Algorithms for weather‐based management decisions in major rainfed crops of India: Validation using data from multi‐location field experiments

Kumar, Pradeep; Bal, Santanu Kumar; Dhakar, Rajkumar; Chandran, M. A. Sarath; Rao, A V R K; Pramod, V. P.; Malleswari, S. N.; Sudhakar, G.; Solanki, N. S.; Shivaramu, H. S.; Lunagaria, M. M.; Dakhore, K. K.; Londhe, V. M.; Singh, Mahender; Kumari, Pragyan; Subbulakshmi, S.; Manjunatha, M.; Chaudhari, N. J.

doi:10.1002/agj2.20518

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…The DCWC intends to atomize agromet advisories using prevailing and forecasted weather. Modules for predicting sowing dates and phenology were validated for principal crops and varieties at selected locations by Kumar, et. al.…”

Section: Crop Weather Calendarmentioning

confidence: 99%

Decision support system for digitally climate informed services to farmers in India

et al. 2023

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India Meteorological Department (IMD), Ministry of Earth Sciences (MoES) in collaboration with Indian Council of Agriculture Research (ICAR), State Agriculture Universities (SAUs) , Indian Institute of Technology (IITs) and other organizations is rendering weather forecast based District level Agrometeorological Advisory Service (AAS) for benefits of farmers in the country under the centrally sponsored scheme ‘Atmosphere & Climate Research-Modelling Observing Systems & Services (ACROSS) ’ of MOES. AAS, popularly known as Gramin Krishi Mausam Sewa (GKMS) provides advance weather information along, with crop specific agromet advisories to the farming community by using state of the art instruments and technology through efficient delivering mechanism of the information which ultimately enables farmers to take appropriate actions at farm level. The various components of GKMS viz. observing weather, its monitoring and forecast; crop specific advisory bulletin generation and dissemination; outreach and feedback have been/are being digitized to support integrating all the components of information generation and action suggested linked to these information. An Information and Communication Technology (ICT) based Agromet Decision Support System is developed for automation of the services provided under GKMS. This includes a dynamic framework to link the information of weather forecast, real time weather observation, crop-weather calendar etc. to translate weather forecast into actionable farm advisories for efficient farm level decision making in India. Apart from this, effort is being made to develop recent technology driven tools to estimate future yield of crops and prepare an irrigation schedule without a need of multiple parameters.

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Section: Crop Weather Calendarmentioning

confidence: 99%

Decision support system for digitally climate informed services to farmers in India

et al. 2023

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“…Hence, whenever the criteria for sowing are met within the sowing window, that particular date was selected as the sowing date. More details regarding estimation of sowing time can be obtained from [25].…”

Section: Computation Of Crop and Irrigation Water Requirement 241 Est...mentioning

confidence: 99%

Water Demand in Maize Is Projected to Decrease under Changing Climate in India

et al. 2022

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Crop stage-specific information on the impacts of projected climate change on crop and irrigation water requirements are essential for improving productivity. This study investigated the possible implications of projected climate change on the phenology, effective rainfall (Peff), crop (CWR) and irrigation water requirements (IWR) of maize in eight locations in India. CWR, Peff and IWR were estimated for seven crop stages viz., emergence, 5th leaf stage, tasseling, silking, milking, dough and maturity during the baseline (1980–2009) and near-century (2022–39) using climate data derived from a subset of 29 general circulation models. The results indicated that mean seasonal maximum temperature, minimum temperature and rainfall were projected to increase in all the locations. Hence, the total crop duration (3–7 days), CWR (8–69 mm) and IWR (1–54 mm) were projected to decrease. The study could identify the specific stages in which the greatest reduction in crop duration, CWR and IWR would occur. Such information will be of immense help to farmers and varietal improvement programs in the study regions in the near future.

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“…However, cotton appears to be underrepresented. When searching for phenology estimation studies on cotton, one can find only few publications that date back more than a decade [32,33], and some more recent ones that deal with multiple crop types and do not focus explicitely on cotton [34,35]. There are also a handful of papers that evaluate the process-based model CSM-CROPGRO-Cotton, but with small-scale experiments (few fields) [36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy clustering for the within-season estimation of cotton phenology

et al. 2023

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Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level. For this, we exploit a variety of Earth observation vegetation indices (derived from Sentinel-2) and numerical simulations of atmospheric and soil parameters. Our method is unsupervised to address the ever-present problem of sparse and scarce ground truth data that makes most supervised alternatives impractical in real-world scenarios. We applied fuzzy c-means clustering to identify the principal phenological stages of cotton and then used the cluster membership weights to further predict the transitional phases between adjacent stages. In order to evaluate our models, we collected 1,285 crop growth ground observations in Orchomenos, Greece. We introduced a new collection protocol, assigning up to two phenology labels that represent the primary and secondary growth stage in the field and thus indicate when stages are transitioning. Our model was tested against a baseline model that allowed to isolate the random agreement and evaluate its true competence. The results showed that our model considerably outperforms the baseline one, which is promising considering the unsupervised nature of the approach. The limitations and the relevant future work are thoroughly discussed. The ground observations are formatted in an ready-to-use dataset and will be available at https://github.com/Agri-Hub/cotton-phenology-dataset upon publication.

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Algorithms for weather‐based management decisions in major rainfed crops of India: Validation using data from multi‐location field experiments

Cited by 7 publications

References 21 publications

Decision support system for digitally climate informed services to farmers in India

Decision support system for digitally climate informed services to farmers in India

Water Demand in Maize Is Projected to Decrease under Changing Climate in India

Fuzzy clustering for the within-season estimation of cotton phenology

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