Determining the within-field yield variability from seasonally changing soil conditions

Boenecke, Eric; Lueck, E.; Rüehlmann, Joerg; Gruendling, Ralf; Franko, Uwe

doi:10.1007/s11119-017-9556-z

Cited by 15 publications

(12 citation statements)

References 56 publications

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“…The clay content can also influence soil water and nutrient content and result in crop yield variability across the field [61,75]. A study showed that clay content in the soil could explain up to 17% variability in winter wheat yield by affecting the water available in the soil for crop growth [76]. A study conducted in the Southern High Plains showed that a very sandy soil (the Brownfield soil series) with high slope consistently had low yield when given the same amount of irrigation water, fertilizer, seed population, and other management practices [56].…”

Section: Soil Texturementioning

confidence: 99%

Agronomic Basis and Strategies for Precision Water Management: A Review

2019

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Agriculture faces the challenge of feeding a growing population with limited or depleting fresh water resources. Advances in irrigation systems and technologies allow site-specific application of irrigation water within the field to improve water use efficiency or reduce water usage for sustainable crop production, especially in arid and semi-arid regions. This paper discusses recent development of variable-rate irrigation (VRI) technologies, data and information for VRI application, and impacts of VRI, including profitability using this technology, with a focus on agronomic factors in precision water management. The development in sprinkler systems enabled irrigation application with greater precision at the scale of individual nozzle control. Further research is required to evaluate VRI prescription maps integrating different soil and crop characteristics in different environments. On-farm trials and whole-field studies are needed to provide support information for practical VRI applications. Future research also needs to address the adjustment of the spatial distribution of prescription zones in response to temporal variability in soil water status and crop growing conditions, which can be evaluated by incorporating remote and proximal sensing data. Comprehensive decision support tools are required to help the user decide where to apply how much irrigation water at different crop growth stages to optimize water use and crop production based on the regional climate conditions and cropping systems.

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Section: Soil Texturementioning

confidence: 99%

Agronomic Basis and Strategies for Precision Water Management: A Review

2019

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“…Among others, such models have been successfully applied to determine the effects of the spatial variability of soil moisture on yield Paz et al, 1998Paz et al, , 1999, to investigate yield loss in corn (Zea mays L.) in a cool climate due to abiotic stress (Žydelis, Weihermüller, Herbst, Klosterhalfen, & Lazauskas, 2018), and to validate management zones derived from satellite-based vegetation indices (Basso, Ritchie, Pierce, Braga, & Jones, 2001). These process-oriented crop growth models typically rely on a one-dimensional description of water flow in the soil column (Vereecken et al, 2016) and require a detailed description of the soil profile characteristics including soil hydraulic properties (Boenecke, Lueck, Ruehlmann, Gruendling, & Franko, 2018). In general, this information is obtained from generalpurpose maps (Boenecke et al, 2018) that discretize soil in relatively large polygons.…”

Section: Introductionmentioning

confidence: 99%

“…Wong and Asseng (2006) used EMI to map the plant-available water content and used simulations to illustrate how variations in available water interacted with the amount and timing of precipitation and yield variability within a single 70-ha field. Similarly, Boenecke et al (2018) used EMI as a basis for simulating the spatial variability of soil water content and yield at the farm scale (30 ha). Despite these successful examples, there is a general lack of studies that link soil maps with modeling applications at scales larger than a single farm (Krüger et al, 2013) and for multiple crops.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Boenecke et al. (2018) used EMI as a basis for simulating the spatial variability of soil water content and yield at the farm scale (30 ha). Despite these successful examples, there is a general lack of studies that link soil maps with modeling applications at scales larger than a single farm (Krüger et al., 2013) and for multiple crops.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics‐based quantitative soil information

Brogi

Huisman

Herbst

et al. 2020

Vadose Zone Journal

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Agroecosystem models that simulate crop growth as a function of weather conditions and soil characteristics are among the most promising tools for improving crop yield and achieving more sustainable agricultural production systems. This study aims at using spatially distributed crop growth simulations to investigate how field-scale patterns in soil properties obtained using geophysical mapping affect the spatial variability of soil water content dynamics and growth of crops at the square kilometer scale. For this, a geophysics-based soil map was intersected with land use information. Soil hydraulic parameters were calculated using pedotransfer functions. Simulations of soil water content dynamics performed with the agroecosystem model AgroC were compared with soil water content measured at two locations, resulting in RMSE of 0.032 and of 0.056 cm 3 cm −3 , respectively. The AgroC model was then used to simulate the growth of sugar beet (Beta vulgaris L.), silage maize (Zea mays L.), potato (Solanum tuberosum L.), winter wheat (Triticum aestivum L.), winter barley (Hordeum vulgare L.), and winter rapeseed (Brassica napus L.) in the 1-by 1-km study area. It was found that the simulated leaf area index (LAI) was affected by the magnitude of simulated water stress, which was a function of both the crop type and soil characteristics. Simulated LAI was generally consistent with the observed LAI calculated from normalized difference vegetation index (LAI NDVI) obtained from RapidEye satellite data. Finally, maps of simulated agricultural yield were produced for four crops, and it was found that simulated yield matched well with actual harvest data and literature values. Therefore, it was concluded that the information obtained from geophysics-based soil mapping was valuable for practical agricultural applications.

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“…Fig. 8 Geophilus mapped soil texture classes (derived from the German KA5 soil texture classification (Eckelmann et al 2005)) Ss pure sand, St2 slightly loamy sand, St3 medium clayey sand, Sl4 highly sandy loam, Ls4 highly sandy loam, Lts clayey sandy loam (a), the MSP-mapped pH values (b) and SOM content (c) and the lime supply level at 2 × 2 m resolution In other studies, Boenecke et al (2018) and Meyer et al (2019) used data from the Geophilus system to successfully generate predictive soil texture maps of the clay, silt and sand fractions of the topsoil for practical purposes. Meyer et al (2019) achieved the best prediction results by deriving the soil texture of the topsoil using the gamma mapping results and by calculating the dimensionless relationship between the gamma and electrical resistivity mapping results.…”

Section: Generated Soil Mapsmentioning

confidence: 98%

Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data

et al. 2020

Self Cite

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Soil acidification is caused by natural paedogenetic processes and anthropogenic impacts but can be counteracted by regular lime application. Although sensors and applicators for variable-rate liming (VRL) exist, there are no established strategies for using these tools or helping to implement VRL in practice. Therefore, this study aimed to provide guidelines for site-specific liming based on proximal soil sensing. First, high-resolution soil maps of the liming-relevant indicators (pH, soil texture and soil organic matter content) were generated using on-the-go sensors. The soil acidity was predicted by two ion-selective antimony electrodes (RMSEpH: 0.37); the soil texture was predicted by a combination of apparent electrical resistivity measurements and natural soil-borne gamma emissions (RMSEclay: 0.046 kg kg−1); and the soil organic matter (SOM) status was predicted by a combination of red (660 nm) and near-infrared (NIR, 970 nm) optical reflection measurements (RMSESOM: 6.4 g kg−1). Second, to address the high within-field soil variability (pH varied by 2.9 units, clay content by 0.44 kg kg−1 and SOM by 5.5 g kg−1), a well-established empirical lime recommendation algorithm that represents the best management practices for liming in Germany was adapted, and the lime requirements (LRs) were determined. The generated workflow was applied to a 25.6 ha test field in north-eastern Germany, and the variable LR was compared to the conventional uniform LR. The comparison showed that under the uniform liming approach, 63% of the field would be over-fertilized by approximately 12 t of lime, 6% would receive approximately 6 t too little lime and 31% would still be adequately limed.

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Determining the within-field yield variability from seasonally changing soil conditions

Cited by 15 publications

References 56 publications

Agronomic Basis and Strategies for Precision Water Management: A Review

Agronomic Basis and Strategies for Precision Water Management: A Review

Simulation of spatial variability in crop leaf area index and yield using agroecosystem modeling and geophysics‐based quantitative soil information

Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data

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