William J. Waltman

Understanding Farmers’ Forecast Use from Their Beliefs, Values, Social Norms, and Perceived Obstacles*

Although the accuracy of weather and climate forecasts is continuously improving and new information retrieved from climate data is adding to the understanding of climate variation, use of the forecasts and climate information by farmers in farming decisions has changed little. This lack of change may result from knowledge barriers and psychological, social, and economic factors that undermine farmer motivation to use forecasts and climate information. According to the theory of planned behavior (TPB), the motivation to use forecasts may arise from personal attitudes, social norms, and perceived control or ability to use forecasts in specific decisions. These attributes are examined using data from a survey designed around the TPB and conducted among farming communities in the region of eastern Nebraska and the western U.S. Corn Belt. There were three major findings: 1) the utility and value of the forecasts for farming decisions as perceived by farmers are, on average, around 3.0 on a 0–7 scale, indicating much room to improve attitudes toward the forecast value. 2) The use of forecasts by farmers to influence decisions is likely affected by several social groups that can provide “expert viewpoints” on forecast use. 3) A major obstacle, next to forecast accuracy, is the perceived identity and reliability of the forecast makers. Given the rapidly increasing number of forecasts in this growing service business, the ambiguous identity of forecast providers may have left farmers confused and may have prevented them from developing both trust in forecasts and skills to use them. These findings shed light on productive avenues for increasing the influence of forecasts, which may lead to greater farming productivity. In addition, this study establishes a set of reference points that can be used for comparisons with future studies to quantify changes in forecast use and influence.

A GIS-based Spatial Pattern Analysis Model for eco-region mapping and characterization

Growing concerns about global climate change, biodiversity maintenance, natural resources conservation, and long-term ecosystem sustainability have been responsible for the transformation of traditional single resource management approaches into integrated ecosystem management models. Eco-regions are large ecosystems of regional extent that contain smaller ecosystems of similar response potential and resource production capabilities. They can be used as a geographical framework for organizing and reporting resource information, setting bioecological recovery criteria, extrapolating site-level management, and monitoring global change. The objective of this research is to develop a quantitative, multivariate regionalization model that is capable of delineating eco-regions at multiple levels from remotely sensed information and other environmental and natural resources spatial data. The Spatial Pattern Analysis Model developed in this study uses a region-growing algorithm to generate spatially contiguous regions from primitive polygonal land units. The algorithm merges the most similar pair of neighbouring units at each iteration, based on satisfying certain similarity criteria until all units are grouped into one. This model was utilized to develop an eco-region map of Nebraska with three hierarchical levels. In the mapping process, the STATSGO data set was used to build the primitive map units. Environmental parameters included in the model were multi-temporal AVHRR data, soil rooting depth, organic matter content, available water capacity, and long-term annual averages of water balance and growing degree day totals. Development of the model provides a new and useful approach to eco-region mapping for resource managers and researchers. The method is automated and efficient, reduces the judgement biases and uncertainty of manual analyses, and can be replicated for other regions or for the regionalization of other themes.

Geospatial decision support for drought risk management

Drought affects virtually all regions of the world and results in significant economic, social, and environmental impacts. The Federal Emergency Management Agency estimates annual drought-related losses in the U.S. at

Understanding the Influence of Climate Forecasts on Farmer Decisions as Planned Behavior

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The effect of organic carbon, temperature, time, and redox conditions on soil color.

8 billion, which is more than any other natural hazard. Congress enacted the Agricultural Risk Protection Act of 2000 to encourage the U.S. Department of Agriculture (USDA) Risk Management Agency (RMA) and farmers to be more proactive in managing drought risk.

Long-term nitrogen use and nitrogen-removal index in continuous crops and rotations

Results of a set of four regression models applied to recent survey data of farmers in eastern Nebraska suggest the causes that drive farmer intentions of using weather and climate information and forecasts in farming decisions. The model results quantify the relative importance of attitude, social norm, perceived behavioral control, and financial capability in explaining the influence of climate-conditions information and short-term and long-term forecasts on agronomic, crop insurance, and crop marketing decisions. Attitude, serving as a proxy for the utility gained from the use of such information, had the most profound positive influence on the outcome of all the decisions, followed by norms. The norms in the community, as a proxy for the utility gained from allowing oneself to be influenced by others, played a larger role in agronomic decisions than in insurance or marketing decisions. In addition, the interaction of controllability (accuracy, availability, reliability, timeliness of weather and climate information), self-efficacy (farmer ability and understanding), and general preference for control was shown to be a substantive cause. Yet control variables also have an economic side: The farm-sales variable as a measure of financial ability and motivation intensified and clarified the role of control while also enhancing the statistical robustness of the attitude and norms variables in better clarifying how they drive the influence. Overall, the integrated model of planned behavior from social psychology and derived demand from economics, that is, the “planned demand model,” is more powerful than models based on either of these approaches alone. Taken together, these results suggest that the “human dimension” needs to be better recognized so as to improve effective use of climate and weather forecasts and information for farming decision making.

No-till management intensity zones for Pennsylvania.

This study was undertaken to observe the effects of organic carbon content (3.2, 9.6, and 15.4 g kg−1), organic carbon quality (26 and 107 g kg−1 nonstructural carbohydrates), temperature (5, 15, and 25°C), and time (7, 14, 21, and 35 wk) on color changes in soil material (originally 7.5YR 5/6) under alternating oxidizing and reducing conditions in the laboratory. Hues of the mottle and matrix colors were strongly influenced by organic carbon content and ranged from 7.5 YR to 2.5Y as organic carbon was added. Temperature and time influenced matrix color and mottle hues to a lesser extent. Mottle and matrix color values were altered but did not exhibit any trends with organic carbon, temperature, or time. The chromas of the mottle and matrix colors were strongly influenced by organic carbon content. Mottle chromas were strongly influenced by organic carbon content. Mottle chromas ranged from 8 to 1 whereas matrix chromas ranged from 6 to 3. Temperature and time influenced mottle and matrix chromas to a lesser extent. The areal extent of color changes increased with organic carbon content, temperature, and time. The increased yellow (10YR and 2.5Y) components, higher values, and lower chroma of the colors was attributed to hematite dissolution, which allowed goethite and non-iron oxide minerals to influence the observed colors more strongly. A decrease in color values was attributed to new precipitates on existing oxide or silicate mineral surfaces.

Fuzzy feature-based image mining in remote sensing

Abstract Cropping systems, nitrogen (N) fertilizer levels, and climate largely dictate patterns of N use and influence problems arising from N fertilization. Nitrogen use was assessed in cropping systems with a nitrogen removal-index (NRI), defined as the ratio of N removed in the grain to total N supply including that from N fixation by legumes grown in rotation. Results are reported from analyses of NRIs of cropping systems that comprised a 12-yr continuous and sequential growing of maize [ Zea mays L.], soybean [ Glycine max. (L.) Merr.], sorghum [ Sorghum bicolor (L.) Moench], and oat/clover [ Avena sativa (L.)/80% Melilotus officinalis (L.) Lam., 20% Trifolium pratense ] in eastern Nebraska. Rotations involving maize or sorghum had higher NRIs than continuous cereals at 0 N application levels. Increasing N rates reduced NRI and resulted in an increase of residual nitrate in all but the continuous soybean system. Also, NRI was highest in continuous soybean, lower in continuous maize, and lowest in continuous sorghum. Rotations and lower N rates both contributed to higher NRI and lower soil residual nitrate. Biological windows that comprised the cumulative number of days in the entire year when soil is moist and temperature above a specific threshold correlated positively and significantly with NRI, whereas NRI and August temperature were negatively related. Between 43 and 87% of variability of NRI in maize and soybean systems was attributed to August temperature plus August precipitation index. Biological window (moist soil, temperature above 5°C ) plus May temperature explained up to 76% of variability of NRI of maize and soybean. Nitrogen removal index for sorghum was unrelated to weather variables. Estimated additions to the soil organic N reserve from the return of crop residues averaged between 16 and 80 kg ha −1 yr −1 with higher levels from sorghum and from all treatments with high levels of N fertilizer. Crop rotations generally increased the N-removal index, reduced the year-to-year variability in N-removal-index, and at 0 N-application rate, increased the return of N in residue to the soil N pool, compared to continuous cropping of single species.

Weather and Management Impact on Crop Yield Variability in Rotations

A classification of management intensity zones for no-till maize production was developed for Pennsylvania using GIS. Zones were based on analysis of Growing Degree Days, drainage characteristics, slope, water holding capacity of the root zone, and rock fragment content. Six zones were distinguished, reflecting the relative management requirements for notill. The map was produced as a tool for farmers and extension agents to recognize the challenges associated with no-till in their area.

Fragipans in Pennsylvania Soils 1

The problem of image mining combines the areas of content- based image retrieval (CBIR), image understanding, data mining and databases. Image mining in remote sensing is more challenging due to its multi-spectral and spatio-temporal characteristics. To deal with the phenomena that have imprecise interpretation in remote sensing applications, images should be identified by the similarity of their attributes rather than exact matching. Fuzzy spatio-temporal objects are modeled by spatial feature values combined with geographic temporal metadata and climatic data. This paper focuses on the implementation of a remotely sensed image databased with fuzzy characteristics, and its application to data mining. A comprehensive series of calibrated, geo- registered, daily observations, and biweekly maximum NDVI composite AVHRR images are processed and used to build the database. The particularity of the NDVI composite images that our experiments are conducted on is that they cover large geographic areas, and are suitable to observe seasonal changes in biomass (greenness). Based on the characterization of land cover and statistical analysis of climatic data related to NDVI, spatial and temporal data mining such as abnormality detection and similar time sequences detection were carried out by fuzzy object queries.