Climate Forecasts for Corn Producer Decision Making

Takle, Eugene S.; Anderson, Christopher J.; Andresen, Jeffrey A.; Angel, James R.; Elmore, Roger W.; Gramig, Benjamin M.; Guinan, Patrick E.; Hilberg, Steven D.; Kluck, Doug; Massey, Raymond E.; Niyogi, Dev; Schneider, J. M.; Shulski, Martha; Todey, Dennis; Widhalm, Melissa

doi:10.1175/2013ei000541.1

Cited by 36 publications

(28 citation statements)

References 8 publications

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“…To bridge those differences, Hansen and Indeje (2004) discuss two pathways: (1) using daily-resolution weather input directly from climate models or from stochastic weather generators, and (2) applying crop yield models that operate on the basis of climatic predictor variables, such as ENSO. Takle et al (2014) also argue that higher spatial resolution is required to allow management decisions on a field-scale. -Decisions on crop and cultivar selection, tillage and conservation practices, fertilizer and chemical application, and planting and harvesting options require climate information that, ideally, is at the field scale‖ (Takle et al, 2014: 4).…”

Section: Applications and Benefits For Agriculturementioning

confidence: 99%

“…5) reflect this need, inform about critical crop stages, and highlight when weather and climate information can be particularly helpful in making crop-related decisions. For example, Table 3 and Figure 5 show that soil temperature information is particularly important for corn producers during winter when fertilizer is applied that could volatilize if temperatures are too high (Takle et al, 2014). Table 3 shows how production and marketing decisions for corn differ depending on leadtime and season.…”

Section: Forecast Demands From Producers and Agricultural Advisorsmentioning

confidence: 99%

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The development of seasonal climate forecasting for agricultural producers

Klemm

McPherson

2017

Agricultural and Forest Meteorology

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Body (7634 words) 1 Introduction Seasonal climate forecasts are used by crop producers for long-term decisions, for example planting and harvest timing, field fertilization, or water management, among others. To review the literature, the main methods used were keyword searches in publisher-unaffiliated databases such as Web of Knowledge and in publication libraries of institutions known for their interdisciplinary work in climate forecasting and agriculture, such as the Southeast Climate Consortium and the International Research Institute for Climate and Society. Section 1 outlines a brief history on the topic. Section 2 explores singlemodel and ensemble forecasts. Section 3 reviews measures of skill and utility to assess the value of seasonal climate forecasts. Section 4 puts seasonal climate forecasts in context with other agricultural decision factors. Section 5 looks at tailored seasonal climate forecasts and seasonal climate forecasts from an agricultural perspective, while exploring communication challenges between forecast provider and forecast user, and highlights how agricultural decision-making differs depending on the crop type and planning horizon. Lastly, section 6 explores end-to-end concepts.

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Section: Applications and Benefits For Agriculturementioning

confidence: 99%

Section: Forecast Demands From Producers and Agricultural Advisorsmentioning

confidence: 99%

The development of seasonal climate forecasting for agricultural producers

Klemm

McPherson

2017

Agricultural and Forest Meteorology

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“…Lobell and Burke () concluded that progress in understanding the magnitude of regional temperature changes is one of the most important needs for climate change impact assessments and adaptation efforts for agriculture. Monthly, seasonal, and interseasonal information is used for production decisions during the growing season, and multiyear or decadal information is used for long‐term decisions (Takle et al , ). The overall goal of our work is to document the characteristics of trends in maximum, minimum, and average temperatures, diurnal temperature range, as well as precipitation in the Midwest United States for the most recent climatological time period.…”

Section: Introductionmentioning

confidence: 99%

A spatiotemporal analysis of Midwest US temperature and precipitation trends during the growing season from 1980 to 2013

et al. 2015

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Since late 1970s, climate warming has been widely recognized. In the Midwest, farmers cannot rely on the normal calendar anymore, and it has become critically necessary to evaluate the most recent climate trends relative to growing season in order to conduct adaptation efforts for agriculture. Based on the homogenized historical monthly temperature and precipitation records during the period of 1980–2013 from 302 observing stations in the 12 Midwestern US states, we investigate the climate trends on four timescales: monthly, early growing season, late growing season, and the entire growing season. The climate metrics include maximum temperature, minimum temperature, average temperature, diurnal temperature range, and precipitation. Nonparametric Sen's Slope together with the nonparametric Mann–Kendall test is used to estimate the decadal trend and to detect the statistical significance. The results show that growing season average temperature has increased at a rate of 0.15 °C decade−1 over the Midwest United States. Within the growing season, minimum temperature is increasing faster in the early growing season, especially in June, while maximum temperature is increasing faster in the late growing season, especially in September. Spatially, statistically significant (p ≤ 0.05) growing season warming is more focused in the southern part of the region in the early growing season but in the northern part of the region in the late growing season. Over the Midwest, dominant trends in diurnal temperature range are decreasing during most months, with the exception of September. The majority of the locations show increasing trends in growing season precipitation, yet few are statistically significant. Furthermore, precipitation has been increasing in the early growing season but decreasing in the late growing season. This within‐season reversing trend in precipitation is found in 8 of 12 Corn Belt states: Illinois, Iowa, Michigan, Minnesota, Missouri, Nebraska, North Dakota, and Wisconsin.

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“…Corn, the primary US feed grain, accounts for around 500-600 billion tons of production in the US [41,42]. Most of the corn production occurs in the Corn Belt, a region in the US Midwest known for the ideal climate and soil conditions for crop production and intense farming characterized by high fertile soils, high organic soil concentration, timely rainfall, and ample solar radiation.…”

Section: Study Areamentioning

confidence: 99%

Evaluation of Hydroclimatic Variability and Prospective Irrigation Strategies in the U.S. Corn Belt

Alegría

Schütze

Niyogi

2019

Water

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Changes in climate, land use, and population growth has put immense pressure on the use of water resources in agriculture. Non-irrigated fields suffer from variable water stress, leading to an increase in the implementation of irrigation technologies, thus stressing the need to analyze diverse irrigation practices. An evaluation of 17 sites in the U.S. Corn Belt for two temporal climatic conditions was carried out. It consisted of the analysis of critical hydroclimatic parameters, and the evaluation of seven diverse irrigation strategies using the Deficit Irrigation Toolbox. The strategies included rainfed, full irrigation, and several optimizations of deficit irrigation. The results show a significant change in the hydroclimatic parameters mainly by increased temperature and potential evapotranspiration, and a decrease in precipitation with an increase in intense short rainfall events. Consequently, the simulations indicated the potential of deficit irrigation optimization strategies to increase water productivity above full irrigation and rainfed conditions. In particular, GET-OPTIS for wet soil conditions and the Decision Tables for dry soil conditions seasons. The present study highlights the contributions of atypical weather to crop production and the implications for future management options, and allows specialized regionalization studies with the optimal irrigation strategy.

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Climate Forecasts for Corn Producer Decision Making

Cited by 36 publications

References 8 publications

The development of seasonal climate forecasting for agricultural producers

The development of seasonal climate forecasting for agricultural producers

A spatiotemporal analysis of Midwest US temperature and precipitation trends during the growing season from 1980 to 2013

Evaluation of Hydroclimatic Variability and Prospective Irrigation Strategies in the U.S. Corn Belt

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