Mapping of yield, economic return, soil electrical conductivity, and management zones of irrigated corn for silage

Bernardi, A. C. de C.; Tupy, O.; Santos, K. E. L.; Mazzuco, Giulia Guillen; Bettiol, G. M.; Rabello, L. M.; Inamasu, Ricardo Yassushi

doi:10.1590/s0100-204x2018001200001

Cited by 5 publications

(3 citation statements)

References 19 publications

(49 reference statements)

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“…Attempting to provide such guidance, since the beginnings of the commercialization of precision agriculture technology research on site-specific input management has looked extensively into creating management zones and cleaning specific data type. But those studies relied on uniform application rate data and not on field trial data (Bernardi et al 2018;Chen et al 2019;Ferguson et al 1996;Guastaferro et al 2010;King et al 2005;Kweon 2012;Velandia et al 2006). Dozens of published studies on the management of variable rate technology (VRT) have looked extensively into creating management zones and cleaning specific data types, but relied not on field trial data but instead on uniform application data (Bernardi et al 2018;Chen et al 2019;Ferguson et al 1996;Guastaferro et al 2010;King et al 2005;Kweon 2012;Velandia et al 2006).…”

Section: Summary Of Literaturementioning

confidence: 99%

“…But those studies relied on uniform application rate data and not on field trial data (Bernardi et al 2018;Chen et al 2019;Ferguson et al 1996;Guastaferro et al 2010;King et al 2005;Kweon 2012;Velandia et al 2006). Dozens of published studies on the management of variable rate technology (VRT) have looked extensively into creating management zones and cleaning specific data types, but relied not on field trial data but instead on uniform application data (Bernardi et al 2018;Chen et al 2019;Ferguson et al 1996;Guastaferro et al 2010;King et al 2005;Kweon 2012;Velandia et al 2006). More recently, Bullock et al (2020) emphasized that the value of VRT may come chiefly from the information gained from OFPEs about the uniform economically optimal nitrogen (EOIR) rather than through actual site-specific management.…”

Section: Summary Of Literaturementioning

confidence: 99%

See 1 more Smart Citation

Impact of Machinery Misalignment on Economic Results through Jensen’s Inequality in On-Farm Precision Experiments

Edge¹,

Mieno²,

Bullock³

2023

Preprint

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This simulation study identifies the economic impact of treatment mixing on the estimated optimal nitrogen recommendations (EONR) from on-farm research and evaluates a data processing procedure to remove treatment mixing and improve the EONR. Treatment mixing is when the combine harvests from more than one treatment area at a time; this can be caused by a parallel shift in the ab-line, different application and harvest headings, or machinery with incompatible widths. Due to the concavity of the yield response curve to nitrogen, Jensen’s Inequality implies that treatment mixing will result in observations lying below the true yield response curve and may affect the resulting EONR. We simulate the effects of the three causes of treatment mixing, varying degrees of these causes, trial rates centered on different areas of the response curve, and different curvatures of the yield-response function on the estimated optimal nitrogen rates. We find that the impact of treatment mixing depends on all of these factors and their interactions. The trial rates have a large impact on the estimated yield response function because they shift the dataset along the yield response function. For example, if the rates are in a steep part of the response function, the estimated response function may overestimate the response to nitrogen. However, without knowledge of the true yield response function or EONR, a researcher cannot determine what trial rates are best for a given OFPE. In general, parallel shift or incompatible machinery have the largest impact on the estimation of optimal nitrogen, particularly a shift of half the combine width or a harvester that is smaller than the applicator. These scenarios result in average profit losses of as much as $37 per hectare. We find overestimation of the EONR is common with harvest misalignment, introducing environmental and economic impacts and reducing nitrogen use efficiency. These results highlight the importance of reducing treatment mixing in OFPE through communication with farmers, trial design, and improved data processing procedures. For example, when machinery is relatively small, designing trials that are twice the width of the combine ensures that each trial plot will have a pass of the combine without treatment mixing even if there is a parallel shift during harvesting. As OFPE are increasingly implemented, these are common problems researchers will be facing. This research shows that the implications of decisions around treatment mixing impact NUE and profitability of OFPE recommendations; thus, working on a common protocol around treatment mixing is important for the future of OFPE.

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Section: Summary Of Literaturementioning

confidence: 99%

Section: Summary Of Literaturementioning

confidence: 99%

Impact of Machinery Misalignment on Economic Results through Jensen’s Inequality in On-Farm Precision Experiments

Edge¹,

Mieno²,

Bullock³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For soil property mapping, ordinary kriging (OK) and its extensions have been widely used in both temperate and tropical regions [17][18][19][20][21][22][23][24][25][26][27][28]. In southeast Brazil, where this study is conducted, OK was used to map ECa measured by a Veris 3100 sensor, as well as corn yield, cost, and profit in a 19-ha irrigated farm [29]. In another study in a 5.7 ha area, OK was compared to inverse distance weighting (IDW) for mapping soil penetration resistance (PR), bulk density (BD), and moisture, using two sampling grids [30].…”

Section: Introductionmentioning

confidence: 99%

Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

et al. 2020

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Mapping soil properties, using geostatistical methods in support of precision agriculture and related activities, requires a large number of samples. To reduce soil sampling and measurement time and cost, a combination of field proximal soil sensors was used to predict and map laboratory-measured soil properties in a 3.4-ha pasture field in southeastern Brazil. Sensor soil properties were measured in situ on a 10 × 10-m dense grid (377 samples) using apparent electrical conductivity meters, apparent magnetic susceptibility meter, gamma-ray spectrometer, water content reflectometer, cone penetrometer, and portable X-ray fluorescence spectrometer (pXRF). Soil samples were collected on a 20 × 20-m thin grid (105 samples) and analyzed in the laboratory for organic C, sum of bases, cation exchange capacity, clay content, soil volumetric moisture, and bulk density. Another 25 samples collected throughout the area were also analyzed for the same soil properties and used for independent validation of models and maps. To test whether the combination of sensors enhances soil property predictions, stepwise multiple linear regression (MLR) models of the laboratory soil properties were derived using individual sensor covariate data versus combined sensor data—except for the pXRF data, which were evaluated separately. Then, to test whether a denser grid sample boosted by sensor-based soil property predictions enhances soil property maps, ordinary kriging of the laboratory-measured soil properties from the thin grid was compared to ordinary kriging of the sensor-based predictions from the dense grid, and ordinary cokriging of the laboratory properties aided by sensor covariate data. The combination of multiple soil sensors improved the MLR predictions for all soil properties relative to single sensors. The pXRF data produced the best MLR predictions for organic C content, clay content, and bulk density, standing out as the best single sensor for soil property prediction, whereas the other sensors combined outperformed the pXRF sensor for the sum of bases, cation exchange capacity, and soil volumetric moisture, based on independent validation. Ordinary kriging of sensor-based predictions outperformed the other interpolation approaches for all soil properties, except organic C content, based on validation results. Thus, combining soil sensors, and using sensor-based soil property predictions to increase the sample size and spatial coverage, leads to more detailed and accurate soil property maps.

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Monitoring soil–plant interactions and maize yield by satellite vegetation indexes, soil electrical conductivity and management zones

et al. 2023

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Mapping of yield, economic return, soil electrical conductivity, and management zones of irrigated corn for silage

Cited by 5 publications

References 19 publications

Impact of Machinery Misalignment on Economic Results through Jensen’s Inequality in On-Farm Precision Experiments

Impact of Machinery Misalignment on Economic Results through Jensen’s Inequality in On-Farm Precision Experiments

Field Proximal Soil Sensor Fusion for Improving High-Resolution Soil Property Maps

Monitoring soil–plant interactions and maize yield by satellite vegetation indexes, soil electrical conductivity and management zones

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