, Agrometeorological services, digital agriculture,

Abstract :

Background and Aim: In order to adapt the agricultural systems to increased climate variability, meteorological information needs to be combined with management recommendations before the start of the cropping season. There are solutions to reduce the vulnerability of agriculture to increasing climate change through agro-meteorological advisories based on weather forecasts. Therefore, the purpose of this study is to review the services provided by agricultural meteorology to reduce the vulnerability of agriculture in climate change conditions. For this purpose, we attempt to use successful experiences from different countries, from developed countries with advanced technologies to less developed countries with local knowledge, in adapting to climate change. This includes various types of agricultural meteorological services, major obstacles, and gaps in providing them. Climatic services provide solutions to offer better climatic services to farmers, climatic services used in agricultural meteorology, as well as approaches to accelerate the adoption of agricultural meteorological technologies and consulting services by farmer.

Results and Conclusion: To be resilient against the negative effects of natural disasters and events, there is a need for resilience programs and policies to be accompanied by civic participation, the development of indigenous knowledge, and the use of technology in line with indigenous knowledge and regional infrastructures, according to the geographical, social, and cultural characteristics of each region. Additionally, the decisions of governments and policymakers, in line with the establishment of laws and regulations and resilience policies, can strengthen or limit the ability of other actors to adapt to the effects of climate change. In the same vein, the results of the review of sources include a list of existing barriers and gaps, such as the lack of real climate forecasts, lack of information on crop growth and development, low participation of agricultural producers in the production and use of services, unfair access to communication channels, and insufficient adaptation of agricultural meteorological services to needs. It also highlights the need to move towards the transformation of digital agriculture, targeted training in Agro-meteorology, strengthening the role of information and communication technology in providing accurate information, increasing investment in installing automatic weather stations and radar, and holding seminars and training workshops to overcome obstacles and improve the quality of climate services in the agricultural sector.

References:

Abdul Manafi, N. Al-S., Mazaheri, M. (2019). Agricultural meteorology and upcoming opportunities (2). Islamic Parliament Research Center of the Islamic Republic of IRAN. [in Persian]
Alexander, S. (2019). What climate-smart agriculture means to members of the Global Alliance for climate-smart agriculture. Future Food J. Food Agric. Soc. 2019, 7, 21–30.
Arslan, A., McCarthy, N., Lipper, L., Asfaw, S., Cattaneo, A., Kokwe, M. (2015). Climate smart agriculture? Assessing the adaptation implications in Zambia. J. Agric. Econ. 66, 753–780.
Bacci, M., Idrissa, O. A., Zini, C., Burrone, S., Sitta, A. A., and Tarchiani, V. (2023). Effectiveness of agrometeorological services for smallholder farmers: the case study in the regions of Dosso and Tillabéri in Niger, Clim. Serv., 30, 100360, https://doi.org/10.1016/j.cliser.2023.100360.
Balaghi, R., Jlibene, M., Tychon, B. & Eerens, H. (2013). Agrometeorological cereal yield forecasting in Morocco. Institut National de la Recherche Agronomique du Maroc (INRA).
Callaghan, M., Schleussner, C. F., Nath, S., Lejeune, Q., Knutson, T. R., Reichstein, M., ... & Minx, J. C. (2021). Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies. Nature climate change, 11(11), 966-972.
CILSS (Permanent Interstate Committee for Drought Control in the Sahel). (2020). AGHRYMET regional center. In: CILSS Publications: Bulletins. https://agrhymet.cilss.int/index. php/bulletins/
Ciruela-Lorenzo, A. M., Del-Aguila-Obra, A. R., Padilla-Meléndez, A., & Plaza-Angulo, J. J. (2020). Digitalization of agri-cooperatives in the smart agriculture context. proposal of a digital diagnosis tool. Sustainability, 12(4), 1325.
Edalat Moghadam, M. (2022). Building Climate-Resilient Communities; Say No to Migration. Science and Technology Policy Letters, 12(4), 98-80. [in Persian]
Ehsani, M., & Shokoohi, Z. (2022). Estimation of Iran's agricultural resilience index to climate change. Strategic Research Journal of Agricultural Sciences and Natural Resources, 7(1), 63-78. doi: 10.22047/srjasnr.2022.147432. [in Persian]
Escamilla-García, A., Soto-Zarazúa, G. M., Toledano-Ayala, M., Rivas-Araiza, E., & Gastélum-Barrios, A. (2020). Applications of artificial neural networks in greenhouse technology and overview for smart agriculture development. Applied Sciences, 10(11), 3835.
European Environment Agency. (2023). Climate Change Adaptation in the Agriculture Sector in Europe, EEA Report No. 04/2019, EEA, https://www.eea.europa.eu/publications/cc-adaptation-agriculture .
FAO. (2021). Global outlook on climate services in agriculture – Investment opportunities to reach the last mile, FAO, Rome, https://doi.org/10.4060/cb6941en.
FAO. The State of Food and Agriculture. (2020). Overcoming Water Challenges in Agriculture; Food and Agriculture Organisation of the United Nations: Rome, Italy.
Food and Agriculture Organization of the United Nations. (2021). Global outlook on climate services in agriculture: Investment opportunities to reach the last mile. Food & Agriculture Org, 153.
Hamed, S., Ibba, P., Petrelli, M., Ciocca, M., Lugli, P., & Petti, L. (2021, November). Transistor-based plant sensors for agriculture 4.0 measurements. In 2021 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) (pp. 69-74). IEEE.
Harris, D.; Orr, A. (2014). Is rainfed agriculture really a pathway from poverty? Agric. Syst. 123, 84–96.
Hasan, M.K., Desiere, S., D’Haese, M., Kumar, L. (2018). Impact of climate-smart agriculture adoption on the food security of coastal farmers in Bangladesh. Food Secur. 10, 1073–1088.
https://library.wmo.int/index.php?lvl=notice_display&id=16002. https://library.wmo.int/index.php?lvl=notice_display&id=12113.
ICPAC. (2021). ICPAC [online]. [Cited 25 January 2021]. https://www.icpac.net/
Iran Meteorological Organization, www.irimo.ir. [in Persian]
Javaid, M., Haleem, A., Singh, R. P., & Suman, R. (2022). Enhancing smart farming through the applications of Agriculture 4.0 technologies. International Journal of Intelligent Networks, 3, 150-164.
Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in space research, 15(11), 91-100.
Lajoie-O'Malley, A., Bronson, K., van der Burg, S., & Klerkx, L. (2020). The future (s) of digital agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosystem Services, 45, 101183.
Lowder, S. K., Skoet, J., and Raney, T. (2016). The number, size, and distribution of farms, smallholder farms, and family farms worldwide, World Dev., 87, 16–29, https://doi.org/10.1016/j.worlddev.2015.10.041. 
Manlio, B., Paolo, B., Alberto, G., & Massimiliano, R. (2021). Unmanned Aerial Vehicles for Agriculture: An Overview of IoT‐Based Scenarios. Autonomous Airborne Wireless Networks, 217-235.
McKune, S., Poulsen, L., Russo, S., Devereux, T., Faas, S., McOmber, C., Ryley, T. (2018). Reaching the end goal: Do interventions to improve climate information services lead to greater food security? Clim. Risk Manag. 22, 22–41.
Ministry of the Environment and Spatial Planning. (2016). Workshop Agrometeorologists for farmers in hotter, drier, wetter future, 9–10 November 2016, Ljubljana, http://www.dmcsee.org/en/news/122 (last access: 25 January 2023. 
Narinbaeva, G., Menglikulov, B., Siddikov, Z., Bustonov, K., & Davlatov, S. (2021). Application of innovative technologies in agriculture of Uzbekistan. In E3S Web of Conferences (Vol. 284). EDP Sciences.
NCAER. (2010). Impact Assessment and Economic Benefits of Weather and Marine Services. National Council of Applied Economic Research, New Delhi.
NCAER. (2015). Economic Benefits of Dynamic Weather and Ocean Information and Advisory Services in India and Cost and Pricing of Customized Products and Services of ESSONCMRWF & ESSO-INCOIS. National Council of Applied Economic Research, New Delhi.
NEPAD Secretariat. (2009). Financial resources and investment for climate change. In: The Africa Partnership Forum Paper on Climate Change. Addis Ababa, Ethiopia.
Pakdaman, M., Babaeian, I., Javanshiri, Z., & Falamarzi, Y. (2022). European multi model ensemble (EMME): A new approach for monthly forecast of precipitation. Water Resources Management, 36(2), 611-623.
Pakdaman, M., Babaeian., I., Bouwer, LM. (2022). Improved Monthly and Seasonal Multi-Model Ensemble Precipitation Forecasts in Southwest Asia Using Machine Learning Algorithms. Water. 14(17):2632
Patil, K. A., & Kale, N. R. (2016, December). A model for smart agriculture using IoT. In 2016 international conference on global trends in signal processing, information computing and communication (ICGTSPICC) (pp. 543-545). IEEE.
Pilarova, T., Bavorova, M., Kandakov, A. (2018). Do farmer, household and farm characteristics influence the adoption of sustainable practices? The evidence from the Republic of Moldova. Int. J. Agric. Sustain. 16, 367–384.
Pinto, D. M., Oliveira, P. D., Fachini Minitti, A., Mansur Mendes, A., Freitas Vilela, G., Castro, G. S. A., ... & Stachetti Rodrigues, G. (2021). Impact assessment of information and communication technologies in agriculture: application of the ambitec-TICs method. Journal of technology management & innovation, 16(2), 91-101.
Ponnusamy, K. (2018). Proofing farmers against climate and weather extremities. ICAR-National Dairy Research Institute, Karnal-132001, Haryana, India.
Rathore, L.S. and Parvinder Maini. (2008). Economic Impact Assessment of Agro-Meteorological Advisory Service of NCMRWF. Report no, Ministry of Earth Sciences, Government of India, NOIDA, UP, INDIA.
Roy, S. K., & De, D. (2022). Genetic algorithm based internet of precision agricultural things (IopaT) for agriculture 4.0. Internet of Things, 18, 100201.
S.C. Bhan. (2018). Economic Benefits of Existing Agrometeorological Advisory System of IMD to the Farmers of India. India Meteorological Department, New Delhi -110 003.
SEBBARI, R., Morocco, DMN. (2016). WMO RA VI Workshop on RCC Implementation and
the second Coordination meeting of RAVI RCC Network Belgrade, Serbia, 11 – 14 October.
Singh, G., Sharma, Y.R., Shanmugasundaram, S., Shih, S.L. and Green, S.K. (2004). Status of mungbean yellow mosaic virus resistance breeding. In Proc: “Final Workshop and Planning Meeting on Mungbean”. Punjab Agricultural University. pp. 204-213.
Singh, S.V. (2018). National Use of Weather Forecast for Livestock Management in Agrometeorological Services. Animal Physiology Division, ICAR-National Dairy Research Institute, Karnal, Haryana, India, 64 pp.
Smit, B., & Pilifosova, O. (2003). Adaptation to climate change in the context of sustainable development and equity. Sustainable Development, 8(9), 9.
Tarchiani, V., Camacho, J., Coulibaly, H., Rossi, F., & Stefanski, R. (2018). Agrometeorological services for smallholder farmers in West Africa. Advances in Science and Research, 15, 15-20.
Tarchiani, V., Coulibaly, H., Baki, G., Sia, C., Burrone, S., Nikiema, P. M., Migraine, J. B., and Camacho, J. (2021) Access, uptake, use and impacts of agrometeorological services in Sahelian rural areas: the case of Burkina Faso, Agronomy, 11, 2431, https://doi.org/10.3390/agronomy11122431. 
Tarchiani, V., Rapisardi, E., Parrish, P., Di Giuseppe, E., Bacci, M., Baldi, M., and Pasqui, M. (2020). Competencies based innovative learning solutions for co-development of climate services in West Africa, Adv. Sci. Res., 17, 47–52, https://doi.org/10.5194/asr-17-47-2020. 
Teagasc. (2023). Signpost Programme, https://www.teagasc.ie/environment/climate-change--air-quality/signpost-programme/ (last access: 25 January 2023).
Totin, E., Segnon, A.C., Schut, M., Affognon, H., Zougmoré, R.B., Rosenstock, T., Thornton, P.K. (2018). Institutional Perspectives of Climate-Smart Agriculture: A Systematic Literature Review. Sustainability, 10, 1990.
Vincent, K., Daly, M., Scannell, C., and Leathes, B. (2018). What can climate services learn from theory and practice of co-production? Clim. Serv., 12, 48–58, https://doi.org/10.1016/j.cliser.2018.11.001. 
WHO. (2023). Climate change and health, https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health (last access: 25 January 2023).
WMO. (1972). Technical Note No. 123, Geneva.
WMO. (2020). 2020 State of Climate Services, WMO No. 1252, WHO, Geneva, https://library.wmo.int/index.php?lvl=notice_display&id=21777#.X4VUG5MzZR4 (last access: 27 January 2023), 2020. 
World Bank, 2008. Weather and Climate Services in Europe and Central Asia, A Regional Review. The World Bank.