13, 2017 Paper and Poster Abstracts

22 Pages·2017·0.23 MB·English

2017 WERA-72 Annual Conference
Boise, Idaho
June 12 – 13, 2017

Paper and Poster Abstracts
Presentation #1

On-Farm Food Waste Recovery in Northern California
Michael J. Harwood and Gregory A. Baker
Food & Agribusiness Institute
Santa Clara University
Santa Clara, California

Problem Statement and Motivation
Food banks need hard evidence on how much edible fresh produce is rejected in-field because it fails to meet retail
market standards. Policymakers and non-profits must be convinced by solid species-by-species data if they are to
support salvage initiatives. In the long-term, engineering companies need encouragement to design robotic multi-
grade harvesting systems. Both approaches could cost-effectively improve diets of vulnerable populations.

The impetus for food waste studies in both the EU and USA over the past decade has been on identifying waste
management practices that reduce urban landfill disposal volumes. This focus essentially ignores farms, where
growers tend to plow in unsalable production or divert the product into low-value processing or on-site animal feed
use. Overall farm-level losses appear minor in rich countries. However, while this is true for the major staple food
commodities, fresh produce wastage and loss are a special case. Fresh produce marketing standards in the USA are
stringent. Large amounts of produce that would be good to eat, but do not meet size, shape, blemish or discoloration
specifications never enter market channels.

Objectives
The field survey research undertaken by FAI focuses on developing detailed numerical data on in-field food harvest
disposal volumes and percentages for key fresh produce items. This study is intended to establish a baseline for
produce recovery efforts and a model for further investigation in other crops and geographies. The specific
objectives of this study are:
 Conduct well-replicated field surveys immediately after harvest to establish the gleaning potential for leading
vegetable and fruit crops in the surrounding region;
 Identify and quantify major categories of left-behind produce for each crop; and
 Assess salvage potential and select candidates for further field survey completion in 2017.

Methods
Crop Selection: We targeted species where field sorting and packing were the norm. Labor cost increases and
vertical integration of supply chains have reconfigured the California produce industry. Industry contacts confirmed
that field sorting and packing by hand-harvest crews is now pervasive for most fresh horticultural produce.
Mechanical harvest systems predominate only for babyleaf lettuce and spring-mix, spinach, and the root crops –
carrot, onion, potato and sweet potato. We also considered factors including volume of production, seasonal harvest
patterns, and physical salvage potential in choosing the crops to study.

We designed our own field survey system, based on thorough examination of plots of a designated size that were
representative of field conditions using the following procedures.

Plot Siting and Demarcation: A production manager accompanied the team to the field. We sought to enter fields
while the harvesting was still in progress, whenever possible. The manager was asked to confirm that the general
area where we proposed to site our plot was typical of field conditions. We then moved into the field at least 20 feet,
to avoid edge-effects, and measured out the plot. The survey plot was demarcated by measuring set distances, most
often 50 feet, down boundary rows and placing poles with red flags at each end.

Plot Size: We assumed that the field survey areas would vary across species because of both differences in gleaning
effort and the weight of produce likely to be recovered. The harvest effort ranged from arduous stooping to separate
tomato, broccoli and strawberry from dense, low-hanging foliage, to sweet corn and artichoke, easily plucked at
waist height. Our aim was to work down a length of row until up to 10 lbs. of left-behind produce was gleaned. We
accounted for harvest-worker idiosyncrasies by ensuring that six or more parallel rows were included in the survey
plot.
Documentation Procedure: The gleanings from each row were carried back to a folding table. The load was sorted
by category, weighed, photographed and recorded on survey forms. Items were sorted by size ranges and various
physical defect categories, including ripeness, blemishes, bronzing and incomplete seed set (sweet corn).

Results

We found that the salvable produce varied greatly by crop. We have a large database, but the two figures below
summarize our results. Figure 1 shows the salvageable produce potential ranging from a few hundred kilograms per
hectare for artichokes to several thousand kilograms per hectare for Romaine hearts. When salvageable produce is
measured as a percent of the commercial harvest, tomatoes falls on the low end at about 5% of the commercial
harvest and Romaine hearts has by far the greatest amount left in the field on a percentage basis.

Figure 1. Salvageable Produce in Northern California Fields, FAI Field Survey, 2016

Figure 2. Salvageable Produce as a Percent of the Commercial Harvest in Northern California Fields, FAI Field
Survey, 2016

Implications
The field survey results provide good evidence that the physical potential exists to allow procurement of great
quantities of currently unutilized fresh produce for distribution to food bank clients. FAI intends to confirm these
findings by continuing the field survey work in 2017. Future research will focus on exploring how this potential
might be exploited by working with growers, packers, processors, food banks, and policymakers to develop
logistics, programs, policies, laws, and tax incentives to get salvageable produce out of the field and onto the table.
Presentation #2

Modelling rice farmers’ entry in a Crop Insurance program in Ghana

Addey K. A1*, Jatoe J. B. D2, Kwadzo G T-M2, Osei-Asare Y. B2

1 Graduate Research Assistant, Department of Agribusiness and Applied Economics, North Dakota State
University.
2 Department of Agricultural Economics and Agribusiness, CBAS, University of Ghana, Legon
* Corresponding author’s details:
Department of Agribusiness & Applied Economics
North Dakota State University
Email: [email protected] Tel: [phone]

Farmers increasingly are relying on crop insurance as a risk management tool. However, even where insurance
premiums are subsidized previous studies suggest low levels of farmer participation. For private providers, the
soundness and profitability of insurance programs is critical hence the need for knowledge of factors affecting
farmer participation. This study models a rice farmer’s purchase decision using the expected utility of profits
framework. Farmers’ participation and level of premium payment decisions are modelled using the double-hurdle
model. The study uses farm survey data from the Shai Osudoku district to investigate the factors that influence rice
farmers’ decision to participate in crop yield protection. The risk attitudes of the farmers are elicited with a risk
attitudinal scale. The study also investigates the influence of other important factors including the ratio of non-rice
to rice income, risk attitudes, reliability perception of crop insurance schemes among farmers and the probability of
failure to achieve target yields. The latter helps us address adverse selection, a central issue to the viability of an
insurance program. The study is timely given the recent introduction of yield-risk protection to crop producers in
other parts of the country in which indemnity payments that are made whenever yield falls below a predetermined
level. Whilst our model is static and focuses on new entrants it is very useful for estimating the potential demand for
crop insurance in this environment where it is new to farmers. Stakeholders such as providers of agricultural
insurance can use model results to assess the responsiveness of the demand for crop insurance to changes in
premium rates, among other factors.
Presentation #3

Supply and Demand Integrated Management Model for Effective Farmer- Cooperative Coordination: Case
of the Organic Milk Industry in the U.S.

Ye Su
301 C West Center, University of Nebraska-kearney
2504 9th Ave, Kearney, NE 68849
Phone: [phone]
Email: [email protected]

Michael Cook

This paper applies the integrated supply and demand model to the organic dairy industry and exams and
analyzes the successful experience of an important player in the industry. It provides pragmatic example for industry
practitioners.
Supply and demand integrated management focuses on resource allocation within a firm to balance the
supply and demand to better meet consumer demand and realize high margins. The key is the coordination of
marketing and production personnel inside a firm by its top leaders. Tate et al. (2015) proposed five aspects of
supply and demand integrated management: value creation for the organization and consumers; knowledge sharing
across the firm; resource allocation to the most important customers; experience accumulation; and balancing
capacity and demand. It is an inclusive procedure and involves change in organizational culture and politics. It is
important for a business to share information with its suppliers about consumer values and needs and collaborate
with suppliers to satisfy those needs.
Balancing supply and demand is a critical function for agricultural cooperatives to increase its pay price for
their farmers through increased cooperative level profitability. Organic Valley is an organic dairy cooperative to
provide such institution for its farmer members. By applying the integrated supply and demand model, Organic
Valley is able to maintain desired profitability level and maintain a stable pay price to its farmer members, which is
unusual in the pricing of agricultural commodities and cooperative supply chain management.
This paper exams the supply chain management system of Organic Valley with the framework of integrated
supply and demand model to find out: 1) how Organic Valley performs its integrated supply and demand
management; 2) what are the critical factors for successful integrated supply and demand management for Organic
Valley; 3) the impacts of the management model to farmer members; 4) implications for other marketing
cooperatives and agribusinesses to improve their overall performance.
The results show that Organic Valley balances its supply with demand through supply control and
marketing management. It only signs members when there is a market for its milk. Organic Valley manages its
demand through developing new products, improving old products, building brand reputation, and advocating
farmer identity. The key factors for the successful supply and demand integration management are significant
market power, farmer coordination, cooperative culture, and dedicated management teams. The most significant
impact of the integrated supply demand management on the farmer members is the stable pay price and economic
sustainability. At the same time, Organic Valley plays a yardstick effect in the organic dairy industry and have
profound effect on the price that other organic dairy farmers receive.
This paper provides a practical case for agricultural marketing cooperatives to improve their performance
and find alternative approach to increase their competitive advantages in their industries and serve their farmer
members better through integrated supply demand management.
Presentation #4

Examining the Financial Performance of Agricultural Cooperatives in the United States

Krishna P Pokharel1, Madhav Regmi2, Allen M Featherstone2, and David W Archer1
1 USDA- Agricultural Research Service, Mandan, ND
2 Kansas State University, Manhattan, KS

Agricultural cooperatives in the United States have gone through significant market fluctuations since 2005 due to
high commodity price volatility, increased competition, consolidations, mergers, and acquisitions. The number of
agricultural cooperatives decreased by almost 30% from 2005 to 2014; however, gross sales more than doubled and
market share is more concentrated. For example, the top 10 largest agricultural cooperatives (co-ops) accounted for
43% of co-ops’ gross business volume and 38% of total assets in 2014 (USDA, 2016). Further, during the same
period, farmer co-ops and investor co-ops engaged in strategic alliances, which may have changed the economic
structure of these co-ops (Reynolds 2012). These shifts in survival strategies caused farmer co-ops to become more
sensitive towards their financial performance and cost structure. In addition, USDA in 2014 projected that total farm
assets and equity growth continue to decrease in higher rate than in the previous decades. Real farm income was also
projected to decrease due to reduction in crop prices from record high crop production. As expected, these decreases
were reflected in the expectations of falling land values for a second year and increased borrowing costs for farmers.
Thus, a decrease in farm income and higher input costs for production might have led farmers towards non-real
estate financing in 2015. These shocks may also potentially increase the financial stress of agricultural co-ops due to
falling profitability and high interest rates.
The examination of financial performance is crucial to measure the overall financial health of agricultural
cooperative over a given period of time and also to compare them across the same industry. Past studies suggest that
co-ops with the lowest financial performance are less likely to meet their members’ need that ultimately leads them
to exit the industry (Moller, Featherstone, and Burton; 1996). According to Moller, Featherstone, and Burton (1996),
inadequate profitability, excessive debt, and/or high interest rates are the sources of financial stress for farmer co-
ops. Identifying the potential sources of financial stress for low performing co-ops provides guidance to the board of
directors and managers to reshape their strategic planning. Eventually, spillover of any improvement in the overall
financial performance of these co-ops benefits all the stakeholders.
There has been much analysis about the impact of these changes in production and pricing on farmers. Less
attention has been given in examining the impact of high grain prices and high production costs on agricultural co-
ops’ financial performance. It is therefore important to examine the financial performance of these co-ops in a new
economic environment. The objective of this research is to examine the impact of profitability, solvency (leverage),
and interest rates on agricultural co-ops’ financial performance. A logistic regression model is used to examine the
contribution of these three factors to co-ops’ financial performance. The second objective is to compare the financial
performance of co-ops for the state of Kansas to other U.S. co-ops by classifying these co-ops by size. The study of
financial performance by the size of cooperatives is important when the number of cooperatives decreases due to
mergers and consolidations.
A firm’s financial performance is measured by its geometric mean real rate of return on equity, which is the
rate of increase or decrease to the firm’s net worth over time. Return on equity is used to identify the sources of
financial stress. When the sources of financial stress are identified, we categorize the co-ops into two groups:
acceptable or unacceptable financial performance. Co-ops are categorized as financially unacceptable if their
geometric mean real rate of equity is negative. The outcome variable can be assigned a value of 0 (1) for
unacceptable (acceptable) performance, to examine the contribution of each factor such as profitability, debt, or
interest rate on financial performance.
Profitability is calculated as return on assets (income before interest rate and taxes) divided by total assets.
This measure provides information on whether a firm has difficulty in generating income. Profitability is chosen
because it is a more consistent and comprehensive measure of a firm’s performance (Moller, Featherstone, and
Burton; 1996). Solvency is calculated as debt divided by assets. Solvency and interest rate show difficulties in
financing assets with debt. The key findings of this study could be useful to stakeholders, managers, and decision
makers in identifying financial stress and improving financial performance of co-ops.
This research uses financial data from CoBank, a part of the Farm Credit System. CoBank loans to farmer
cooperatives and agricultural businesses across the United States. The majority of agricultural cooperatives are
located in Midwestern and a few agricultural cooperatives are located on the Eastern and Western regions of the
United States. The data include annual financial records with complete balance sheet and service statement from
audited financial statements of agricultural cooperatives.
The results show that low earning is the main factor for financial stress and smaller cooperatives are more
likely to face financial stress than larger cooperatives. Since the financial performance of larger cooperatives is
better than smaller cooperatives, mergers and acquisitions may likely to continue in the industry.

References
Moller, L.G., A. M. Featherstone, D. G. Barton. Sources of Financial Stress in Agricultural Cooperatives. Journal of
Cooperative, volume 11, 1996.
Reynolds, B. J. Joint Ventures and Subsides of Agricultural Cooperatives, Cooperative Programs, USDA Rural
Development, RBS Research Report 226, June 2012.
United States Department of Agriculture, Economic Research Service: accessed on Oct 27, 2016
http://www.ers.usda.gov/data-products/farm-income-and-wealth-statistics.aspx
Presentation #5

The Impact of Web Search Queries on Stock Market: Implications for Food Safety

Mahdi Asgari*
Agricultural Economics, Ph.D. candidate
University of Kentucky
302 Charles E. Barnhart Bldg.
Lexington, KY 40546
[phone]
[email protected]

Mehdi Nemati
Agricultural Economics, Ph.D. student
University of Kentucky
[email protected]

Each year, Food Safety News publishes top 10 most important foodborne outbreaks in the United States. According
to some estimations, 25 percent of total episodes of illnesses in the U.S. is due to foodborne illnesses. Centers for
Disease Control and Prevention (CDC) detects foodborne outbreaks through public health surveillance system which
takes 2-4 weeks. To accelerate the information transfer, “syndromic surveillance” has been vastly adopted by
national and international health organizations to monitor various infectious diseases as a complement to the
traditional system. In this method, internet search queries are correlated with the prevalence of a particular illness.
Previous studies demonstrated that web search volume could be used to predict the near real time and verified this
hypothesis in the context of economics and health decisions (Goel et al. 2010, Choi and Varian 2012, Garcia-
Herranz et al. 2014, Lazer et al. 2014, Varian 2014, Chunara, Andrews, and Brownstein 2012).
Our proposed research builds on the previous works which investigated the predictive power of search
volumes on the stock returns, trading volumes, and the investor attention (Andrei and Hasler 2014; Joseph et al.
2011; Bordino et al. 2010). Our objective is to test whether search engine data could help predicting the impact of
food safety shocks on stock market. To do so, we will use historical data on the stock price and trading volume for
leading companies in the food industry. Also, using Google Trends, we will obtain longitudinal query volume of
searches relating to these companies and food safety concerns for 2004 to present. Next, we investigate whether
search activity is a systematic indicator of stock price and trading volume by forecasting stock price returns and
trading volume returns using following equation 1. Price returns are defined by (cid:1877) (cid:3404)ln(cid:4666) (cid:1842) /(cid:1868) (cid:4667)*100, where p is
(cid:3036)(cid:3047) (cid:3036)(cid:3047) (cid:3036)(cid:3047)(cid:2879)(cid:2869) it
the stock price of industry i at time t. google trends index is defined by (cid:1850) which indicates the search index for
(cid:3036)(cid:3047)
search term i at time t.
(cid:3044) (cid:3031)
(cid:1877) (cid:3404)(cid:3533)(cid:2010) (cid:1877) (cid:3397)(cid:3533)(cid:2010) (cid:1850) (cid:3397)(cid:2020) (cid:4666)1(cid:4667)
(cid:3036)(cid:3047) (cid:3044) (cid:3036)(cid:3047)(cid:2879)(cid:3044) (cid:3031) (cid:3036)(cid:3047)(cid:2879)(cid:3031) (cid:3047)
(cid:3044)(cid:2880)(cid:2869) (cid:3031)(cid:2880)(cid:2868)
where q is number of lag returns for price return used in the model and d is number of lag search indexes used to
predict return at time t. In sample measures of model performance such as (cid:1844)(cid:2870) and (cid:1827)(cid:1835)(cid:1829) prefer models that best fit
existing data, rather than providing meaningful forecasts of out of sample returns. To conduct our out-of-sample
evaluation, we segment the underlying data into a training set and a prediction set. The training set is truncated at
time t, while the prediction set includes observations for only time p = t + , for time p prediction. For example, to
evaluate a model's ability to accurately predict 2016 returns, we use observations from 2004 to 2015 to build our
prediction models. Next, we use these models to predict returns in 2016. Then, we apply out of sample score
criterion which is mean square forecast error (MSFE).
The models with the lowest mean square forecast error will be the preferred models, and therefore will be
used to predict returns. The end result will not necessarily be a single predicted return for each food company.
Rather, we will use the top performing models to generate a range of predicted outcomes to capture uncertainty in
outcomes. However, a simple median or mean can be reported alongside a distribution of predicted outcomes for
each company.
References
Andrei, Daniel, and Michael Hasler. 2014. “Investor attention and stock market volatility.” Review of Financial
Studies: hhu059.
Bordino I., S. Battiston, G. Caldarelli, M. Cristelli, A. Ukkonen, et al. 2012. “Web Search Queries Can Predict
Stock Market Volumes.” PLoS ONE 7(7): e40014. doi:10.1371/journal.pone.0040014
Choi, H., and H. Varian. 2012. "Predicting the Present with Google Trends." Economic Record 88:2-9. doi:
10.1111/j.1475-4932.2012.00809.x.
Chunara, R., Jason R Andrews, and J. S. Brownstein. 2012. "Social and news media enable estimation of
epidemiological patterns early in the 2010 Haitian cholera outbreak." The American journal of tropical
medicine and hygiene 86 (1):39-45.
Garcia-Herranz, M., E. Moro, M. Cebrian, N. A. Christakis, and J. H. Fowler. 2014. "Using friends as sensors to
detect global-scale contagious outbreaks." PLoS One 9 (4):e92413. doi: 10.1371/journal.pone.0092413.
Goel, S., J. M. Hofman, S. Lahaie, D. M. Pennock, and D. J. Watts. 2010. "Predicting consumer behavior with Web
search." Proc Natl Acad Sci U S A 107 (41):17486-90. doi: 10.1073/pnas.1005962107.
Joseph, K., M. B. Wintoki, and Z. Zhang. 2011. “Forecasting abnormal stock returns and trading volume using
investor sentiment: Evidence from online search.” International Journal of Forecasting 27(4):1116-1127.
Lazer, D., R. Kennedy, G. King, and A. Vespignani. 2014. "The parable of Google Flu: traps in big data analysis."
Science 343 (6176):1203-1205.
Varian, H. R. 2014. "Big data: New tricks for econometrics." The Journal of Economic Perspectives 28 (2):3-27.
Presentation #6

LEGITIMACY OF LOCAL FOOD IN THE U.S. MARKET: COMPARATIVE CONSUMER
PERSPECTIVES

Ali Asgari and Tim Woods*
402 CE Barnhart
Department of Ag Economics
859-257-7270
[email protected]
* corresponding author

Measures of legitimacy have been described in the business literature in terms of four legitimacy types:
regulatory, normative, cognitive, and industry. This study provides a unique empirical examination of legitimacy
with an application to local foods, shedding light on how consumers view various types of legitimacy within these
markets. To apply the concept of legitimacy to local foods marketing, we utilize a national consumer survey asking
about perspectives of local food, together with questions regarding different shopping behaviors.
Using cumulative logit models, results of the legitimacy models suggest – not surprisingly - that core local
foods consumers are more likely to place a high value on the most of the legitimacy measures such as certifications,
freshness and quality, environmentally friendly practices and direct purchase from the producers. The value that the
core and to some extent the mid-level consumers place on different legitimacy measures have important implications
for the marketing, merchandising, and product positioning by marketers, grocers, and retailers that are selling
products with local characteristics.
The purpose of this study is to expand the application of legitimacy, defined in the business literature as a
“social judgment of acceptance, appropriateness, and desirability” (Zimmerman and Zeitz, 2002), to the concept of
local food by capturing consumers’ perception of local food, and comparing that to the drivers influencing their
purchasing decisions across different groups, considering their level of engagement to local food.
In order to understand the demand for local food, there has been an increased effort to understand consumer
purchasing behavior and their attitudes toward local food. In an imperfect market where information about their
behavior is limited, national surveys, and comparing local food demand alongside mainstream food systems helps
give a look into what drives consumers to buy locally. We bring legitimacy into the local food discussion since there
are grocery entities that have not traditionally been marketing food products as local, but noticed their consumers are
more interested in local foods than before and they might lose market share to farmers’ market or other natural foods
and local retailers like Whole Foods. Therefore, latecomers such as Walmart, Kroger, Meijer, and other big natural
grocery stores are trying to capture a larger portion of the local food market share, but first, they have to overcome
the liability of newness problem since they are new entrants into this market.
The second justification for looking at the issue of legitimacy and applying it to the context of local food,
markets, and institutions is that illegitimate product marketed as local can undermine the legitimate market. The
“market for lemons” argument (Akerlof, 1995) describes how the whole market could be influenced by consumers’
several bad experiences with one product. Likewise, some retailers in the food market that sell local foods that are
not legitimate could put a bad name on local foods in general, adversely impacting those retailers that are more
focused on local products. Consumers receive mixed signals in the marketplace when the local product is not
actually available or they experience false marketing. For instance, companies like Walmart want to appeal to the
customers that they care about the environment, sustainability, health care, communities, and so forth. Despite their
successful promotions and marketing campaigns, consumer protection groups believe that they are not as
environmentally driven as they claim and their efforts are greenwashing intended to change the image of the
company (Henderson and Weber, 2016). The rationale to look at these issues of legitimacy is the view to help
producers and vendors to move toward better overall market performance and to understand what consumers are
seeking with respect to different types and measures of legitimacy.
Implications and marketing recommendations are given based on the analysis of national primary food
consumer data collection in 2016. Logit models are utilized to examine determinants of representative legitimacy
elements, including consumer types.