Recalibrating plant water status of winter wheat based on nitrogen nutrition index using thermal images

Zhao, Ben; Traore, Adama; Ata-Ul-Karim, Syed Tahir; Guo, Yan; Liu, Zhandong; Xiao, Junfu; Liu, Zugui; Qin, Anzhen; Ning, Dongfeng; Duan, Aiwang

doi:10.1007/s11119-021-09859-y

Cited by 6 publications

(2 citation statements)

References 35 publications

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“…The real gain of N use and differences in grain yield can be calculated using both the factor of nitrogen nutrition index (NNI) (Lemaire and Ciampitti, 2020;Zhao et al, 2022) and the partial factor productivity of nitrogen (PFP N ) (Figure 4). To minimize the N losses through leaching, volatilization and denitrification were managed by the placement and timing of fertilizer N in tiny doses and with more splits through SDI utilizing fertigation to meet the crop need and enhanced NNI, as well as its efficiency in RW system.…”

Section: Fertilizer-n Nutrition Index and Efficiencymentioning

confidence: 99%

Bundling subsurface drip irrigation with no-till provides a window to integrate mung bean with intensive cereal systems for improving resource use efficiency

Kakraliya,

Jat,

Kumar

et al. 2024

Front. Sustain. Food Syst.

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The future of South Asia’s major production system (rice–wheat rotation) is at stake due to continuously aggravating pressure on groundwater aquifers and other natural resources which will further intensify with climate change. Traditional practices, conventional tillage (CT) residue burning, and indiscriminate use of groundwater with flood irrigation are the major drivers of the non-sustainability of rice–wheat (RW) system in northwest (NW) India. For designing sustainable practices in intensive cereal systems, we conducted a study on bundled practices (zero tillage, residue mulch, precise irrigation, and mung bean integration) based on multi-indicator (system productivity, profitability, and efficiency of water, nitrogen, and energy) analysis in RW system. The study showed that bundling conservation agriculture (CA) practices with subsurface drip irrigation (SDI) saved ~70 and 45% (3-year mean) of irrigation water in rice and wheat, respectively, compared to farmers’ practice/CT practice (pooled data of Sc1 and Sc2; 1,035 and 318 mm ha−1). On a 3-year system basis, CA with SDI scenarios (mean of Sc5–Sc8) saved 35.4% irrigation water under RW systems compared to their respective CA with flood irrigation (FI) scenarios (mean of Sc3 and Sc4) during the investigation irrespective of residue management. CA with FI system increased the water productivity (WPi) and its use efficiency (WUE) by ~52 and 12.3% (3-year mean), whereas SDI improved by 221.2 and 39.2% compared to farmers practice (Sc1; 0.69 kg grain m−3 and 21.39 kg grain ha−1 cm−1), respectively. Based on the 3-year mean, CA with SDI (mean of Sc5–Sc8) recorded −2.5% rice yield, whereas wheat yield was +25% compared to farmers practice (Sc1; 5.44 and 3.79 Mg ha−1) and rice and wheat yield under CA with flood irrigation were increased by +7 and + 11%, compared to their respective CT practices. Mung bean integration in Sc7 and Sc8 contributed to ~26% in crop productivity and profitability compared to farmers’ practice (Sc1) as SDI facilitated advancing the sowing time by 1 week. On a system basis, CA with SDI improved energy use efficiency (EUE) by ~70% and partial factor productivity of N by 18.4% compared to CT practices. In the RW system of NW India, CA with SDI for precise water and N management proved to be a profitable solution to address the problems of groundwater, residue burning, sustainable intensification, and input (water and energy) use with the potential for replication in large areas in NW India.

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Section: Fertilizer-n Nutrition Index and Efficiencymentioning

confidence: 99%

Bundling subsurface drip irrigation with no-till provides a window to integrate mung bean with intensive cereal systems for improving resource use efficiency

Kakraliya,

Jat,

Kumar

et al. 2024

Front. Sustain. Food Syst.

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“…In example, complementary sugar beet canopy temperature measurements with UAV infrared and Red, Green, and Blue (RGB) measurements were proved to improve the accuracy of UAV thermal measurements at altitudes below 40 m [52]. Another direct use of UAV thermal remote sensing can be given as nitrogen fertilization management with spatial clustering models [53,54]. Most of these applications are used to monitor crops, and the investigation of other types of vegetation within agricultural systems is needed.…”

Section: Introductionmentioning

confidence: 99%

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

et al. 2021

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The foundation of contemporary weed management practices in many parts of the world is glyphosate. However, dependency on the effectiveness of herbicide practices has led to overuse through continuous growth of crops resistant to a single mode of action. In order to provide a cost-effective weed management strategy that does not promote glyphosate-resistant weed biotypes, differences between resistant and susceptible biotypes have to be identified accurately in the field conditions. Unmanned Aerial Vehicle (UAV)-assisted thermal and multispectral remote sensing has potential for detecting biophysical characteristics of weed biotypes during the growing season, which includes distinguishing glyphosate-susceptible and glyphosate-resistant weed populations based on canopy temperature and deep learning driven weed identification algorithms. The objective of this study was to identify herbicide resistance after glyphosate application in true field conditions by analyzing the UAV-acquired thermal and multispectral response of kochia, waterhemp, redroot pigweed, and common ragweed. The data were processed in ArcGIS for raster classification as well as spectral comparison of glyphosate-resistant and glyphosate-susceptible weeds. The classification accuracy between the sensors and classification methods of maximum likelihood, random trees, and Support Vector Machine (SVM) were compared. The random trees classifier performed the best at 4 days after application (DAA) for kochia with 62.9% accuracy. The maximum likelihood classifier provided the highest performing result out of all classification methods with an accuracy of 75.2%. A commendable classification was made at 8 DAA where the random trees classifier attained an accuracy of 87.2%. However, thermal reflectance measurements as a predictor for glyphosate resistance within weed populations in field condition was unreliable due to its susceptibility to environmental conditions. Normalized Difference Vegetation Index (NDVI) and a composite reflectance of 842 nm, 705 nm, and 740 nm wavelength managed to provide better classification results than thermal in most cases.

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Investigating the impacts of different degrees of deficit irrigation and nitrogen interactions on assimilate translocation, yield, and resource use efficiencies in winter wheat

Zhuang,

Ata-UI-Karim,

Zhao

et al. 2024

Agricultural Water Management

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Recalibrating plant water status of winter wheat based on nitrogen nutrition index using thermal images

Cited by 6 publications

References 35 publications

Bundling subsurface drip irrigation with no-till provides a window to integrate mung bean with intensive cereal systems for improving resource use efficiency

Bundling subsurface drip irrigation with no-till provides a window to integrate mung bean with intensive cereal systems for improving resource use efficiency

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

Investigating the impacts of different degrees of deficit irrigation and nitrogen interactions on assimilate translocation, yield, and resource use efficiencies in winter wheat

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