Assessing causes of yield gaps in agricultural areas with diversity in climate and soils

Agricultural and Forest Meteorology

Volumn 247, Issue , 2017, Pages 170-180

  Rattalino Edreira, Juan I ^a   Mourtzinis, Spyridon ^b   Conley, Shawn P ^b   Roth, Adam C ^b   Ciampitti, Ignacio A ^c   Licht, Mark A ^d   Kandel, Hans ^e   Kyveryga, Peter M ^f   Lindsey, Laura E ^g   Mueller, Daren S ^d   Naeve, Seth L ^h   Nafziger, Emerson ⁱ   Specht, James E ^a   Stanley, Jordan ^e   Staton, Michael J ^j   Grassini, Patricio ^a  

^a UNIVERSITY OF NEBRASKA LINCOLN   (United States)

^b UNIVERSITY OF WISCONSIN MADISON   (United States)

^c KANSAS STATE UNIVERSITY   (United States)

^d IOWA STATE UNIVERSITY   (United States)

^e NORTH DAKOTA STATE UNIVERSITY   (United States)

^f Iowa Soybean Association   (United States)

^g OHIO STATE UNIVERSITY   (United States)

^h UNIVERSITY OF MINNESOTA   (United States)

ⁱ UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^j MICHIGAN STATE UNIVERSITY   (United States)

more..

Author keywords

Interaction; Soybean; Spatial framework; Survey; Yield gap; Yield potential

Indexed keywords

AGRICULTURAL LAND; AGRICULTURAL MANAGEMENT; CLIMATE CONDITIONS; CROP PRODUCTION; CROP YIELD; FIELD SURVEY; FUNGICIDE; INSECTICIDE; IRRIGATION; RAINFED AGRICULTURE; SOIL TYPE; SOWING DATE; SOYBEAN; SPATIAL ANALYSIS; YIELD RESPONSE;

UNITED STATES;

GLYCINE MAX;

EID: 85026887938     PISSN: 01681923     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.agrformet.2017.07.010     Document Type: Article

References (46)

1. Antle, J., Capalbo, S., Houston, L., Using Big Data to Evaluate Agro-Environmental Policies. 2015, Choices AAEA Available online: http://choicesmagazine.org/choices-magazine/submitted-articles/using-big-data-to-evaluate-agro-environmental-policies.
2. Bastidas, A.M., et al. Soybean sowing date: the vegetative, reproductive, and agronomic impacts. Crop Sci., 48(2), 2008.
3. Calviño, P.A., Andrade, F.H., Sadras, V.O., Maize yield as affected by water availability, soil depth, and crop management. Agron. J. 95:2 (2003), 275–281.
4. Calviño, P., Sadras, V.O., Redolatti, M., Canepa, M., Yield responses to narrow rows as related to interception of radiation and water deficit in sunflower hybrids of varying cycle. Field Crops Res. 88 (2004), 261–267.
5. Cassman, K.G., Dobermann, A., Walters, D.T., Yang, H., Meeting cereal demand while protecting natural resources and improving environmental quality. Annu. Rev. Environ. Resour. 28:1 (2003), 315–358.
6. De Bruin, J.L., Pedersen, P., Soybean seed yield response to planting date and seeding rate in the Upper Midwest. Agron. J. 100:3 (2008), 696–703.
7. De Bruin, J.L., Pedersen, P., New and old soybean cultivar responses to plant density and intercepted light. Crop Sci. 49:6 (2009), 2225–2232.
8. Egli, D.B., Cornelius, P.L., A regional analysis of the response of soybean yield to planting date. Agron. J. 101:2 (2009), 330–335.
9. Evans, L.T., Fisher, R.A., Yield potential: its definition, measurement, and significance. Crop Sci. 39:6 (1999), 1544–1551.
10. Evans, L.T., Crop Evolution, Adaptation, and Yield. 1993, Cambridge University Press, Cambridge, UK.
11. FAOSTAT, Crops and Livestock Trade Database. 2016 www.faostat.fao.org.
12. Fehr, W.R., Caviness, C.E., Stages of soybean development. Cooperative Extension Service, Agriculture and Home Economics Experiment Station, 1977, Iowa State University, Ames, Iowa.
13. Fehr, W.R., Caviness, C.E., Burmood, D.T., Pennington, J.S., Stage of development descriptions for soybeans, Glycine Max (L.) Merrill. Crop Sci. 11:6 (1971), 929–931.
14. Gaspar, A.P., Conley, S.P., Responses of canopy reflectance, light interception, and soybean seed yield to replanting suboptimal stands. Crop Sci. 55:1 (2015), 377–385.
15. Grassini, P., Yang, H., Cassman, K.G., Limits to maize productivity in Western Corn-Belt: a simulation analysis for fully irrigated and rainfed conditions. Agric. For. Meteorol. 149 (2009), 1254–2165.
16. Grassini, P., Thorburn, J., Burr, C., Cassman, K.G., High-yield irrigated maize in the Western U.S. Corn Belt: I. On-farm yield, yield potential, and impact of agronomic practices. Field Crops Res. 120:1 (2011), 142–150.
17. Grassini, P., Torrion, J.A., Cassman, K.G., Yang, H.S., Specht, J.E., Drivers of spatial and temporal variation in soybean yield and irrigation requirements in the western US Corn Belt. Field Crops Res. 163 (2014), 32–46.
18. Grassini, P., Specht, J.E., Tollenaar, M., Ciampitti, I.A., Cassman, K.G., High-yield maize–soybean cropping systems in the US Corn Belt. Sadras, V.O., Calderini, D.F., (eds.) Crop Physiology. Applications for Genetic Improvement and Agronomy, 2015, Academic Press, Oxford, 15–42.
19. Grassini, P., et al. Soybean yield gaps and water productivity in the western U.S. Corn Belt. Field Crops Res. 179 (2015), 150–163.
20. Heatherly, L.G., Planting date, row spacing, and irrigation effects on soybean grown on clay soil. Agron. J. 80:2 (1988), 227–231.
21. Hochman, Z., Gobbett, D., Horan, H., Navarro Garcia, J., Data rich yield gap analysis of wheat in Australia. Field Crops Res. 197 (2016), 97–106.
22. Kravchenko, A.N., Snapp, S.S., Robertson, G.P., Field-scale experiments reveal persistent yield gaps in low-input and organic cropping systems. Proc. Natl. Acad. Sci. U. S. A. 114:5 (2017), 926–931.
23. Lobell, D.B., Cassman, K.G., Field, C.B., Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour., 2009, 179–204.
24. Lollato, R.P., Edwards, J.T., Maximum attainable wheat yield and resource-use efficiency in the Southern Great plains. Crop Sci. 55:6 (2015), 2863–2876.
25. Morell, F.J., et al. Can Crop Simulation Models Be Used to Predict Local to Regional Maize Yields and Total Production in the U.S. Corn Belt?. 2016.
26. Mourtzinis, S., Rattalino Edreira, J.I., Conley, S.P., Grassini, P., From grid to field: assessing quality of gridded weather data for agricultural applications. Eur. J. Agron. 82:Part A (2017), 163–172.
27. Passioura, J.B., Angus, J.F., Improving productivity of crops in water-limited environments. Donald, L.S., (eds.) Advances in Agronomy, 2010, Academic Press, 37–75.
28. R Development Core Team, R: A Language and Environment for Statistical Computing. 2016, R Foundation for Statistical Computing, Vienna, Austria.
29. Rowntree, S.C., et al. Genetic gain × management interactions in soybean: I. Planting date. Crop Sci. 53:3 (2013), 1128–1138.
30. Sadras, V.O., Angus, J.F., Benchmarking water-use efficiency of rainfed wheat in dry environments. Aust. J. Agric. Res. 57:8 (2006), 847–856.
31. Sadras, V.O., et al. Yield Gap Analysis of Rainfed and Irrigated Crops: Methods and Case Studies. 2015, FAO, Rome, Italy.
32. Salvagiotti, F., et al. Nitrogen uptake, fixation and response to fertilizer N in soybeans: a review. Field Crops Res. 108:1 (2008), 1–13.
33. Setiyono, T.D., et al. Simulation of soybean growth and yield in near-optimal growth conditions. Field Crops Res. 119:1 (2010), 161–174.
34. Silva, J.V., Reidsma, P., Laborte, A.G., van Ittersum, M.K., Explaining rice yields and yield gaps in Central Luzon, Philippines: an application of stochastic frontier analysis and crop modelling. Eur. J. Agron., 2016.
35. Subedi, K.D., Ma, B.L., Assessment of some major yield-limiting factors on maize production in a humid temperate environment. Field Crops Res. 110:1 (2009), 21–26.
36. Taylor, H.M., Soybean growth and yield as affected by row spacing and by seasonal water supply. Agron. J. 72:3 (1980), 543–547.
37. Thomson, A.M., et al. Science in the supply chain: collaboration opportunities for advancing sustainable agriculture in the United States. Agric. Environ. Lett., 2(1), 2017.
38. USDA-NASS, 2016. USDA-National Agricultural Statistics Service (NASS), Crop U.S. State and County Databases. https://www.nass.usda.gov.
39. USDA-NASS, 2016. USDA-National Agricultural Statistics Service (NASS), National Cultivated Layer. https://www.nass.usda.gov.
40. van Ittersum, M.K., Rabbinge, R., Concept of production ecology for analysis and quantification of agricultural imput-output combination. Field Crops Res. 52 (1997), 197–208.
41. van Ittersum, M.K., et al. Yield gap analysis with local to global relevance—a review. Field Crops Res. 143 (2013), 4–17.
42. van Wart, J., et al. Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Res. 143 (2013), 44–55.
43. Villamil, M.B., Davis, V.M., Nafziger, E.D., Estimating factor contributions to soybean yield from farm field data. Agron. J. 104:4 (2012), 881–887.
44. Wilhelm, W.W., Wortmann, C.S., Tillage and rotation interactions for corn and soybean grain yield as affected by precipitation and air temperature. Agron. J. 96:2 (2004), 425–432.
45. Yang, H.S., et al. Hybrid-maize—a maize simulation model that combines two crop modeling approaches. Field Crops Res. 87:2–3 (2004), 131–154.
46. Yang, H., Grassini, P., Cassman, K.G., Aiken, R.M., Coyne, P.I., Improvements to the Hybrid-maize model for simulating maize yields in harsh rainfed environments. Field Crops Res. 204 (2017), 180–190.