Defining and validating limits of meteorological parameters for high yield of wheat in Punjab, India

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Defining and validating limits of meteorological parameters for high yield of wheat in Punjab, India

Prabhjyot- Kaur, S. S. Sandhu, Jagjeet Kaur, Agatambidi Bala Krishna

Article ID: 2844
Vol 5, Issue 3, 2024

DOI: https://doi.org/10.54517/ama.v5i3.2844
VIEWS - 1968 (Abstract)

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Abstract

Wheat is a rabi season crop and is highly susceptible to abrupt increases/decreases in weather parameters. So, a study was conducted to compute the critical limits of temperature, relative humidity, and rainfall by analyzing meteorological and crop data (1999–00 to 2018–19) for six locations (Ballowal Saunkhari, Ludhiana, Patiala, Amritsar, Bathinda, and Faridkot) in Punjab. Amongst the 20 years, high, medium, and low yield years for each location were identified, and then meteorological data for crop growth stages, i.e., sowing-emergence (43–47 Standard Meteorological Week (SMW)), vegetative (48–02 SMW), anthesis (03–06 SMW), grain filling (07–11 SMW), and physiological maturity (12–15 SMW), were tabulated. The week-wise deviations of maximum/minimum temperature, maximum/minimum relative humidity, and rainfall from normal data of those 20 years under study were computed to derive their critical limits. Then these stage-wise critical limits were validated using the actual yields achieved during crop years 2019–20, 2020–21, and 2021–22. During a good crop year 2019–20, the upper and lower limits of the ranges accounted for high yields obtained at 03 locations and medium yields at the remaining 03 locations. During the crop year 2020–21, when the medium yield was obtained at all six locations, the major reason was the deviation of temperature above the upper range during the later grain-filling stage. On the other hand, during 2021–22, when low yield was reported at 03 locations and medium yield at remaining 03 locations, in addition to temperature deviations, heavy rainfall during SMW 1 and 2 (late vegetative stage) and hot and dry weather during SMW 10 and 11 (late grain development stage) were the major reasons. Hence it may be concluded that to get higher wheat productivity during vegetative growth, flowering, and grain filling, the maximum/minimum temperature ranges should be 16–22/4–9 ℃, 21–28/7–13 ℃ and 25–32/11–16 ℃, respectively; the maximum/minimum relative humidity ranges should be 85%–99%/39%–77%, 80%–92%/32%–66% and 75%–86%/31%–59%, respectively.

Keywords

wheat; optimum limits; temperature; relative humidity; yield; Punjab

References

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