Estimating Soil Organic Carbon in Central Iowa Using Aerial Imagery and Soil Surveys

Gelder, Brian K.; Anex, Robert P.; Kaspar, T. C.; Karlen, Douglas L.

doi:10.2136/sssaj2010.0260

Cited by 11 publications

(4 citation statements)

References 22 publications

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“…A disadvantage of model-database tools is that the datadriven approach requires inputs that pose a cost to the user or database information that may contain inaccuracies (e.g., SSURGO;Gelder et al, 2011). Nevertheless, most requisite information relates to standard management information or application records (SOM content, hybrids, manure and fertilizer applications, etc.).…”

Section: Limitations Of Computer Modelsmentioning

confidence: 99%

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

et al. 2018

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A gronomy J our n al • Volume 110 , I ssue 1 • 2 018 1 T he goal of an N recommendation system is to accurately estimate the gap between the N provided by the soil and the N required by the plant. Accurately estimating this gap depends on the ability of the recommendation system to accurately estimate fi eld or subfi eld specifi c economically optimal nitrogen rates (EONR). Current recommendation systems are not as accurate as needed to provide consistently reliable estimates of N needs across years at the fi eld or subfi eld scale. Uncontrollable factors like temperature, rainfall timing, intensity and amount, and interactions of temperature and rainfall with factors such as N source, timing and placement, plant genetics, and soil characteristics combine to make N rate recommendations for an individual fi eld or rates for subfi elds a process guided as much by science as by the best professional judgement of farmers and farm advisors.Substantial evidence has accumulated that EONRs can vary widely across fi elds, within fi elds and over years in the same fi eld for a wide range of crops and geographies. Examples ABSTRACTNitrogen fi xation by the Haber-Bosch process has more than doubled the amount of fi xed N on Earth, signifi cantly infl uencing the global N cycle. Much of this fi xed N is made into N fertilizer that is used to produce nearly half of the world's food. Too much of the N fertilizer pollutes air and water when it is lost from agroecosystems through volatilization, denitrifi cation, leaching, and runoff . Most of the N fertilizer used in the United States is applied to corn (Zea mays L.), and the profi tability and environmental footprint of corn production is directly tied to N fertilizer applications. Accurately predicting the amount of N needed by corn, however, has proven to be challenging because of the eff ects of rainfall, temperature, and interactions with soil properties on the N cycle. For this reason, improving N recommendations is critical for profi table corn production and for reducing N losses to the environment. Th e objectives of this paper were to review current methods for estimating N needs of corn by: (i) reviewing fundamental background information about how N recommendations are created; (ii) evaluating the performance, strengths, and limitations of systems and tools used for making N fertilizer recommendations; (iii) discussing how adaptive management principles and methods can improve recommendations; and (iv) providing a framework for improving N fertilizer rate recommendations.

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Section: Limitations Of Computer Modelsmentioning

confidence: 99%

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

et al. 2018

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“…Past research found that field measurements of SOC compared adequately with SSURGO values in Louisiana, USA ( R 2 = 0.63) (Zhong & Xu, 2011) and soils derived from glacial parent materials in New York ( R 2 = 0.53) (Mikhailova et al., 2016). Further, SSURGO estimates of SOC have been shown to accurately capture the range of SOC values at field scales (Gelder et al., 2011) in central Iowa. We also ran field‐scale simulations at three sites (see Text S1 in Supporting Information S1) to compare field estimated and gSSURGO SOC values and found correlations comparable to those from prior studies ( R 2 = 0.47 to 0.69) (Figures S1 and S2 in Supporting Information S1).…”

Section: Methodsmentioning

confidence: 99%

The Future of Soils in the Midwestern United States

2023

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Soil is the source of the vast majority of food consumed on Earth, and soils constitute the largest terrestrial carbon pool. Soil erosion associated with agriculture reduces crop productivity, and the redistribution of soil organic carbon (SOC) by erosion has potential to influence the global carbon cycle. Tillage strongly influences the erosion and redistribution of soil and SOC. However, tillage is rarely considered in predictions of soil erosion in the U.S.; hence regionwide estimates of both the current magnitude and future trends of soil redistribution by tillage are unknown. Here we use a landscape evolution model to forecast soil and SOC redistribution in the Midwestern United States over centennial timescales. We predict that present‐day rates of soil and SOC erosion are 1.1 ± 0.4 kg ⋅ m−‐2 ⋅ yr−‐1 and 12 ± 4 g ⋅ m−2 ⋅ yr−1, respectively, but these rates will rapidly decelerate due to diffusive evolution of topography and the progressive depletion of SOC in eroding soil profiles. After 100 years, we forecast that 8.8 (+1.9/−2.1) Pg of soil and 0.17 (+0.03/−0.04) Pg of SOC will have eroded, causing the surface concentration of SOC to decrease by 4.4% (+0.9/−1.1%). Model simulations that include more widespread adoption of low‐intensity tillage (i.e., no‐till farming) determine that soil redistribution, SOC redistribution, and surficial SOC loss after 100 years would decrease by ∼95% if low‐intensity tillage is fully adopted. Our findings indicate that low‐intensity tillage could greatly decrease soil degradation and that the potential for agricultural soil erosion to influence the global carbon cycle will diminish with time due to a reduction in SOC burial.

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“…Soil color is a feature that has been observed to have strong correlation with spectral reflectance features and between soil organic matter and soil color [16], [17], [18], [19], [20], [35] and soil moisture content and soil color [21], [22], [23], [24]. Dark color of the soil is essentially linked to higher values of SOM, SMC, intrinsic soil fertility [25], [26], [45]. Therefore, this kind of amalgamation among soil reflectance, soil organic and moisture content is a very good way to estimate the content through modelling.…”

Section: Introductionmentioning

confidence: 99%

Predictive Analysis for Soil moisture content and Soil organic matter from Soil Images

Srivast

Shukla

Bansal

2022

Preprint

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– Soil organic matter (SOM) and soil moisture contents (SMC) are two main properties in defining soil health. It is a challenge to measure organic matter and moisture content in soil, as the conventional methods are time, labor and money consuming. In order to overcome these challenges, various image processing based models have been proposed to predict SOM and SMC. Proposed model uses a stepwise multiple linear regression (SMLR) method to predict these properties on basis of soil color features like color moments, GLCMs and different color models as well, since, soil moisture and organic content are influenced by soil color. Multiple soil samples from field are collected with a certain distance in order to simulate continuous variation in the contents. Loss of ignition method is used to generate the ground truth to feed the model. For produce and compare the result first 34 and then 6 optimal predictor variables are used in model. The output results in external validation for SOM prediction were: R 2 = 0.07, RMSE = 0.76, RPIQ = 1.00 and that for SMC were: R 2 = 0.77, RMSE = 0.55, RPIQ = 1.07.

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Estimating Soil Organic Carbon in Central Iowa Using Aerial Imagery and Soil Surveys

Cited by 11 publications

References 22 publications

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement

The Future of Soils in the Midwestern United States

Predictive Analysis for Soil moisture content and Soil organic matter from Soil Images

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