Evaluation of Soil Survey Scale for Zone Development of Site‐Specific Nitrogen Management

Franzen, David W.; Hopkins, David; Sweeney, M. D.; Ulmer, Michael K.; Halvorson, Ardell D.

doi:10.2134/agronj2002.3810

Cited by 21 publications

(5 citation statements)

References 21 publications

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“…As a consequence, delineating zones within the field of similar crop response may provide the right amount of data to execute the model (Basso et al, 2007). Various authors have proposed criteria for the delineation of management zones (Mulla, 1991;Fleming et al, 2001;Ferguson et al, 2004;Schepers et al, 2004;Chang et al, 2004;Inman et al, 2005: Franzen et al, 2002Basso et al, 2009). The objective of this paper is to present a procedure that allows for the selection of optimal nitrogen fertilizer rates to be applied spatially on previously identified management zones through crop simulation modelling.…”

Section: Introductionmentioning

confidence: 99%

Criteria for Selecting Optimal Nitrogen Fertilizer Rates for Precision Agriculture

Basso¹,

Cammarano²,

Grace³

et al. 2009

Ital J Agronomy

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Yield rates vary spatially and maps produced by the yield monitor systems are evidence of the degree of withinfield variability. The magnitude of this variability is a good indication of the suitability of implementing a spatially variable management plan. Crop simulation models have the potential to integrate the effects of temporal and multiple stress interaction on crop growth under different environmental and management conditions. The strength of these models is their ability to account for stress by simulating the temporal interaction of stress on plant growth each day during the season. The objective of paper is to present a procedure that allows for the selection of optimal nitrogen fertilizer rates to be applied spatially on previously identified management zones through crop simulation modelling. The integration of yield maps, remote sensing imagery, ground truth measurements, electrical resistivity imaging allowed for the identifications of three distinct management zones based on their ability to produce yield and their stability over time (Basso et al., 2009). After validating the model, we simulated 7 N rates from 0 to 180 kg N/ha with a 30 kg N/ha increment. The model results illustrate the different N responses for each of the zone. The analysis allowed us to identify the optimal N rate for each of the zone based on agronomic, economic and environmental sustainability of N management

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Section: Introductionmentioning

confidence: 99%

Criteria for Selecting Optimal Nitrogen Fertilizer Rates for Precision Agriculture

Basso¹,

Cammarano²,

Grace³

et al. 2009

Ital J Agronomy

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“…Myriad approaches to MZ delineation have been developed in the last 25 years (Khosla et al., 2010). Some common attributes that have been used—either individually or in combination—for MZ delineation include soil apparent electrical conductivity (EC a ) (Fleming et al., 2004; Kitchen et al., 1999), yield maps (Flowers et al., 2005), aerial imagery (Schepers et al., 2004), topography (Fraisse et al., 2001), and soil survey maps (D. W. Franzen et al., 2002).…”

Section: Introductionmentioning

confidence: 99%

Evaluating management zones and crop‐sensing relationships for improved irrigated maize nitrogen management

Crowther¹,

Parrish

Luck

et al. 2023

Agrosystems Geosci & Env

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Active crop canopy sensors and management zones (MZ) are two methods of directing variable-rate, in-season nitrogen (N) fertilizer applications in maize (Zea mays L.). Researchers have suggested that integrating these two approaches may result in improved performance of sensor-based N application algorithms through increased N use efficiency and profitability. The objectives of this research study were to (1) identify soil and topographic variables that are related to in-season canopy reflectance and yield for soil-based MZ delineation and (2) determine if delineated MZ can identify areas with differential crop response to N fertilizer. N ramp blocks were placed end-to-end in field-length strips at eight irrigated maize fields in east central Nebraska in 2016 and 2017. Maize response to N was evaluated with inseason canopy reflectance measurements and grain yield. Relationships between maize response variables and measured soil and topographic attributes were evaluated and used to delineate MZ. Yield response to N rate was highly variable among and within fields. Soil apparent electrical conductivity had the highest overall correlations with crop response and was used as a clustering variable in five of eight fields.Economic analysis showed a potential advantage to using soil-based MZ compared to producer-chosen uniform N rates in five of eight fields. Delineated MZ were able to identify areas with differential soil properties and crop response to N fertilizer. Integrating soil-based MZ and sensor-based N management has potential to achieve further economic benefits.

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“…Information about the spatial variability of soil physicochemical properties and microbiological activity is very important in the selection of crops and cultivation systems, and also broadens ideas about prevailing management practices [4][5][6]. The spatial variability of pH, EC, organic matter NPK and available micronutrients has been studied by various researchers by comparing soils and their management systems to refine and implement site-specific nutrient management [7,8].…”

Section: Introductionmentioning

confidence: 99%

Spatial Variability in Soil Properties, Delineation Site-specific Management Division Based on Soil Fertility Using Fuzzy Clustering in Gwalior, Madhya Pradesh, India

Yadav¹,

Singh²,

Yadav³

et al. 2023

IJPSS

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Farmers who want to improve nitrogen usage efficiency (NUE) and crop yield must have access to information regarding the geographical variability and distribution of soil parameters. Fertilizer application relying on soil characteristic maps and fertilizers recommendations may help reduce fertilizer input without sacrificing crop production. The current research focused heavily on evaluating the variability of soil fertility status in the Madhya Pradesh Gwalior region using geostatistical techniques. To do this, 150 GPS-based surfaces (0–15 cm) soil samples were obtained from the Gwalior region's five districts (Gwalior, Shivpuri, Datia, Guna, & Ashok Nagar) during crop harvest during the rabi season of 2019–20. Statistics and geo-statistics were used to analyze the results of the laboratory analysis. The analysis revealed that the soil samples' pH, EC, SOC, and CaCO3 values, respectively, varied from 4.40 to 8.30, 0.09 to 1.03 dSm-1, 2.0 to 10.60 gkg-1, and 3.0 to 24.0 gkg-1. In contrast, the amounts of N, P, K, and S that are present in soil vary from 102.0 to 356.0 kg ha-1, 6.0 to 61.0 kg ha-1, 114.0 to 896.0 kg ha-1, and 5.90 to 49.20 mg kg-1, respectively. Through building semi-variograms and mapping the data utilizing standard kriging techniques, the information was studied using both traditional statistics and geostatistics. For soil properties, semi-variograms were created, and their geographical distributions were delineated. The Nugget/Sill (Co/Co+ C) ratio for the modeled variables revealed moderate to high spatial dependences. The best-fit models for the reported soil characteristics were exponential, spherical, and circular. The findings of this research demonstrated that soil fertility quality varied significantly across the Gwalior area. This knowledge may aid in making choices about crop succession and the usage of plant nutrients to increase farmers' financial returns.

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Evaluation of Soil Survey Scale for Zone Development of Site‐Specific Nitrogen Management

Cited by 21 publications

References 21 publications

Criteria for Selecting Optimal Nitrogen Fertilizer Rates for Precision Agriculture

Criteria for Selecting Optimal Nitrogen Fertilizer Rates for Precision Agriculture

Evaluating management zones and crop‐sensing relationships for improved irrigated maize nitrogen management

Spatial Variability in Soil Properties, Delineation Site-specific Management Division Based on Soil Fertility Using Fuzzy Clustering in Gwalior, Madhya Pradesh, India

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