Extended range forecasting and the ENSO effects in the Corn Belt

Contingency tables are used to categorize Midwest monthly precipitation and maximum temperature to study persistence here. The percentage of times each category of the 2 x 2 contingency table persists for single month and several month periods is quantified climatologically using 102 years of data. Persistences reached as high as 50% in the warm months for cool-wet conditions. Winter persistence using this method is less accurate. Warm-dry or cool-wet conditions were the most dominant and persistent except for the southeast part of the region in winter. Here warm-wet or cool-dry conditions were most common. Temperature persistence, staying below or above the mean, reached near 70% for below mean temperatures during the summer and 65% for above mean temperatures during the winter. Precipitation persistence rarely reached the 60% level. Skill scores, as an improvement upon a climatological forecast, were commonly 10-20% for each cell and sometimes higher. Persistence skill is improved by classifying persistence based on the original climatic state;The same monthly climatological data are compared to the monthly Southern Oscillation Index (SOI) to measure effects of the El Nino/Southern Oscillation in the Corn Belt. Monthly extreme phase means are compared to the 102 year temperature and precipitation averages via Student\u27s t-test. Warm anomalies are found in the winter in the northern states and cool anomalies to the south during low phase of the SOI. Summer anomalies produced cooler and wetter conditions. Precipitation was increased to the west and decreased to the east. During the high phase of the SOI, opposite effects were seen in most areas. Significance varied, but many stations had values significant at the 0.05 and 0.01 level. Extreme ENSO phase months categorized via the contingency table displayed differences in the percentage of occurrence of ENSO events to the long-term averages. The percentage of occurrence of temperature and precipitation anomalies agreed with the t-test results. Same sign anomaly persistences (i.e. positive precipitation) were compared to long-term persistences. In Iowa cool conditions persist slightly more than average during El Nino events and dry conditions persist better than average during La Nina events

Climate Forecasts for Corn Producer Decision Making

Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn

Climate Forecasts for Corn Producer Decision Making

Corn is the most widely grown crop in the Americas, with annual production in the United States of approximately 332 million metric tons. Improved climate forecasts, together with climate-related decision tools for corn producers based on these improved forecasts, could substantially reduce uncertainty and increase profitability for corn producers. The purpose of this paper is to acquaint climate information developers, climate information users, and climate researchers with an overview of weather conditions throughout the year that affect corn production as well as forecast content and timing needed by producers. The authors provide a graphic depicting the climate-informed decision cycle, which they call the climate forecast–decision cycle calendar for corn.This article is from Earth Interactions 18 (2014): 1, doi:10.1175/2013EI000541.1. Posted with permission.</p

Using a team survey to improve team communication for enhanced delivery of agro-climate decision support tools

In the Midwestern United States, where a third of the world&apos;s maize crop is grown, there are few decision support tools available to help farmers and their advisors plan for an uncertain climatic future. Developing tools that are actually useful and usable to agricultural decision makers necessitates an interdisciplinary team of climate scientists, agronomists, computer scientists, and social scientists. With such diversity come varying levels of engagement (e.g. co-project director, student, technician, etc.) and experience working with farmers and/or serving in an official Extension capacity. Therefore working together to address this challenging issue is not straightforward. This paper reviews how a survey of a large interdisciplinary team working on developing decision support tools to ensure resilient maize production in this region identified differences between team members and helped improve team functioning and communication. Specifically the team survey revealed some important differences in how team members perceive farmers&apos; use of climate information, the types of decisions that should be addressed with a tool, and how such tools should function. These differences can be primarily explained by disciplinary background and project role and have provided valuable opportunities to learn from each other and build consensus on decision support tools developed. The survey as a feed-back tool complements other team communication approaches and reminds the team of the need for continuous communication and frequent discussion of assumptions