Spatial estimation methods for mapping corn silage and grain yield monitor data

Cho, Jason B.; Guinness, Joseph; Kharel, Tulsi P.; Sunoj, S.; Kharel, Dilip; Oware, E. K.; Aardt, Jan van; Ketterings, Quirine M.

doi:10.1007/s11119-021-09793-z

Cited by 15 publications

(13 citation statements)

References 43 publications

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“…This enables documentation of spatial variability caused by a variety of different reasons, including non-uniform distribution of soil properties, soil moisture, pest pressure, rooting depth, and other factors [13]. For example, recent studies of corn (Zea mays L.) fields in New York have shown large variability in yield within fields [14,15]. The existence of such variability is well-recognized by both farmers and scientists [16].…”

Section: Figurementioning

confidence: 99%

“…Temporal yield variabilityheterogeneity in yield across multiple years at the field-level-can be caused by a variety of factors, including weather, management, and topography [29][30][31][32]. With availability of yield data from past years, both spatial and temporal yield variability over time can be assessed [14,15]. The main challenge remains whether we can design a statistically sound approach to treatment evaluation on farms, by farmers, given access to high resolution yield data, as well as to quantify both spatial and temporal yield variability.…”

Section: Figurementioning

confidence: 99%

“…Yield stability-based management zones, described in Kharel et al [15], were delineated for each field with data through the year prior to strip implementation. Cleaned historic yield monitor data were interpolated using kriging with the Matérn covariance function with 2 × 2 m resolution [14]. Temporal average yield and standard deviation of yield were determined at the farm-level for farm-specific zone delineation [15].…”

Section: Zone Delineationmentioning

confidence: 99%

“…where X is the design matrix containing data of the independent variables, y is a vector of the response, and Σ is the covariance matrix modeling the spatial dependence structure among data points. Isotropy, uniformity of variances in all directions, was assumed, and the Matérn covariance function was used to model spatial covariance [14]. The Matérn covariance function is parametrized as:…”

Section: Statistical Modellingmentioning

confidence: 99%

“…The nugget value σ 2 τ 2 is added to the diagonal of the covariance matrix. Γ is a gamma function, and K ν is the modified Bessel function of the second kind [14]. The parameters for the covariance function were estimated through maximum likelihood estimation using the GpGp package [39]:…”

Section: Statistical Modellingmentioning

confidence: 99%

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Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials

et al. 2021

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On-farm experimentation (OFE) allows farmers to improve crop management over time. The randomized complete blocks design (RCBD) with field-length strips as individual plots is commonly used, but it requires advanced planning and has limited statistical power when only three to four replications are implemented. Harvester-mounted yield monitor systems generate high resolution data (1-s intervals), allowing for development of more meaningful, easily implementable OFE designs. Here we explored statistical frameworks to quantify the effect of a single treatment strip using georeferenced yield monitor data and yield stability-based management zones. Nitrogen-rich single treatment strips per field were implemented in 2018 and 2019 on three fields each on two farms in central New York. Least squares and generalized least squares approaches were evaluated for estimating treatment effects (assuming independence) versus spatial covariance for estimating standard errors. The analysis showed that estimates of treatment effects using the generalized least squares approach are unstable due to over-emphasis on certain data points, while assuming independence leads to underestimation of standard errors. We concluded that the least squares approach should be used to estimate treatment effects, while spatial covariance should be assumed when estimating standard errors for evaluation of zone-based treatment effects using the single-strip spatial evaluation approach.

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Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Zone Delineationmentioning

confidence: 99%

Section: Statistical Modellingmentioning

confidence: 99%

Section: Statistical Modellingmentioning

confidence: 99%

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Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials

et al. 2021

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Artificial intelligence and satellite‐based remote sensing can be used to predict soybean (Glycine max) yield

Joshi,

Clay,

Sharma

et al. 2023

Agronomy Journal

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Because the manual counting of soybean (Glycine max) plants, pods, and seeds/pods is unsuitable for soybean yield predictions, alternative methods are desired. Therefore, the objective was to determine if satellite remote sensing−based artificial intelligence (AI) models could be used to predict soybean yield. In the study, multiple remote sensing−based AI models were developed for soybean growth stage ranging from VE/VC (plant emergence) to R6/R7 (full seed to beginning maturity). The ability of the Deep Neural Network (DNN), Support Vector Machine (SVM), Random Forest (RF), Least Absolute Shrinkage and Selection Operator (LASSO), and AdaBoost to predict soybean yield, based on blue, green, red, and near infrared reflectance data collected by the PlanetScope satellite at 6 growth stages, was determined. Remote sensing and soybean yield monitor data from 3 different fields in two years (2019 and 2021) were aggregated into 24,282 grid cells that had the dimensions of 10 by 10m. A comparison across models showed that the DNN outperformed the other models. Moreover, as crops matured from VE/VC to R4/R5, the R2 value of the models increased from 0.26 to over 0.70. These findings indicate that remote sensing data collected at different growth stages can be combined for soybean yield predictions. Moreover, additional work needs to be conducted to assess the model's ability to predict soybean yield with vegetation indices (VI) data for fields not used to train the model.This article is protected by copyright. All rights reserved

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Single‐Strip Spatial Evaluation Approach for Easier, More Meaningful On‐Farm Research

Srinivasagan,

Tanchez,

Marcaida

et al. 2023

Crops & Soils

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On‐farm research helps farmers make more informed management decisions in their operations for increased productivity and sustainability. While conventional on‐farm research designs are scientifically sound, the power to determine significant differences among various treatments is limited by the number of replications that can be put in place (more replications increase the chance to detect differences). In addition, major barriers to adoption of on‐farm research have been the space (larger fields) and the time required to conduct trials during planting and harvest. As these logistical and potentially economic challenges might discourage farmers from engaging in on‐farm research and be part of on‐farm research networks, a new way to conduct trials was developed that makes use of yield monitor technology and advances in spatial statistics. Earn 0.5 CEUs in Crop Management by reading this article and taking the quiz at https://web.sciencesocieties.org/Learning-Center/Courses.

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Spatial estimation methods for mapping corn silage and grain yield monitor data

Cited by 15 publications

References 43 publications

Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials

Proposed Method for Statistical Analysis of On-Farm Single Strip Treatment Trials

Artificial intelligence and satellite‐based remote sensing can be used to predict soybean (Glycine max) yield

Single‐Strip Spatial Evaluation Approach for Easier, More Meaningful On‐Farm Research

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