Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions

Naser, Mohammed A.; Khosla, Raj; Longchamps, Louis; Dahal, Subash

doi:10.3390/rs12050824

Cited by 58 publications

(52 citation statements)

References 48 publications

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“…Additionally, chlorophyll content is a function of leaf N content and provides an indirect estimate of plant nitrogen status. Normalized Difference Vegetative Index (NDVI) is a widely used vegetative index and has been shown to correlate well with leaf N status and final grain yield [38][39][40]. Both the SSMZ and proximal sensing approaches aim to increase NUE and yield by determining the optimal rate of N to apply.…”

Section: Introductionmentioning

confidence: 99%

Variable Rate Nitrogen and Water Management for Irrigated Maize in the Western US

et al. 2020

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Nitrogen (N) and water continue to be the most limiting factors for profitable maize (Zea Mays L.) production in the western US Great Plains. Precision application of N and water has the potential to significantly enhance input use efficiency without impairing yields. The overall objective of this study was to determine the most productive and efficient nitrogen and water management strategy for irrigated maize by using site-specific management zones and a proximal remote sensing approach. This study was conducted over 2016, 2017, 2018 and 2019 crop growing seasons near Fort Collins, Colorado, USA. Six nitrogen rates (0, 56, 112, 168, 224, and 280 kg N ha−1) were applied along experimental strips across three delineated management zones (low, medium, and high productivity). Four rates of irrigation were applied to maize (60%, 80%, 100%, and 120% of evapotranspiration) using a center pivot precision irrigation system equipped with zone control. Optical proximal sensor readings were acquired on all experimental strips four times during the growing season to assess four nitrogen management strategies (uniform, management zone (MZ), remote sensing (RS), and management zone remote sensing (MZRS)) on grain yield and nitrogen use efficiency (NUE). Results from this three-year study showed the significant interaction (p = 0.05) of zone vs. irrigation and irrigation vs. nitrogen across all years. In two of the three years (2016 and 2018), the high productivity zone benefitted from high irrigation rates, demonstrated by 16% and 18% yield increase from the lowest irrigation rate. In 2016, yield plateau was reached at 168 kg N ha−1 with 80% and 100% irrigation rates, whereas the plateau was reached at 112 kg N ha−1 in the 120% irrigation rate. These results demonstrate the possibility of fine-tuning zones, irrigation, and nitrogen to achieve optimum yield. While uniform and MZ nitrogen management strategies produced the highest grain yield, the best NUE was achieved via the RS strategy, followed by the MZ strategy. In this study, the MZRS strategy (combined MZ and RS) did not produce superior yield and NUE as compared to uniform and other strategies. However, there is a tremendous opportunity to fine-tune these two strategies, using other algorithms that are not explored in this study to improve the sustainability of maize production under irrigated conditions.

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Section: Introductionmentioning

confidence: 99%

Variable Rate Nitrogen and Water Management for Irrigated Maize in the Western US

et al. 2020

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“…In the past 20 years, several studies have been carried out on the use of precision farming technologies to evaluate crops' agronomic traits, as well as stress detection and quantification. Naser et al [4] used proximal sensor derived Normalized Difference Vegetation Index (NDVI) to discriminate productivity between different wheat genotypes. Randelović et al [5] used a machine learning model and vegetation indices extracted by a Unmanned Aerial Vehicles (UAV) device to predict soybean plant density.…”

Section: Introductionmentioning

confidence: 99%

A Sustainability Assessment of the Greenseeker N Management Tool: A Lysimetric Experiment on Barley

et al. 2020

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A preliminary study was conducted to analyze the sustainability of barley production through: (i) investigating sensor-based nitrogen (N) application on barley performance, compared with conventional N management (CT); (ii) assessing the potential of the Normalized Difference Vegetation Index (NDVI) at different growth stages for within-season predictions of crop parameters; and (iii) evaluating sensor-based fertilization benefits in the form of greenhouse gasses mitigation. Barley was grown under CT, sensor-based management (RF) and with no N fertilization (Control). NDVI measurements and RF fertilization were performed using a GreenSeeker™ 505 hand-held optical sensor. Gas emissions were measured using a static chamber method with a portable gas analyzer. Results showed that barley yield was not statistically different under RF and CF, while they both differed significantly from Control. Highly significant positive correlations were observed between NDVI and production parameters at harvesting from the middle of stem elongation to the medium milk stage across treatments. Our findings suggest that RF is able to decrease CO2 emission in comparison with CF. The relationship between N fertilization and CH4 emission showed high variability. These preliminary results provide an indication of the benefits achieved using a simple proximal sensing methodology to support N fertilization.

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“…The irrigated conditions produced larger amounts of biomass and thus more chlorophyll content, which led to NDVI saturation early in the season and a low R 2 for both years. Additionally, the lower R 2 was possibly related to differences in the crop growth conditions and the timing, rate and distribution of precipitation during the growing season [40]. In this context, a higher R 2 would be expected in the earlier growth stages, before crop canopy closure.…”

Section: Characterization Of Nue Variability Across Wheat Genotypes Umentioning

confidence: 99%

“…This is consistent with the high variability of the NDVI observed in dryland conditions as compared to the variability in the NDVI observed in irrigated conditions. More details on NDVI variability are available in Naser et al [40]. Because of the lower variability in the NDVI, the model predicting PFP and PNB based on NDVI values showed better performance in dryland than in irrigated conditions.…”

Section: Characterization Of Nue Variability Across Wheat Genotypes Umentioning

confidence: 99%

“…A recent publication [40] reported that the early assessment of wheat grain production using a field-based NDVI is possible at early growth stages (r = 0.89) for the purposes of plant breeding and precision nitrogen fertilization. Kefauver et al [41] reported the use of vegetation indices to assess NUE in Barley, and they were able to explain up to 83% of the total variability in NUE using a combination of UAV and field-based measurements.…”

Section: Of 16mentioning

confidence: 99%

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Characterizing Variation in Nitrogen Use Efficiency in Wheat Genotypes Using Proximal Canopy Sensing for Sustainable Wheat Production

et al. 2020

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Global nitrogen use efficiency (NUE) for cereal production is marginal and is estimated to be about 33%. Remote sensing tools have tremendous potential for improving NUE in crops through efficient nitrogen management as well as the identification of high-NUE genotypes. The objectives of this study were (i) to identify and quantify the variation in NUE across 24 winter wheat genotypes (Triticum aestivum L.) and (ii) to determine if the normalized difference vegetation index (NDVI) could characterize the variability in NUE across wheat genotypes. This study was conducted in 2010 and 2011 in the semi-arid climate of Northeastern Colorado across dryland and irrigated conditions. Our results indicate significant variation in the NUE among genotypes across two irrigation conditions. We observed a strong relationship between the NDVI and NUE—as PFP (partial factor productivity) and PNB (partial nitrogen balance)—across the 24 wheat genotypes under dryland conditions (average R2 for PFP and PNB = 0.84) at Feekes growth stage 11.1, for site year II. However, poor association was observed under irrigated conditions (average R2 for PFP and PNB = 0.29) at Feekes growth stage 3 to 4 for site year II. This study demonstrates the potential and limitations of active canopy sensing to successfully characterize the variability in NUE across wheat genotypes.

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Using NDVI to Differentiate Wheat Genotypes Productivity Under Dryland and Irrigated Conditions

Cited by 58 publications

References 48 publications

Variable Rate Nitrogen and Water Management for Irrigated Maize in the Western US

Variable Rate Nitrogen and Water Management for Irrigated Maize in the Western US

A Sustainability Assessment of the Greenseeker N Management Tool: A Lysimetric Experiment on Barley

Characterizing Variation in Nitrogen Use Efficiency in Wheat Genotypes Using Proximal Canopy Sensing for Sustainable Wheat Production

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