Climate Forecasts for Corn Producer Decision Making

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Paper No. 5 Page 1 Copyright Ó 2014, Paper 18-05; 22727 wors, 1 Figures, 0 Animations, 0 Tables. http://earthinteractions.org Climate Forecasts for Corn Proucer Decision Making Eugene S.

1 Paper No. 5 Page 1 Copyright Ó 2014, Paper 18-05; wors, 1 Figures, 0 Animations, 0 Tables. Climate Forecasts for Corn Proucer Decision Making Eugene S. Takle, a,b Christopher J. Anerson, b Jeffrey Anresen, c James Angel, Roger W. Elmore, b Benjamin M. Gramig, e Patrick Guinan, f Steven Hilberg, g Doug Kluck, h Raymon Massey, i Dev Niyogi, j Jeanne M. Schneier, k Martha D. Shulski, l Dennis Toey, m an Melissa Wihalm n b Agronomy Department, Iowa State University, Ames, Iowa c Department of Geography, Michigan State University, East Lansing, Michigan Illinois State Water Survey, University of Illinois Urbana Champaign, Champaign, Illinois e Department of Agricultural Economics, Purue University, West Lafayette, Iniana f Department of Soil, Environmental an Atmospheric Sciences, University of Missouri, Columbia, Missouri g Miwestern Regional Climate Center, University of Illinois at Urbana Champaign, Champaign, Illinois h National Weather Service Central Region, Kansas City, Missouri i Department of Agriculture an Applie Economics, University of Missouri, Columbia, Missouri USA j Department of Agronomy, an Department of Earth, Atmospheric, an Planetary Sciences, Purue University, West Lafayette, Iniana k Great Plains Agroclimate an Natural Resources Research Unit, Grazinglans Research Laboratory, Agricultural Research Service, USDA, El Reno, Oklahoma l School of Natural Resources, University of Nebraska Lincoln, Lincoln, Nebraska m Agricultural an Biosystems Engineering, South Dakota State University, Brookings, South Dakota n Purue University, West Lafayette, Iniana Receive 10 June 2013; accepte 21 October 2013 a Corresponing author aress: Eugene S. Takle, 2010 Agronomy Hall, Iowa State University, Ames, IA aress: gstakle@iastate.eu DOI: /2013EI

2 Paper No. 5 Page 2 ABSTRACT: Corn is the most wiely grown crop in the Americas, with annual prouction in the Unite States of approximately 332 million metric tons. Improve climate forecasts, together with climate-relate ecision tools for corn proucers base on these improve forecasts, coul substantially reuce uncertainty an increase profitability for corn proucers. The purpose of this paper is to acquaint climate information evelopers, climate information users, an climate researchers with an overview of weather conitions throughout the year that affect corn prouction as well as forecast content an timing neee by proucers. The authors provie a graphic epicting the climate-informe ecision cycle, which they call the climate forecast ecision cycle calenar for corn. KEYWORDS: Agriculture systems; Forecasting; Climate forecast; Seasonal forecast; Agriculture forecast; Decision calenar 1. Introuction The purpose of this paper is to create a communication tool for use by forecast evelopers an agricultural climate forecast users that will improve the usability an usefulness of climate information. Potential users of the graphic, in aition to crop proucers an climate forecasters, inclue agribusiness prouct an service proviers, extension agents, financial institutions, risk management organizations, an commoity traers. Like proucers an forecasters, these users will provie neee input relating to proucts, information, an timing. Many stuies have suggeste that improve climate information an forecasts can reuce the risk of economic losses, increase profits, an improve short- an long-term farm management ecisions (Crane et al. 2010; Meinke an Stone 2005; Meza et al. 2008). However, integration of climate information an forecasts into farm planning has been slow to occur for numerous reasons incluing a lack of forecast skill, usability of the information, an relevance of proucts to specific agricultural ecisions (Garbrecht an Schneier 2007). Here we acquaint climate information evelopers, climate information users, an climate researchers with an overview of weather conitions throughout the year that affect corn prouction as well as forecast content an timing neee by proucers. Our intent is to engage private an public sectors by integrating multiple time horizons of ecisions an time scales of climate an weather information more so than has been one in the past. The climate-base ecision cycle for corn graphically illustrates the time of year an lea time of climate forecasts that are relevant to specific agricultural ecisions. There is urgent nee for the climate forecast creators an information proviers to work more closely with ecision-tool evelopers an information users to improve climate information flow. Climate science researchers from universities an feeral laboratories have an opportunity to ientify an engage in strategic areas neeing further research on assessing preictability, characterizing an narrowing uncertainty, an elivering information for enhancing ecision making in relation to foo prouction. Two recent workshops, the Corn an Climate Workshop (Angel et al. 2009) an the Climate Preiction Applications Science Workshop (NOAA 2011), brought more clearly into focus the lack of publicly available agriculturally relevant information at scales neee for ecision making in agriculture, particularly for corn prouction in the Miwest. At these events the authors hel breakout sessions with

3 Paper No. 5 Page 3 farmers, representatives of farm organizations, agribusiness, insurance, finance, an agriculture extension staff. Forecast content, lea time, proucts elivere, an moes of elivery were iscusse. One theme emerging from these meetings was the nee for actionable information, whereby climate proucts an forecasts are translate into agriculturally relevant impact-specific terms for proucers an/or their avisors, for example, number of ays suitable for fiel work. This outcome is consistent with a growing number of calls for more actionable climate information in other regions (Crane et al. 2010; Dilling an Lemos 2011; Hammer et al. 2001; Hansen an Coffey 2011; Kerr 2011; Klopper et al. 2006). Another was the way that forecast uncertainty is conveye to information users. The climate forecast ecision cycle for corn is an outgrowth of iscussions from these events an follow-on meetings of the U.S. Department of Agriculture (USDA) Useful to Usable (U2U) project ( We iscuss the relationship between climate preiction, which we efine as being beyon the limits of a weather forecast (i.e., having a lea time of more than 2 weeks), an ecision making for foo-crop prouction. For the purpose of introucing the concept, we focus on one crop an one globally important prouction region to more clearly expose possible opportunities for using climate preiction to increase foo prouction. For these reasons we target growing corn in Iowa, which is locate in the central U.S. Corn Belt. 2. Corn an climate One attractive feature of corn as an agronomic crop is that it can be an has been aapte to a range of growing conitions: soils an climate. Furthermore, this crop continues to be improve through selection an breeing as well as management for increase yiels. Because of ifferent sensitivities at various stages of evelopment uring the course of the growing season, corn yiels respon to iniviual weather events as well as seasonal climate. Although many weather an climate conitions o not allow corn proucers to take preemptive action, climate forecasts have the potential to play a very large role in the ecision-making an planning process. The usefulness of stanar, currently provie projections of aily precipitation an temperature is limite. By contrast, specific an, in some cases, more integrate types of projections woul be more useful. For instance, a much more actionable piece of information is 3-month-avance forecast of the number of ays suitable for fiel work at planting time or the overwinter climate conitions that etermine the winter-long volatility of fall-applie nitrogen fertilizer. 3. Favorable an unfavorable growing conitions Development of actionable forecast information for corn proucers requires unerstaning of the climate impacts on corn growth an management operations. In many years, the amount of water require by the crop excees the total precipitation that falls uring the growing season. As a result, the ieal climate year for corn prouction in the U.S. Miwest starts with ample precipitation following fall harvest of the previous crop. This precipitation recharges soil moisture in the upper 2 m of soil, which provies a buffer against the lack of timely rains the following growing season. It also permits planting of high plant populations, which requires

4 Paper No. 5 Page 4 relatively more water. During the winter, frozen soils reuce loss of fall-applie nitrogen fertilizer. On the other han, if the soil is suitably ry an soil temperatures are below 508F, fall application of nitrogen fertilizer reuces chances for soil compaction an potential interference with spring planting activities. Weeklong ry spells uring the spring accelerate warming of the soil to the 508F threshol to allow planting without causing soil compaction from the weight of machinery. Abunant an timely but not excessive rain, as well as lack of late spring frost (or extene cool perios), allows rapi germination an early growth. Crop evelopment is promote by temperatures in the range of F uring the ay an F at night, lack of extene perios of clouiness, an once-a-week rains. These conitions allow winows of opportunity for spraying for pests an supplemental fertilizer application. Lack of early fall frost allows maximum see fill an kernel weight. Relatively ry conitions after maturity in late September permit the crop to ry own naturally, thereby reucing the nee for artificial grain rying after harvest. Weeklong perios without rain allow harvest without soil compaction. Low absolute humiity reuces chances of toxins eveloping in grain prior to harvest. Detrimental growing season conitions inclue extene perios with insufficient plant available soil water an air temperatures above 958F. The most vulnerable perio for high temperature is uring pollination, which normally occurs in mi- to late July. Growth is suppresse by perios of 10 ays or more without rain or shorter perios if soil moisture is low at the beginning of the growing season. Nighttime temperatures above 758F, high win, hail, an excessive rainfall all are etrimental to corn evelopment. 4. Decisions base on climate forecasts Corn proucers can use climate information for making ecisions every month of the year. Commoity markets allow farmers to sell corn at any time, even before the crop is plante. Marketing ecisions may require accurate forecasts of climate in regional, national, an international corn growing regions as well as forecasts of on-farm climate. Decisions on crop an cultivar selection, tillage an conservation practices, fertilizer an chemical application, an planting an harvesting options require climate information that, ieally, is at the fiel scale. For most efficient use of climate forecast information, the climate forecast eveloper nees to know the following: 1) Who makes the ecisions? 2) What types of ecisions are being mae? 3) What month an ay ecisions are being mae? 4) What month an ay oes the weather conition an/or ecision impact occur? 5) What specific meteorological variable or combination of variables relate to the consequences of this ecision? 6) Are forecasts of variables etermine, in item 5, available on calenar ates, in item 3, with neee lea times etermine from the time ifference between item 4 an item 3? 7) Are combinations of meteorological conitions at ifferent times important? Examples inclue successive ays of heavy rain an early win

5 Paper No. 5 Page 5 amage followe later by another stress (e.g., rought) that leas to aflatoxin infection. 8) Which forecast applications are highly site specific an which are regional an/or global? 9) What aitional tools are neee to translate meteorological variables into ecision ais such as a crop growth/yiel moel, soil compaction moel, soil erosion moel, an calculation of ays per week suitable for fiel work? 10) What ancillary biological or soil information is neee such as crop evelopment stage, plant physiology, soil fertility, terrain slope, wees, insects, an iseases to allow evaluation of both biotic an abiotic impact on the crop? 11) How are uncertainty metrics an terms associate with the forecasts evelope an conveye to the users: graphs, tables, pfs, or terciles of skill? For most efficient use of climate forecast information, the climate forecast user nees to know the following: 1) What is an acceptable level of accuracy for specific variables that will improve ecision making? 2) How can the uncertainty metrics an terms associate with the forecasts be interprete an use? 3) What is the consequence of a goo ecision versus a ba ecision, economically or environmentally, base on the forecast, an what is the consequence of no ecision or action? For improving the value of climate forecast information, the climate researcher nees to know the following: 1) What meteorological variables are neee to improve the climateinforme ecisions? Is there linkage of this information to remote, slow time-varying forcing such as ENSO, the Atlantic multiecaal oscillation (AMO), the Pacific ecaal oscillation (PDO), an soil moisture? 2) At what points in the annual or interannual ecision cycle are these variables neee? 3) How can past information best be collecte an archive for effective ata mining? 4) How can trens in technology be accounte for in relating past recors of yiel to climate-relate factors? 5. Climate forecasts beyon 1 year Climate forecast information with lea times of a year or more can be use by proucers an lan owners for ecisions on machinery purchases, marketing, refining annual rental contracts, an installing conservation practices such as grass waterways an for the use of cover crops. Decaal or longer climate projections can affect ecisions on lan purchase or sale, estate planning, or installation of irrigation or conservation structures such as riparian zone planting or rainage tile. 6. The corn forecast ecision cycle Figure 1 illustrates an example of the forecast ecision cycle for communicating to climate forecast proucers the nature an timing of forecasts that are useful to

6 Paper No. 5 Page 6 Figure 1. Climate-base ecision cycle for corn. The outer calenar ientifies the time of year management ecisions are mae. The inner calenar epicts the soil or crop impact, an the label on the arrow ientifies the weather or soil conitions relevant to the impact. Length of the arrow gives the lea time of climate forecasts that links the specific agricultural ecisions to soil or crop impacts. corn proucers. Decision calenars are commonly assemble in a matrix format to provie a listing of information in relation to timing of ecisions. The forecast ecision cycle provies a unique epiction of the cyclic an overlapping time horizons affecting the ecision process. The outer calenar in Figure 1 provies sample ecisions that are mae in certain months that require specific climate forecast information to be available at that time. The labele arrow inicates the climate-relate factor neee to make that ecision. The arrowhea lans on the inner calenar at the time that the soil or crop impact of the management action influence by climate forecast information occurs. The length of the arrow provies the issue ate an lea time neee for the forecast. Large arrows surrouning the outer calenar provie example ecisions that have climate forecast lea times of a half a year or more, typically beyon the normal range of seasonal climate forecasts. These ecisions coul be informe by trens in climate change or emerging new science on interannual variability an probability of extreme events, with the latter two likely having a larger impact than the climate change tren over one or a few years into the future.

7 Paper No. 5 Page 7 A ecision on fall application of nitrogen fertilizer (anhyrous ammonia) will illustrate use of the climate forecast ecision cycle. A farmer may be able to take avantage of favorable prices for purchasing fertilizer in the fall compare to the following spring. Fall application also can be complete at a time that oes not interfere with tillage or planting operations. The possible ownsie of fall application is that if soils are too warm an not frozen substantial nitrogen will be lost because of volatilization. So the farmer, making the ecision in November, nees a forecast of the likelihoo that soils will be continuously frozen or snow covere from shortly after application until about 1 March. For this ecision, the farmer is not concerne about air temperature, precipitation, wins, humiity, etc. Even a forecast that lacks skill in these variables will be useful in making this ecision if it is accurate for preicting frozen soils. As another example, a month-ahea forecast of wet soils in fall woul warn of harvest elay an interference with natural in-fiel grain ry own, possibly leaing to high-moisture grain at harvest. A elay in harvest ate or increase costs ue to the nee for artificial rying of grain will influence timing of crop elivery to market an expectations for profitability. There are follow-on climate-sensitive ecisions that arenotrepresente.for instance, the springtime ecisions of purchasing fertilizer an herbicies are connecte to the ecision to plant corn versus soybeans. Similarly, replant ecisions will change marketing options. Iniviual Miwest corn proucers an corn proucers outsie the central Miwest will require a slightly altere cycle of ecisions an timelines. Figure 1 is meant to be illustrative. Proucers of other crops can use this example to create calenars reflecting their own nees as a way of opening the ialog on climate nees with their climate services provier or, collectively, as feeback to the National Oceanic an Atmospheric Aministration (NOAA). 7. Summary We evelope a cyclic epiction of a corn forecast ecision cycle for an agricultural application: in our example to corn prouction ecisions in the central Miwest. In contrast to a tabular listing of ata, the cyclic calenar clearly reveals the timing, lea times, an variables of highest interest from climate forecasts throughout the year. Climate forecasts typically will have ifferent skill levels at ifferent times of the year an for ifferent variables. This cycle is intene to open the ialog between climate forecast proviers an climate forecast users on the linkage between specific forecast time of year, lea time, an combinations of variables with specific ecisions mae by proucers. This allows the skill of forecasts to be measure more irectly in terms of forecast use rather than conventional meteorological components such as pressure patterns, monthly precipitation, or monthly temperature. Through ialog among information proviers, information users, an the research community, climate services for this significant agricultural sector can be meaningfully enhance using a unique forecast ecision cycle perspective. The potential exists for this approach to be expane for other crops an regions. Acknowlegments. This report is a prouct of the project Useful to Usable (U2U): Transforming Climate Variability an Change Information for Cereal Crop Proucers, an

8 interisciplinary research an extension project involving 10 Miwestern universities supporte by USDA-NIFA Awar References Earth Interactions Paper No. 5 Page 8 Angel, J., an Coauthors, 2009: Corn an climate report: An overview of climate science in service of Miwest agriculture. U.S. Climate Change Science Program Rep., 67 pp. [Available online at Crane, T. A., C. Roncoli, J. Paz, N. Breuer, K. Broa, K. T. Ingram, an G. Hoogenboom, 2010: Forecast skill an farmers skills: Seasonal climate forecasts an agricultural risk management in the southeastern Unite States. Wea. Climate Soc., 2, Dilling, L., an M. C. Lemos, 2011: Creating usable science: Opportunities an constraints for climate knowlege use an their implications for science policy. Global Environ. Change, 21, Garbrecht, J. D., an J. M. Schneier, 2007: Climate forecast an preiction prouct issemination for agriculture in the Unite States. Aust. J. Agric. Res., 58, Hammer, G. L., J. W. Hansen, J. G. Phillips, J. W. Mjele, H. Hill, A. Love, an A. Potgieter, 2001: Avances in application of climate preiction in agriculture. Agric. Syst., 70, Hansen, J., an K. Coffey, 2011: Agro-climate tools for a new climate-smart agriculture. CCAFS Rep., 4 pp. Kerr, R. A., 2011: Aaptation to climate change. Time to aapt to a warming worl, but where s the science? Science, 334, Klopper, E., C. H. Vogel, an W. A. Lanman, 2006: Seasonal climate forecasts potential agriculturalrisk management tools? Climatic Change, 76, Meinke, H., an R. C. Stone, 2005: Seasonal an inter-annual climate forecasting: The new tool for increasing prepareness to climate variability an change in agricultural planning an operations. Increasing Climate Variability an Change, J. Salinger, M. V. K. Sivakumar, an R. P. Moth, Es., Springer-Verlag, Meza, F. J., J. W. Hansen, an D. Osgoo, 2008: Economic value of seasonal climate forecasts for agriculture: Review of ex-ante assessments an recommenations for future research. J. Appl. Meteor. Climatol., 47, NOAA, 2011: 9th Annual Climate Preiction Applications Science Workshop report. NOAA Rep., 32 pp. [Available online at 20Workshop_Final_low%20rez.pf.] Earth Interactions is publishe jointly by the American Meteorological Society, the American Geophysical Union, an the Association of American Geographers. Permission to use figures, tables, an brief excerpts from this journal in scientific an eucational works is hereby grante provie that the source is acknowlege. Any use of material in this journal that is etermine to be fair use uner Section 107 or that satisfies the conitions specifie in Section 108 of the U.S. Copyright Law (17 USC, as revise by P.IL ) oes not require the publishers permission. For permission for any other from of copying, contact one of the copublishing societies.

Calibration and Validation of the Hybrid-Maize Crop Model for Regional Analysis and Application over the U.S. Corn Belt

Calibration and Validation of the Hybrid-Maize Crop Model for Regional Analysis and Application over the U.S. Corn Belt Paper No. 9 Page 1 Copyright Ó 2015, Paper 19-009; 33841 wors, 7 Figures, 0 Animations, 5 Tables. http://earthinteractions.org Calibration an Valiation of the Hybri-Maize Crop Moel for Regional Analysis