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Fleming, Kim; Westfall, D. G.; Wiens, D. W.; Brodahl, M. C.

doi:10.1023/a:1011481832064

Cited by 149 publications

(26 citation statements)

References 20 publications

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“…Th e increased use of precision-based technologies allows growers access to new research systems which can help them make current and future management decisions Fleming et al, 2000;Mueller et al, 2001;Bullock, 2002a,2002b;Kaspar et al, 2003;Kravchenko et al, 2005;Hornung et al, 2006). Th e infl uence of soil physical and chemical parameters on crop yields is relatively predictable (Miller et al, 1988;Sudduth et al, 1996;Moran et al, 1997;Machado et al, 2000) which make layers such as topsoil depth, primary nutrients, and soil texture potentially useful for yield data analysis.…”

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confidence: 99%

Identifying Field Attributes that Predict Soybean Yield Using Random Forest Analysis

et al. 2016

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Until recently, soybean [Glycine max (L.) Merr.] fi elds were oft en seeded at a single rate. Advances in GPS and variable rate technology (VRT) are allowing growers to use variable rate planting prescriptions to optimize yields and input costs. Th is study was conducted to fi nd the key predictors for characterizing soybean seed yield from commonly collected precision agriculture data layers. Research was conducted in 11 unique fi elds both in 2013 and 2014 in Wisconsin and all 22 site-years were following corn [Zea mays (L.)].

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confidence: 99%

Identifying Field Attributes that Predict Soybean Yield Using Random Forest Analysis

et al. 2016

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“…Soil organic carbon (SOS) has a considerable effect on the interactions between soil and plants. The SOC content is closely related to soil quality (Reeves 1997;Susanne and Michelle 1998;Al-Kaisi et al 2005;Huang et al 2007), not only as an indicator of soil erosion and degradation (Dick 1992), but also as a regulating factor of processes such as water holding capacity and permeability (Pepper 1996), bioavailability and fate of many herbicides (Williams and Mortensen 2000;Graff et al 2000), pesticide behavior in soil (Patzold et al 2008), plant-available N (Marschner et al 2003, the soil's ability to adsorb plant nutrients (Shatar and McBratney 1999) and crop yield (Fleming et al 2000;Khakural et al 1999). Knowing its concentration in the soil could be useful, especially if its spatial distribution could be determined accurately (Blackmer and White 1998) and at a low cost (Wolf and Buttel 1996;Lu et al 1997).…”

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confidence: 99%

Estimating soil organic carbon from soil reflectance: a review

et al. 2009

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Soil organic carbon (SOC) concentration is a useful soil property with which to guide agricultural applications of chemical inputs. To enable this, simple, accurate, rapid and inexpensive methods are needed to produce maps of surface SOC concentrations. Researchers have investigated estimates of soil surface properties from remotely sensed information as a means of rapidly quantifying and monitoring some surface soil properties, such as SOC. The objective of this paper is to review the potential and limitations of remotely sensed data for mapping and evaluating SOC. Several statistical methods including simple regression models, the 'soil line' approach, principal component analysis and geostatistics have been applied to data to investigate the accuracy of such estimates. A review of the literature shows that predictive equations are not universal and require new regression models for every scene. An important benefit of remotely sensed data is to suggest a sampling strategy that can lead to improved representation of spatial heterogeneity in SOC.

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“…Compared to the sensor-based approach, which uses expensive sensors, management zone maps are more economical (Zhang et al, 2002). The development of VRA maps has been widely based on laboratory measurements of spectral reflectance of soil or crops (Daniel et al, 2004), or hand-held device measurements (Liakos, 2013), aerial photography images (Fleming et al, 2000;Zaman & Schuman, 2006) and satellite observations (Li et al, 2008;Guo et al, 2012). Spatial variability in yields has also been considered as a useful indicator to determine variable rate nutrient management (Johnson et al, 2003).…”

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Nitrogen replenishment using variable rate application technique in a small hand-harvested pear orchard

Vatsanidou

Nanos

Fountas

et al. 2018

Span. j. agric. res.

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Precision agriculture is a management approach for sustainable agriculture. It can be applied even in small fields. It aims to optimize inputs, improve profits, and reduce adverse environmental impacts. In this study, a series of measurements were conducted over three growing seasons to assess variability in a 0.55 ha pear orchard located in central Greece. Soil ECa was measured using EM38 sensor, while soil samples were taken from a grid 17 × 8 m and analysed for texture, pH, P, K, Mg, CaCO3, and organic matter content. Data analysis indicated that most of the nutrients were at sufficient levels. Soil and yield maps showed considerable variability while fruit quality presented small variations across the orchard. Yield fluctuations were observed, possibly due to climatic conditions. Prescription maps were developed for nitrogen variable rate application (VRA) for two years based on the replacement of the nutrients removed by the crop. VRA application resulted in 56% and 50% reduction of N fertiliser compared to uniform application.

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Untitled

Cited by 149 publications

References 20 publications

Identifying Field Attributes that Predict Soybean Yield Using Random Forest Analysis

Identifying Field Attributes that Predict Soybean Yield Using Random Forest Analysis

Estimating soil organic carbon from soil reflectance: a review

Nitrogen replenishment using variable rate application technique in a small hand-harvested pear orchard

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