Abstract
Understanding yield potential and yield-limiting factors is essential for improving profitability and grain yields while avoiding adverse environmental effects. In the USA, grain yield monitors are widely used but the information they provide is rarely used to understand within-field yield variations and associated yield constraints. The objectives of this research were to understand the influence of in-season precipitation on within-field spatio-temporal variation of maize (Zea mays L.) yield and to determine manageable yield variation in two contrasting Major Land Resource Areas of the Mid-Atlantic USA. It does this by assessing the association of grain yield monitor data and in-season precipitation information to be used for variable rate management. Maize yields, as evaluated by baseline functions, were more closely associated with in-season precipitation in the Coastal Plain than in the Piedmont. The study then used standardized principal component analysis (stdPCA) to reveal within-field yield patterns. These varied only under moisture-limited conditions in the Coastal Plain. In the Piedmont, the within-field yield pattern was more consistent under a range of in-season precipitation conditions. In Coastal Plain rainfed fields, the yield predictability increased at the end of June, indicating the possibility of predicting within-field spatial patterns in mid-season. These approaches were successful in deciding whether within-field site- and time-specific management is beneficial for a particular region or field. The presented method, combining stdPCA and geostatistical assessment, is useful in strategizing precision crop management, but do not reveal causes. Detailed soil information and topography could additionally be valuable for understanding constraints to crop yield.
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References
Arslan, S., & Colvin, T. S. (2002). Grain yield mapping: Yield sensing, yield reconstruction, and errors. Precision Agriculture, 3(2), 135–154.
Belcher, B. N., & DeGaetano, A. T. (2005). A method to infer time of observation at US Cooperative Observer Network stations using model analyses. International Journal of Climatology, 25(9), 1237–1251.
Blackmore, S. (2000). The interpretation of trends from multiple yield maps. Computers and Electronics in Agriculture, 26(1), 37–51.
Blackmore, S., Godwin, R. J., & Fountas, S. (2003). The analysis of spatial and temporal trends in yield map data over six years. Biosystems Engineering, 84(4), 455–466.
Brock, A., Brouder, S. M., Blumhoff, G., & Hofmann, B. S. (2005). Defining yield-based management zones for corn–soybean rotations. Agronomy Journal, 97(4), 1115–1128.
Calviño, P. A., Andrade, F. H., & Sadras, V. O. (2003). Maize yield as affected by water availability, soil depth, and crop management. Agronomy Journal, 95(2), 275–281.
Calviño, P. A., & Sadras, V. O. (1999). Interannual variation in soybean yield: Interaction among rainfall, soil depth and crop management. Field Crops Research, 63(3), 237–246.
Cox, M. S., & Gerard, P. D. (2007). Soil management zone determination by yield stability analysis and classification. Agronomy Journal, 99(5), 1357–1365.
Eastman, J. R., & Filk, M. (1993). Long sequence time series evaluation using standardized principal components. Photogrammetric Engineering and Remote Sensing, 59(6), 991–996.
Florin, M. J., McBratney, A. B., & Whelan, B. M. (2009). Quantification and comparison of wheat yield variation across space and time. European Journal of Agronomy, 30(3), 212–219.
Foss, J. E., Fanning, D. S., Miller, F. P., & Wagner, D. P. (1978). Loess deposits of the Eastern Shore of Maryland. Soil Science Society of America Journal, 42(2), 329–334.
GRASS Development Team. (2015). Geographic Resources Analysis Support System (GRASS) Software, Version 6.4. Open Source Geospatial Foundation Project. Retrieved April 18, 2021, from http://grass.osgeo.org
Grassini, P., Eskridge, K. M., & Cassman, K. G. (2013). Distinguishing between yield advances and yield plateaus in historical crop production trends. Nature Communications. https://doi.org/10.1038/ncomms3918
Grassini, P., van Bussel, L. G. J., Wart, J. V., Wolf, J., Claessens, L., Yang, H., et al. (2015). How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crops Research, 177, 49–63.
Hochman, Z., Gobbett, D., Holzworth, D., McClelland, T., van Rees, H., Marinoni, O., et al. (2012). Quantifying yield gaps in rainfed cropping systems: A case study of wheat in Australia. Field Crops Research, 136, 85–96.
Hong, N., Scharf, P. C., Davis, J. G., Kitchen, N. R., & Sudduth, K. A. (2007). Economically optimal nitrogen rate reduces soil residual nitrate. Journal of Environmental Quality, 36(2), 354–362.
Jiang, P., & Thelen, K. D. (2004). Effect of soil and topographic properties on crop yield in a north-central corn–soybean cropping system. Agronomy Journal, 96, 252–258.
Kaul, M., Hill, R. L., & Walthall, C. (2005). Artificial neural networks for corn and soybean yield prediction. Agricultural Systems, 85(1), 1–18.
Kettler, T. A., Doran, J. W., & Gilbert, T. L. (2001). Simplified method for soil particle-size determination to accompany soil-quality analyses. Soil Science Society of America Journal, 65, 849–852.
Kinoshita, R., van Es, H., Dantinne, J., & Twining, M. (2016). Within-field profitability informs agronomic management decision in the Mid-Atlantic USA. Agricultural & Environmental Letters. https://doi.org/10.2134/ael2016.09.0034
Koen, B. V. (1988). Toward a definition of the engineering method. European Journal of Engineering Education, 13(3), 307–315.
Kravchenko, A. N., & Bullock, D. G. (2000). Correlation of corn and soybean grain yield with topography and soil properties. Agronomy Journal, 92, 75–83.
Kumhálová, J., Kumhála, F., Kroulík, M., & Matějková, Š. (2011). The impact of topography on soil properties and yield and the effects of weather conditions. Precision Agriculture, 12, 813–830.
Lawes, R. A., Oliver, Y. M., & Robertson, M. J. (2009). Capturing the in-field spatial-temporal dynamic of yield variation. Crop & Pasture Science, 60, 834–843.
Lowenberg-DeBoer, J., & Erickson, B. (2019). Setting the record straight on precision agriculture adoption. Agronomy Journal, 111, 1552–1569.
Markewich, H. W., Pavich, M. J., & Buell, G. R. (1990). Contrasting soils and landscapes of the Piedmont and Coastal Plain, eastern United States. Geomorphology, 3(3), 417–447.
Mitášová, H., & Hofierka, J. (1993). Interpolation by regularized spline with tension: II. Application to terrain modeling and surface geometry analysis. Mathematical Geology, 25(6), 657–669.
Mueller, S. M., & Vyn, T. J. (2018). Physiological constraints to realizing maize grain yield recovery with silking-stage nitrogen fertilizer applications. Field Crops Research, 228, 102–109.
Neild, R. E., & Newman, J. E. (1990). Growing season characteristics and requirements in the corn belt. Purdue University, Cooperative Extension Service.
Oliver, Y. M., Robertson, M. J., & Wong, M. T. F. (2010). Integrating farmer knowledge, precision agriculture tools, and crop simulation modelling to evaluate management options for poor-performing patches in cropping fields. European Journal of Agronomy, 32(1), 40–50.
Pebesma, E. J. (2004). Multivariate geostatistics in S: The gstat package. Computers & Geosciences, 30, 683–691.
Peel, M. C., Finlayson, B. L., & McMahon, T. A. (2007). Updated world map of the Koppen-Geiger climate classification. Hydrology and Earth System Sciences, 11, 1633–1644.
Potgieter, A. B., Hammer, G. L., & Butler, D. (2002). Spatial and temporal patterns in Australian wheat yield and their relationship with ENSO. Australian Journal of Agricultural Research, 53(1), 77–89.
Pringle, M. J., McBratney, A. B., Whelan, B. M., & Taylor, J. A. (2003). A preliminary approach to assessing the opportunity for site-specific crop management in a field, using yield monitor data. Agricultural Systems, 76(1), 273–292.
QGIS Development Team. (2015). QGIS Geographic Information System. Open Source Geospatial Foundation Project. Retrieved April 18, 2021, from http://qgis.osgeo.org
R Core Team. (2014). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Retrieved April 18, 2021, from http://www.R-project.org/
Russo, D., & Bresler, E. (1981). Effect of field variability in soil hydraulic properties on solutions of unsaturated water and salt flows. Soil Science Society of America Journal, 45(4), 675–681.
Scharf, P. C., Shannon, D. K., Palm, H. L., Sudduth, K. A., Drummond, S. T., Kitchen, N. R., et al. (2011). Sensor-based nitrogen applications out-performed producer-chosen rates for corn in on-farm demonstrations. Agronomy Journal, 103(6), 1683–1691.
Sela, S., van Es, H. M., Moebius-Clune, B. N., Marjerison, R., Melkonian, J., Moebius-Clune, D., et al. (2016). Adapt-N outperforms grower-selected nitrogen rates in Northeast and Midwestern United States strip trials. Agronomy Journal, 108, 1726–1734.
Simonson, R. W. (1982). Loess in soils of Delaware, Maryland, and northeastern Virginia. Soil Science, 133(5), 167–178.
Soil Survey Staff. (2015). Official Soil Series Descriptions. Retrieved August 29, 2015, from https://soilseries.sc.egov.usda.gov/osdquery.aspx
Stanford, G. (1973). Rationale for optimum nitrogen fertilization in corn production. Journal of Environmental Quality, 2(2), 159–166.
Sudduth, K. A., & Drummond, S. T. (2007). Yield Editor: Software for removing errors from crop yield maps. Agronomy Journal, 99(6), 1471–1482.
Tremblay, N., Bouroubi, Y. M., Bélec, C., Mullen, R. W., Kitchen, N. R., Thomason, W. E., et al. (2012). Corn response to nitrogen is influenced by soil texture and weather. Agronomy Journal, 104(6), 1658–1671.
Troy, T. J., Kipgen, C., & Pal, I. (2015). The impact of climate extremes and irrigation on US crop yields. Environmental Research Letters. https://doi.org/10.1088/1748-9326/10/5/054013
USDA-NRCS. (2006). Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin. U.S. Department of Agriculture Handbook 296.
van Es, H. M., Kay, B. D., Melkonian, J. J., & Sogbedji, J. M. (2007). Nitrogen management under maize in humid regions: Case for a dynamic approach. In Bruulsema (Ed.), Managing crop nutrition for weather (pp. 6–13). International Plant Nutrition Institute.
van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P., & Hochman, Z. (2013). Yield gap analysis wtih local to global relevance: A review. Field Crops Research, 143, 4–17.
van Ittersum, M. K., & Rabbinge, R. (1997). Concepts in production ecology for analysis and quantification of agricultural input-output combinations. Field Crops Research, 52(3), 197–208.
Ware, E. C. (2005). Corrections to Radar-Estimated Precipitation Using Observed Rain Gauge Data (MS thesis). Cornell University.
Warrick, A. W., Myers, D. E., & Nielsen, D. R. (1986). Geostatistical Methods Applied to Soil Science. In Methods of Soil Analysis, Part 1. Physical and Mineralogical Methods (2nd ed., pp. 53–82). American Society of Agronomy: Soil Science Society of America.
Weaver, K. N. (1967). Generalized Geologic Map of Maryland. Maryland Geological Survey.
Wilks, D. S. (2008). High-resolution spatial interpolation of weather generator parameters using local weighted regressions. Agricultural and Forest Meteorology, 148(1), 111–120.
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The authors are grateful to Jeff Melkonian, John Dantinne, and Mike Twinning who provided valuable suggestions.
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Funding was provided by Willard Agri-Service of Frederick, Inc.
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Kinoshita, R., Rossiter, D. & van Es, H. Spatio-temporal analysis of yield and weather data for defining site-specific crop management zones. Precision Agric 22, 1952–1972 (2021). https://doi.org/10.1007/s11119-021-09820-z
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DOI: https://doi.org/10.1007/s11119-021-09820-z