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 6 publications

(2 citation statements)

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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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Section: Introductionmentioning

confidence: 99%

Fuzzy clustering for the within-season estimation of cotton phenology

et al. 2023

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“…Biological development rates are linearly related to temperature. It is important to include the variations in temperature in agricultural models which describe the crop phenology and development based on heat accumulation 4 . Although daily mean temperature determines the growth and development of crop plants, the day and night cycle/diurnal pattern of temperature is more important as it affects many plant morphological characteristics like leaf, shoot orientation, plant height and internodes length etc.…”

Section: Introductionmentioning

confidence: 99%

Identifying appropriate prediction models for estimating hourly temperature over diverse agro-ecological regions of India

Bal

Pramod

Manikandan

et al. 2023

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The present study tests the accuracy of four models in estimating the hourly air temperatures in different agroecological regions of the country during two major crop seasons, kharif and rabi, by taking daily maximum and minimum temperatures as input. These methods that are being used in different crop growth simulation models were selected from the literature. To adjust the biases of estimated hourly temperature, three bias correction methods (Linear regression, Linear scaling and Quantile mapping) were used. When compared with the observed data, the estimated hourly temperature, after bias correction, is reasonably close to the observed during both kharif and rabi seasons. The bias-corrected Soygro model exhibited its good performance at 14 locations, followed by the WAVE model and Temperature models at 8 and 6 locations, respectively during the kharif season. In the case of rabi season, the bias-corrected Temperature model appears to be accurate at more locations (21), followed by WAVE and Soygro models at 4 and 2 locations, respectively. The pooled data analysis showed the least error between estimated (uncorrected and bias-corrected) and observed hourly temperature from 04 to 08 h during kharif season while it was 03 to 08 h during the rabi season. The results of the present study indicated that Soygro and Temperature models estimated hourly temperature with better accuracy at a majority of the locations situated in the agroecological regions representing different climates and soil types. Though the WAVE model worked well at some of the locations, estimation by the PL model was not up to the mark in both kharif and rabi seasons. Hence, Soygro and Temperature models can be used to estimate hourly temperature data during both kharif and rabi seasons, after the bias correction by the Linear Regression method. We believe that the application of the study would facilitate the usage of hourly temperature data instead of daily data which in turn improves the precision in predicting phenological events and bud dormancy breaks, chilling hour requirement etc.

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