Using Canopy Indices to Quantify the Economic Optimum Nitrogen Rate in Spring Wheat

Calvo, Nahuel Ignacio Reussi; Rozas, Hernán Saínz; Echeverría, Hernán Eduardo; Diovisalvi, Natalia Verónica

doi:10.2134/agronj14.0392

Cited by 13 publications

(5 citation statements)

References 31 publications

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“…This study found that rice canopy NDVI values had a similar tendency during the entire growth period, and both achieved maximal NDVI values at the booting stage. In addition, researchers conducted several experiments on maize, rice and wheat, and found that the spectral index can be used to monitor crops’ main growth indices [39,40,41,42,43,44]. This confirmed, to some extent, the stable relationships of the canopy NDVI with LAI, above-ground DM and GY performed at key growth stages (Figure 2).…”

Section: Discussionmentioning

confidence: 60%

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

Liu

Ferguson

Zheng

et al. 2017

Sensors

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The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI=(1+e−15.2829×(RAGDDi−0.1944))−1−(1+e−11.6517×(RAGDDi−1.0267))−1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status.

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Section: Discussionmentioning

confidence: 60%

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

Liu

Ferguson

Zheng

et al. 2017

Sensors

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“…Furthermore, Mullen et al [80] showed that in-season N demand for added N fertilizer in winter wheat could be detected using NDVI readings collected at GS30. Moreover, algorithms using crop canopy reflectance sensing to make N recommendation for wheat have been identified, and it has been shown that active canopy sensors could be used in determining variable N rate applications in wheat from the mid-growing season [81,82]. Marti et al [28] found significant correlations between the NDVI, yield and biomass in wheat.…”

Section: Proximal Sensing Tools and Vegetation Indicesmentioning

confidence: 99%

Crop Sensor-Based In-Season Nitrogen Management of Wheat with Manure Application

2019

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It is difficult to predict the crop-available nitrogen (N) from farmyard manures applied to soil. The aim of this study was to assess the usefulness of the proximal sensors, Yara N-TesterTM and RapidScan CS-45, for diagnosing the N nutritional status of wheat after the application of manures at sowing. Three annual field trials were established (2014–2015, 2015–2016 and 2016–2017) with three types of fertilizer treatments: dairy slurry (40 t ha−1 before sowing), sheep manure (40 t ha−1 before sowing) and conventional treatment (40 kg N ha−1 at tillering). For each treatment, five different mineral N fertilization doses were applied at stem elongation: 0, 40, 80, 120, and 160 kg N ha−1. The proximal sensing tools were used at stem elongation before the application of mineral N. Normalized values of the proximal sensing look promising for adjusting mineral N application rates at stem elongation. For dairy slurry, when either proximal sensor readings were 60–65% of the reference plants with non-limiting N, the optimum N rate for maximizing yield was 118–128 kg N ha−1. When the readings were 85–90%, the optimum N rate dropped to 100–110 kg N ha−1 for both dairy slurry and conventional treatments. It was difficult to find a clear relationship between sensor readings and yield for sheep manure treatments. Measurements taken with RapidScan C-45 were less time consuming and better represent the spatial variation, as they are taken on the plant canopy. Routine measurements throughout the growing season are particularly needed in climates with variable rainfall. The application of 40 kg N ha−1 at the end of winter is necessary to ensure an optimal N status from the beginning of wheat crop development. These research findings could be used in applicator-mounted sensors to make variable-rate N applications.

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“…To analyze the economic profit of N fertilization, the EONR approach previously used in other crops such as maize (Alotaibi et al., 2018; Barker & Sawyer, 2010; Puntel et al., 2019) and wheat ( Triticum aestivum L.; Reussi Calvo et al., 2015) was followed. Two scenarios of grain prices were considered: (a) a historical (last 30‐yr mean) fertilizer to grain cost ratio of 6:1 for feed barley (i.e., when malting barley quality requirements are not met) and (b) a historical (last 30‐yr mean) fertilizer to grain cost ratio of 4:1 for malting barley (MAGYP, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…In sites without N response, the EONRf and EONRm were zero. Within each site, the differential from the EONRf (dEONRf) and EONRm (dEONRm) were calculated as the N rate minus the EONRf or EONRm, respectively (Barker & Sawyer, 2010; Reussi Calvo et al., 2015).…”

Section: Methodsmentioning

confidence: 99%

Economic optimum nitrogen rate analysis for feed and malting barley

et al. 2022

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The malt industry has specific requirements for malting barley (Hordeum vulgare L.) grain protein concentration (from 9.5 to 13%) and size (>85%), which could be affected by nitrogen (N) fertilization. We aimed to (a) define and explain the economic optimum nitrogen rate (EONR) for grain yield in feed (EONRf) and malting barley (EONRm), and (b) analyze if grain quality requirements are reached at the EONRm. We evaluated six N rates (0, 40, 80, 120, 160, and 200 kg N ha −1 ) in field experiments (14 site-years). At each site-year, the EONR was calculated using two fertilizer cost to grain price ratios, 6:1 for feed and 4:1 for malting barley. On average, maximum yield and protein response to N were 1,681 kg ha −1 and 2%, respectively. There was no consistent effect of N fertilization on grain size. The EONRf was explained by N availability and maximum yield response to N (y = 130 -0.37N availability + 0.03N response, r 2 = .89), and EONRm was explained solely by N availability (y = 320 -0.93N availability, r 2 = .75). The overall EONRm was 134 kg N ha −1 . At sites with low soil N availability and high yield response to N, an average N rate of 69 kg ha −1 greater than the EONRm was necessary to reach 9.5% protein.Otherwise, the EONRf could be used (106 kg N ha −1 ) and grains be sold as feed barley. These results are useful to adjust N fertilizer recommendations to satisfy the malting industry demand while reducing the economic and environmental impacts of fertilization.Abbreviations: dEONRf, differential from the economic optimum N rate for feed barley; dEONRm, differential from the economic optimum N rate for malting barley; EONR, economic optimum N rate; EONRm, economic optimum N rate conditioned to malting quality requirements; GP, grain protein; GS, grain size; GY, grain yield; Nan, nitrogen mineralized in short-term anaerobic incubation; OM, organic matter.

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Using Canopy Indices to Quantify the Economic Optimum Nitrogen Rate in Spring Wheat

Cited by 13 publications

References 31 publications

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

Crop Sensor-Based In-Season Nitrogen Management of Wheat with Manure Application

Economic optimum nitrogen rate analysis for feed and malting barley

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