Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton?

Bronson, K. F.; Conley, Matthew M.; French, Andrew N.; Hunsaker, Douglas J.; Thorp, Kelly R.; Barnes, Edward M.

doi:10.1002/agj2.20120

Cited by 10 publications

(11 citation statements)

References 34 publications

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“…Ultrasonic-sensed cotton canopy heights showed the expected curvilinear growth pattern during the season with the greatest growth rates between first square and mid bloom (Figure 2). Plant heights did not reach a plateau in the second half of the season, as Bronson et al (2020) observed in these same plots with NDVI at mid bloom. Statistical differences between N-fertilized and zero-N plots were observed early in all three seasons at mid-squaring in 2016 and in 2017, and at first square in 2018 (Figure 2).…”

Section: Resultssupporting

confidence: 74%

Use of an ultrasonic sensor for plant height estimation in irrigated cotton

et al. 2021

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Plant height in field crops like cotton (Gossypium hirsutum L.) have long been of great interest to agronomists and plant breeders. Canopy height sensors can replace laborious point hand sampling and enable site-specific management. Study objectives were to compare manual plant height measurements with ultrasonic distance sensor measurements in subsurface drip-irrigated cotton, and to assess the effects of nitrogen and water management. Two Honeywell 943 sensors were used weekly to estimate canopy height in a nitrogen-and water-management study in Maricopa, AZ from 2016 to 2018. Hand measurements of plant height-ultrasonic-sensed height correlation increased with growth stage with r 2 of .92 to .99 at mid bloom. Root mean square errors ranged from 0.9 to 4.0 cm. Canopy height was strongly affected by nitrogen and water treatments, starting at first square. This rapid-response ultrasonic distance sensor is accurate and has great potential as a canopy height measuring instrument in cotton.

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Section: Resultssupporting

confidence: 74%

Use of an ultrasonic sensor for plant height estimation in irrigated cotton

et al. 2021

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“…Petiole NO 3 concentration sampling can then be used to complement the soil test approach as an in-season assessment of N status for in-season N adjustments and to guide the timing of N applications. In addition to petiole sampling, in-season N status can be done with proximal sensing using active optical sensors (Bronson et al, 2019(Bronson et al, , 2020. We can compare the dates when N deficiency in petiole NO 3 in zero-N or reduced-N reflectance-based N management plots were detected in the Maricopa studies with the dates when published vegetation indices, calculated from active optical sensor-measured canopy reflectance detected N deficiency.…”

Section: Discussionmentioning

confidence: 99%

“…We can compare the dates when N deficiency in petiole NO 3 in zero-N or reduced-N reflectance-based N management plots were detected in the Maricopa studies with the dates when published vegetation indices, calculated from active optical sensor-measured canopy reflectance detected N deficiency. In many cases, petiole NO 3 indicated N deficiency 1-4 wk before the active optical sensors did (Bronson et al, 2019(Bronson et al, , 2020. For instance, with OSI in 2014, petiole NO 3 levels in zero-N plots were less than soil test plots 54 DAP, whereas NDRE showed this difference 82 DAP (Day of Year 203) (Bronson et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…In many cases, petiole NO 3 indicated N deficiency 1-4 wk before the active optical sensors did (Bronson et al, 2019(Bronson et al, , 2020. For instance, with OSI in 2014, petiole NO 3 levels in zero-N plots were less than soil test plots 54 DAP, whereas NDRE showed this difference 82 DAP (Day of Year 203) (Bronson et al, 2020). In SDI in 2018, petiole NO 3 levels in reduced-N reflectance plots were less than soil test plots (both 100% irrigation) 51 DAP, whereas NDRE showed this difference 84 DAP (Day of Year 176).…”

Section: Discussionmentioning

confidence: 99%

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Revising petiole nitrate sufficiency/deficiency guidelines for irrigated cotton in the Desert Southwest

Bronson

Norton

Silvertooth

2021

Soil Science Soc of Amer J

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Petiole nitrate-nitrogen (NO 3 -N) sampling and testing remains a popular in-season nitrogen (N) management practice in the western United States for cotton (Gossypium hirsutum L.). However, the present guidelines used by Arizona are greater than 35 yr old and are in need of updating. The objectives of this study were to relate in-season petiole NO 3 levels with lint yields and N deficiencies and to revise the former critical deficiency level guidelines by growth stage. Petioles were sampled between first square and peak bloom in nine site-years of cotton N management field trials in Maricopa and Safford, AZ, from 2014 to 2018. Irrigation type in Maricopa was overhead sprinkler irrigation (OSI) (2014)(2015) and subsurface drip irrigation (SDI) (2016)(2017)(2018). In Safford (2014-2017), surface irrigation (SI) was used. Petiole NO 3 in SDI was dramatically lower than with SI or OSI, mostly in the deficient range. Lower lint yields in zero-N treatments compared with pre-plant soil NO 3 test-based reference treatments occurred in eight site-years (Safford 2016 crop lost to late hailstorm) and were considered N deficient. Critical petiole NO 3 -N levels from 1984 were revised downward 1 g N kg −1 because several N-fertilized treatment means of petiole NO 3 were in that range and did not exhibit an N rate-related yield depression. Sampling cotton plants for petiole NO 3 analysis should start within 1 wk of first bloom. Petiole NO 3 dynamics and critical levels in SDI cotton required further study.

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“…Red-edge based vegetation indices are also less prone to saturation and have provided stronger relationships with lint yield compared with red or green-based vegetation indices in previous studies (Ballester et al, 2017;Bronson et al, 2020;Raper & Varco, 2015). However, Landsat 7-8 (used F I G U R E 6 Observed cotton lint yields and predicted yield maps for Fields 1 and 2 at Mungindi (New South Wales) using the gradient boosting machine (GBM) model at the flowering stage and the random forests (RF) model at the boll-fill stage using all variables in the current study) was not equipped with red-edge and was the only publicly available multispectral satellite imagery available during all seasons and at a within-field resolution.…”

Section: Model Performancementioning

confidence: 94%

Predicting within‐field cotton yields using publicly available datasets and machine learning

2021

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Early detection of within-field yield variability for high-value commodity crops, such as cotton (Gossypium spp.), offers growers potential to improve decision-making, optimize yields, and increase profits. Over recent years, publicly available datasets have become increasingly available and at a resolution where within-field yield prediction is possible. However, the viability of using these datasets with machine learning to predict within-field cotton lint yield at key growth stages are largely unknown. This study was conducted on two cotton fields, located near Mungindi, New South Wales, Australia. Three years of yield data, soil, elevation, rainfall, and Landsat imagery were collected from each field. A total of 12 models were created using: (a) two machine learning algorithms: random forest (RF) and gradient boosting machines (GBM); (b) three growth stages: squaring, flowering, and boll-fill; and(c) two different amounts of variables: all variables and the optimal variables determined by a recursive feature elimination (RFE). Results showed a strong agreement between predicted and observed yields at flowering and boll-fill when more information was available. At flowering and boll-fill, root mean square error (RMSE) ranged between 0.15 and 0.20 t ha −1 and Lin's concordance correlation coefficient (LCCC) ranged between 0.50 and 0.66, with RF providing superior results in most cases. Models created using the optimal variables determined by the RFE provided similar results compared to using all variables, allowing greater model accuracy and resolution with targeted sampling. Overall, these findings indicate significant potential of publicly available datasets to predict within-field cotton yield and guide decisionmaking in-season.

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Which active optical sensor vegetation index is best for nitrogen assessment in irrigated cotton?

Cited by 10 publications

References 34 publications

Use of an ultrasonic sensor for plant height estimation in irrigated cotton

Use of an ultrasonic sensor for plant height estimation in irrigated cotton

Revising petiole nitrate sufficiency/deficiency guidelines for irrigated cotton in the Desert Southwest

Predicting within‐field cotton yields using publicly available datasets and machine learning

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