Examples of strategies to analyze spatial and temporal yield variability using crop models

Batchelor, William D.; Basso, Bruno; Paz, Joel O.

doi:10.1016/s1161-0301(02)00101-6

Cited by 189 publications

(127 citation statements)

References 27 publications

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“…A two-factorial field trial was laid out in a common randomized split-plot design on 27 May 2013, with the corn cultivar "NK Ravello". Four sowing densities (8)(9)(10)(11) seeds·m −2 ) were tested at four different levels of nitrogen fertilization (50, 100, 150 and 200 kg·N·ha −1 ) in a setup with four replicates.…”

Section: Methodsmentioning

confidence: 99%

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

2014

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Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three early-to mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients R 2 of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04 m·px −1 ). Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction.

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Section: Methodsmentioning

confidence: 99%

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

2014

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“…Process oriented crop simulation models integrate the effects on crop growth of multiple stress interactions over time and under different environmental and management conditions (Batchelor et al, 2002;Basso et al, 2013). However their application in PA can be limited because simulations cannot be performed everywhere in a field given that the availability of detailed (soil and crop data) inputs is limited and the costs are prohibitive.…”

Section: Introductionmentioning

confidence: 99%

Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone

Basso

Dumont

Cammarano

et al. 2016

Science of The Total Environment

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143

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Agronomic input and management practices have traditionally been applied uniformly on agricultural fields despite the presence of spatial variability of soil properties and landscape position. When spatial variability is ignored, uniform agronomic management can be both economically and environmentally inefficient. The objectives of this study were to: i) identify optimal N fertilizer rates using an integrated spatio-temporal analysis of yield and site-specific N rate response; ii) test the sensitivity of site specific N management to nitrate leaching in response to different N rates; and iii) demonstrate the environmental benefits of variable rate N fertilizer in a Nitrate Vulnerable Zone. This study was carried out on a 13.6 ha field near the Venice Lagoon, northeast Italy over four years (2005–2008). We utilized a validated crop simulation model to evaluate crop response to different N rates at specific zones in the field based on localized soil and landscape properties under rainfed conditions. The simulated rates were: 50 kg N ha− 1 applied at sowing for the entire study area and increasing fractions, ranging from 150 to 350 kg N ha− 1 applied at V6 stage. Based on the analysis of yield maps from previous harvests and soil electrical resistivity data, three management zones were defined. Two N rates were applied in each of these zones, one suggested by our simulation analysis and the other with uniform N fertilization as normally applied by the producer. N leaching was lower and net revenue was higher in the zones where variable rates of N were applied when compared to uniform N fertilization. This demonstrates the efficacy of using crop models to determine variable rates of N fertilization within a field and the application of variable rate N fertilizer to achieve higher profit and reduce nitrate leaching

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“…The calculations performed in crop models are based on the existing knowledge of the physics, physiology and ecology of crop responses to the environment (United States Department of Agriculture [USDA], 2007). There exists a large literature on crop models (Sirotenko, 2001;Batchelor, Basso, & Paz, 2002;Lobell & Burke, 2010), and details about their features will not be discussed here. A good overview on them, particularly on the DSSAT (Decision Support System for Agrotechnology Transfer) package, that incorporates models of different crops with software that facilitates the evaluation and application of crop models for different purposes, can be read in Jones et al (2003).…”

Section: Crop Modelsmentioning

confidence: 99%

What Can Be Learned about the Adaptation Process of Farming Systems to Climate Dynamics Using Crop Models?

Schlindwein¹,

Eulenstein²,

Lana³

et al. 2015

SAR

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The objective of this paper is to reflect and discuss how the use of crop models by aware practitioners might trigger learning of how to think and act differently about the adaptation process of farming systems to climate dynamics. The development of adaptation strategies is discussed from the perspective of contrasting metaphors, since the metaphors in use have distinctive practical implications regarding how crop models might be used for adaptation purposes. Further, in this paper it is pointed out that adaptation should be understood as the result of a learning process and therefore the use of crop models for adaptation purposes must be transformed. Instead of seeing them only as tools to secure yield of cropping systems under a changing climate they must be conceived as components of learning systems for adaptation of whole farming systems.

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Examples of strategies to analyze spatial and temporal yield variability using crop models

Cited by 189 publications

References 27 publications

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

Environmental and economic benefits of variable rate nitrogen fertilization in a nitrate vulnerable zone

What Can Be Learned about the Adaptation Process of Farming Systems to Climate Dynamics Using Crop Models?

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