Search

Potassium (K) is one of the essential nutrients that play a crucial role in rice fields; however, its availability, transport, and balance components can be influenced by alternating wetting and drying (AWD) irrigation practice. This study used the Hydrus-1D solute transport model to simulate K transport and balance components in AWD irrigated rice fields. Thirty lysimeter field experiment plots with three different irrigation treatments (No: no soil water stress, MS: mild soil water stress (400 cm), and SS: severe soil water stress (750 cm)) were used for rice cultivation. Daily measurements of soil matric potential head (SMPH) and ponding water depth (PWD) were recorded. Throughout the rice-growing season, leachate water samples from the surface (ponding water) and soil profiles were collected to measure K content. Using the lysimeter field experiment data, the Hydrus-1D model simulated K transport in the AWD rice field. The simulation results indicate that the K distribution predominantly occurred within the top puddled soil layer (0–20 cm) in AWD practice. This suggests that the recommended K fertilizer dose with a 4–5 cm PWD and SMPH of 400 cm optimizes K availability for rice while limiting K leaching loss. The Hydrus-1D model proves to be a valuable management tool, aiding in determining optimal K application rates and ponding water depths for AWD irrigation, ensuring sufficient K nutrients for rice, and controlling K loss for sustainable rice production. [ABSTRACT FROM AUTHOR]

Wheat is a very vital cereal crop in the development of a nation's economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin's concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively. [ABSTRACT FROM AUTHOR]

Assessment of crop nitrogen status is essential for efficient crop growth management. Existing nitrogen measurements are accurate but destructive, laborious, and time-consuming. Therefore, the soil plant analysis development (SPAD) meter approach is commonly used to address these challenges along with locationspecific measurements. The study aims to develop a robust machine learning-based model for predicting rice crop SPAD values using spectral data and to generate spatial maps of SPAD values and nitrogen content. The SPAD meter data, UAV-based multispectral images, and spectroradiometer-based data were collected during Rabi 2021/22 and 2022/23 seasons. The red and rededge bands, Normalized Difference Vegetation Index, and Normalized Pigment Chlorophyll Index correlated well with SPAD values. The random forest regressor model performed well with UAV-based data compared to support vector regression and partial least square regression and achieved good accuracy with the ground truth spectroradiometer data. This generalized model demonstrates adaptability in precisely assessing crop nitrogen status. [ABSTRACT FROM AUTHOR]

Multispectral (MS) images offer essential spectral information for monitoring paddy crops' Aboveground-biomass (AGB), but efficiency decreases due to background materials and high canopy biomass. Texture reveals canopy structure and can be employed in vegetation-indices (VIs) to enhance monitoring accuracy. This study focuses to estimate AGB of paddy crop by exploring the combined potential of spectral and textural features of unmanned aerial vehicle (UAV)-MS images using linear regression (LR), multilinear regression (MLR), and random forest (RF) models. Results demonstrate that near infrared (NIR)-based VIs outperform Colour-Indices. Normalised difference texture indices (NDTIs) composed of NIR, red-edge (RE) and blue (B) bands outperform all-evaluated VIs and grey-level co-occurrence matrix (GLCM)-textures for different growth stages. Combining VIs and NDTIs, RF performs best compared to other models. The outcomes suggest that the combined spectral and texture information can significantly improve estimation of AGB in paddy crops compared to using either of them alone. [ABSTRACT FROM AUTHOR]

The alternate wetting and drying (AWD) irrigation practice involves soil moisture stress-based irrigation scheduling which possibly affects the rice field's phosphorus (P) transport and balance components. The focus of this study was to understand P-transport under AWD irrigation and estimate the associated balance components in rice using a solute transport model, Hydrus-1D. In this study, rice was cultivated in 30 lysimeter plots with three irrigation treatments (no soil water stress/NS, mild soil water stress/400 cm/MS, and severe soil water stress/750 cm/SS) and each treatment was replicated 10 times. The field experimental data were used to simulate P transport with the help of the Hydrus-1D model. The Hydrus-1D model was calibrated and validated for P transport within the soil matrix under AWD irrigation practice. The P distribution was mostly concentrated within the top puddled soil layer (0–20 cm) and did not move to the deeper soil layers. The simulated results indicate that the recommended dose of P with 3–5 cm ponding water depth will increase the P availability for rice and reduce the P leaching loss in AWD irrigation practice. The Hydrus-1D model can be used as a management tool to determine the appropriate P application rate and ponding water depth irrigated with AWD for which the required P nutrients are sufficiently available to rice and the P loss to the environment can be controlled for sustainable rice production. [ABSTRACT FROM AUTHOR]

Copyright of World Water Policy is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Irrigation & Drainage is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Inappropriate use of phosphorus and nitrogen fertilizers has imposed irreparable damage to plant growth and productivity. Urea doped hydroxyapatite nanoparticles (Ur@HANP) was designed for controlled-release formulation for crop improvement. The main objective of the present work was to synthesize and evaluate the functioning of Ur@HANP on rice seed germination and transport of nano-nutrients in soil media. HANP was synthesized using sol–gel technique and urea was loaded into it for monitoring rice seeds' physiological and biochemical activities under controlled laboratory condition. Seedling growth (5.99217 ± 0.156), fresh weight (13.8 ± 0.5) and dry weight (1.6 ± 0.5) were enhanced in Ur@HANP treatment compared to control. Both HANP and Ur@HANP was also tested in sand columns and agricultural soil to understand the retention capacity of plant nutrients in sand and interaction with soil colloids, where Ur@HANP showed almost twofold efficiency than conventional P and N fertilizers. Even at a higher dose of 1 M, it did not impart any kind of toxicity symptoms. Almost twofold increase in α-amylase and starch content in Ur@HANP could be the reason for such response. We anticipate Ur@HANP could be a suitable alternative for N and P fertilizers in the development of eco-friendly sustainable agriculture. [ABSTRACT FROM AUTHOR]