Determining Economic Optimum Soil Sampling Density for Potassium Fertilizer Management in Soybean: A Case Study in the U.S. Mid-South

Bayarbat Badarch

Department of Agriculture Economics and Agribusiness, University of Arkansas, Fayetteville, AR 72701, USA

Michael P. Popp

Department of Agriculture Economics and Agribusiness, University of Arkansas, Fayetteville, AR 72701, USA

Aurelie M. Poncet

Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA

Shelby T. Rider

Department of Agriculture Economics and Agribusiness, University of Arkansas, Fayetteville, AR 72701, USA

Nathan A. Slaton

Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA

DOI: https://doi.org/10.36956/rwae.v4i4.985

Received: 18 November 2023; Received in revised form: 12 December 2023; Accepted: 22 December 2023; Published: 29 December 2023

Copyright © 2023 Bayarbat Badarch, Michael P. Popp, Aurelie M. Poncet, Shelby T. Rider, Nathan A. Slaton. Published by Nan Yang Academy of Sciences Pte. Ltd.

Creative Commons LicenseThis is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.


Abstract

Determining the number of samples to collect in a field to develop soil-test K (STK) maps that are sufficiently accurate for profit-maximizing fertilizer rate prescription maps is complex. The decision also hinges on the application method—variable rate or uniform rate (VRT vs. URT). Using a 400 m2 fishnet grid on a 26.3-ha irrigated soybean field, the authors compared sampling densities ranging from 5 to 60 samples or 5.3 ha/sample to 0.40 ha/sample. Subsequently, the authors simulated yields based on STK maps generated with that range of samples taken to generate i) associated profit-maximizing fertilizer-K rates (K*) that varied by grid with VRT, or ii) a single fertilizer rate based on field-average STK with URT, to compare revenue less fertilizer cost (NR) across VRT, URT, and sampling strategy. With more information, NR increased at a diminishing rate as crop needs could be better matched to fertilizer needs with greater detail in STK maps with VRT. Also, fertilizer use with URT was higher than VRT given the field-specific distribution of STK. Regardless of the sampling strategy, NR was higher for VRT than URT, however, that benefit was smaller than the upcharges for VRT equipment. Marginal benefits from added soil sampling were smaller than their marginal cost leading to an optimal least-cost, 5-sample strategy and URT. Changing one of the 5 sampling locations, however, revealed unreliable field average STK estimates. Since soil samples inform about several macronutrients, splitting soil sampling charges across K and P profitably justified sampling near every 1.5 ha with URT.

Keywords: Soil sampling density, Potassium, Soybean, URT, VRT


References

[1] Bongiovanni, R., Lowenberg-DeBoer, J., 2004. Precision agriculture and sustainability. Precision Agriculture. 5, 359-387. DOI: https://doi.org/10.1023/B:PRAG.0000040806.39604.aa

[2] Finger, R., Swinton, S.M., El Benni, N., et al., 2019. Precision farming at the nexus of agricultural production and the environment. Annual Review of Resource Economics. 11, 313-335. DOI: https://doi.org/10.1146/annurev-resource-100518-093929

[3] Lowenberg-DeBoer, J., Erickson, B., 2019. Setting the record straight on precision agriculture adoption. Agronomy Journal. 111(4), 1552-1569. DOI: https://doi.org/10.2134/agronj2018.12.0779

[4] Lyons, S.E., Osmond, D.L., Slaton, N.A., et al., 2020. FRST: A national soil testing database to improve fertility recommendations. Agricultural & Environmental Letters. 5(1), e20008. DOI: https://doi.org/10.1002/ael2.20008

[5] Lyons, S.E., Clark, J.D., Osmond, D.L., et al., 2023. Current status of US soil test phosphorus and potassium recommendations and analytical methods. Soil Science Society of America Journal. 87(4), 985-998. DOI: https://doi.org/10.1002/saj2.20536

[6] Kravchenko, A.N., 2003. Influence of spatial structure on accuracy of interpolation methods. Soil Science Society of America Journal. 67(5), 1564-1571. DOI: https://doi.org/10.2136/sssaj2003.1564

[7] Kovacs, K. F., Rider, S., 2022. Estimating the Demand for In-situ Groundwater for Climate Resilience: The Case of the Mississippi River Alluvial Aquifer in Arkansas. NBER Chapters.

[8] Schmidt, J.P., Taylor, R.K., Milliken, G.A., 2002. Evaluating the potential for site-specific phosphorus applications without high-density soil sampling. Soil Science Society of America Journal. 66(1), 276-283. DOI: https://doi.org/10.2136/sssaj2002.2760

[9] Franzen, D.W., Peck, T.R., 1995. Field soil sampling density for variable rate fertilization. Journal of Production Agriculture. 8(4), 568-574. DOI: https://doi.org/10.2134/jpa1995.0568

[10] Lawrence, P.G., Roper, W., Morris, T.F., et al., 2020. Guiding soil sampling strategies using classical and spatial statistics: A review. Agronomy Journal. 112(1), 493-510. DOI: https://doi.org/10.1002/agj2.20048

[11] Murrell, T.S., Fixen, P.E., Bruulsema, T.W., et al., 2015. The fertility of North American soils: A preliminary look at 2015 results. Better Crops Plant Food. 99(4), 28-31.

[12] Wayne E. Sabbe Arkansas Soil Fertility Studies 2012 [Internet]. University of Arkansas Agricultural Experiment Station [cited 2023 May 16]. Available from: https://scholarworks.uark.edu/aaesser/167

[13] Wayne E. Sabbe Arkansas Soil Fertility Studies 2022 [Internet]. University of Arkansas Agricultural Experiment Station [cited 2023 May 16]. Available from: https://scholarworks.uark.edu/aaesser/213

[14] Oliver, K.B., Popp, M.P., Fang, D., et al., 2023. Potassium fertilizer rate recommendations: Does accounting for soil stock of potassium matter? Journal of Agricultural and Applied Economics. 55(1), 13-33. DOI: https://doi.org/10.1017/aae.2023.1

[15] Popp, M.P., Slaton, N.A., Roberts, T.L., 2020. Profit-maximizing potassium fertilizer recommendations for soybean. Agronomy Journal. 112(6), 5081-5095. DOI: https://doi.org/10.1002/agj2.20424

[16] Popp, M.P., Slaton, N.A., Norsworthy, J.S., et al., 2021. Rice yield response to potassium: An economic analysis. Agronomy Journal. 113(1), 287-297. DOI: https://doi.org/10.1002/agj2.20471

[17] Mississippi State Budget Generator Crop Cost of Production Archives (2011-2023) [Internet]. Mississippi State University [cited 2023 Mar 10]. Available from: https://www.agecon.msstate.edu/whatwedo/budgets/archive.php

[18] McNall, P.E., 1933. The law of diminishing returns in agriculture. Journal of Agricultural Research. 47(3), 167-178.

[19] Oliver, M.A., Webster, R., 2014. A tutorial guide to geostatistics: Computing and modelling variograms and kriging. Catena. 113, 56-69. DOI: https://doi.org/10.1016/j.catena.2013.09.006

[20] Mallarino, A.P., Wittry, D.J., 2004. Efficacy of grid and zone soil sampling approaches for site-specific assessment of phosphorus, potassium, pH, and organic matter. Precision Agriculture. 5, 131-144. DOI: https://doi.org/10.1023/B:PRAG.0000022358.24102.1b

[21] Matcham, E.G., Subburayalu, S.K., Culman, S.W., et al., 2021. Implications of choosing different interpolation methods: A case study for soil test phosphorus. Crop, Forage & Turfgrass Management. 7(2), e20126. DOI: https://doi.org/10.1002/cft2.20126

[22] Annual Arkansas Soybean Prices [Internet]. USDA NASS [cited 2023 May 10]. Available from: https://quickstats.nass.usda.gov/results/10939681-6DA0-3684-953D-10639E87C4D0

[23] Gramm, J., Guo, J., Hüffner, F., et al., 2007. Algorithms for compact letter displays: Comparison and evaluation. Computational Statistics & Data Analysis. 52(2), 725-736. DOI: https://doi.org/10.1016/j.csda.2006.09.035

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