Ohio State University Extension On-Farm Energy Demand Monitoring Project

Chris Zoller –Extension Educator, ANR & Eric Romich- Extension Field Specialist, Energy Education

Greater automation on farms has resulted in an increase in energy consumption on many farms. Due to increased electrical usage, many farms are now billed on a commercial rate structure. Unlike residential rates, which are based primarily on total energy usage measured in kilowatt hours (kWh), commercial accounts are also charged for the highest peak demand usage spike over a short time period measured in kilowatts (kW).

Ohio State University Extension secured grant funding to investigate how peak energy demand affects livestock facilities and, in turn, the manner by which farmers can implement energy management strategies, and make investments in equipment to minimize costs and promote long-term sustainability. We have equipment installed on six university and/or private swine and dairy farms across the state. Monitoring equipment installation was finalized earlier this year and we have begun collecting data from each cooperating farm. OSU Extension personnel involved in the project include Eric Romich, Tim Barnes, Rory Lewandowski, Eric Richer, Dale Ricker, and Chris Zoller.

While we are have not collected enough data to make any specific recommendations, we have a few months of data collected that has provided us the opportunity to make sure our monitoring equipment is functioning properly. As data is collected, it is shared with faculty and students in the Ohio State University College of Computer and Electrical Engineering. Students and faculty in the college analyze the data to develop a model that will help us interpret the findings.

Click Here to Access Full Report Which Shows Results

Observations

Many farmers are aware if they are on a demand rate. However, fewer farmers fully understand the details of how their demand charges are calculated including monthly measured demand formulas, power factor correction penalties, and if they are charged a minimum monthly demand based on seasonal spikes. These specific electric rate details greatly influence possible solution strategies.

Based on the preliminary data, there appears to be some motor loads that can be shifted (load shifting) to perform work during times when other critical motor loads are idle, thus reducing demand charges. Ultimately, energy management strategies to reduce demand cost will likely include a mixture of energy conservation, energy efficiency technologies, programmable logic controls and timers to preform load shifting, and possible on-site electric generation.

Summary

Obviously, farmers are interested in ways to reduce energy operational cost. However, before making investments in energy efficiency and renewable energy equipment, it is important to understand how you are charged for electricity. Some farms are still on residential electric rate tariffs and their bills are relatively easy to understand. However, because farms are using more electric, many farms are now on commercial electric rate tariffs that are more complex. Taking the time to investigate your rate tariff and analyze your consumption patterns will help you prioritize potential energy savings solutions, providing you the greatest return on your investment.

It’s almost that time of year … Don’t forget to calibrate your yield monitor!

Source: John Barker, OSU Extension – Knox County

Remember the old adage … Garbage in = Garbage out. Many of us use our yield data to make additional management decisions on our farms such as hybrid or variety selection, fertilizer applications, marketing, etc. Data from an uncalibrated yield monitor can haunt us for many years by leading us into improper decisions with lasting financial affects. In today’s Ag economy we can ill afford any decision with adverse financial implications.

The two biggest reasons I usually hear for not calibrating a yield monitor are 1) I just don’t have time to do it or 2) I can’t remember how to do it without getting my manual out. While i know it’s easy to criticize from “the cheap seats”, I would argue that this could be some of the most important time you spend in your farming operation each year. Like many other tasks on our farm, the more we do it, the easier it gets. To learn more read 2018 Yield Monitor Calibration

Understanding the Generational Differences

by: Chris Zoller, Extension Educator, ANR in Tuscarawas County

We hear about and read labels for different generations and we know there are differences among them.  What do the differences mean if you are managing people from different generations?  Depending upon the publication you read or with whom you speak, there may be a slight difference in birth start and end years, but the following table provides some general guidelines.

Generation Name Births Start Births End Age Range
Baby Boomer 1946 1964 72 – 54 yrs. old
Generation X (the lost generation) 1965 1985 53 – 33 yrs. Old
Generation Y (Millenials) 1980 1994 38 – 24 yrs. Old
Generation Z

(the unknown)

1995 2012 23 – 6 yrs. Old
Generation Alpha 2013 2025 5

 

Each generation has its thoughts, beliefs, and ideals with respect to a number of items.  What are the differences with respect to employment?  It’s not accurate or fair to say that every person who falls into a particular generational category is the same.  However, general statements can be made about each generation.

Generational Differences:

  Baby Boomers Generation X Generation Y
Business Focus Long Hours Productivity Contribution
Work Ethic & Values Loyal

Question authority

Strive to be their best

Value ambition, collaboration, equality, personal growth, & teamwork

Work efficiently

Want respect from younger workers

Willing to take risks

Care more about work/life balance

Work/family balance is important

Like a casual work environment

Outcome oriented

Output focused

Rely on technology

Work ethic no longer mandates 10 hr. work days

Criticized for not being loyal to a particular job/employer

Believe technology allows them to work flexibly

Work ethic no longer mandates 10 hr. work days

High expectation to be mentored

Goal oriented

Looking for meaningful work

Obsessed with career development

Prefer diversity, informality, technology, and fun

Thrive on collaboration

Training is important

Preferred Work Environment Humane

Equal opportunity

Warm, friendly

Functional, positive, & fun

Fast paced & flexible

Access to leadership

Access to information

Collaborative

Creative

Positive

Diverse

Fun, flexible, want continuous feedback

 

Work is…

 

Exciting

A career

Work & then retire

 

Difficult challenge

Just a job

 

A means to an end

Fulfillment

Flexible work arrangements

 

What They are Looking for in a Job

Ability to “shine”

Make a contribution

Team approach

Need clear and concise job expectations

Dynamic leaders

Cutting edge with technology

Flexible scheduling

Input valued on merit, not age/seniority

Must see the reason for the task

Want to be challenged

Treated with respect

Friendly environments

Flexible scheduling

Expect to be paid well

Want to make a difference

As a product of the “drop down and click menu”, may need to be given options

Work Ethic Driven

Workaholic – 60 hr. weeks

Quality

Balance

Not work long hours

Self-reliant

Skeptical

Ambitious

What’s next?

Multitasking

Entrepreneurial

View on Work/Life Balance Hesitant to take time off – result is an imbalance between work & family More focus on maintaining a balance

Don’t worry about losing their place if they take time off

Flex time, job sharing

Balance work, life, and community involvement

(Source: www.wmfc.org/uploads/GenerationalDifferencesChart)

 

So what does this mean for agricultural employers?

  • The Baby Boomer generation is reaching retirement age.
  • Generations X and Y have a different outlook on work and family life as compared to previous generations. The more recent generations place a greater value on maintaining a balance between family and work.  Workers in these generations are less likely to willingly work extra hours.  They are not workaholics like the Baby Boomer generation.
  • Flexibility is a key word when it comes to Generation X and Y. Members of this generation want to be able to attend their son or daughter’s baseball game or have dinner with their family and then return to work.
  • Money may not be the motivating factor for some in Generation X or Y. Members in these groups often want flex scheduling, to collaborate with others, and not perform routine tasks.
  • Generations X and Y have a greater focus on technology. This can be a real plus to a farm as the use of technology grows.  These generations are much more familiar with and accepting of technology.
  • Generations Z and Alpha are too young to make any conclusions. However, we do know that these generations are heavily focused on technology.  Stay tuned…

The article is an introduction to the topic of understanding the differences across the generations.  Each generation brings with it challenges and opportunities.  As you think about your next employee or the next generation to enter your business, what factors must you consider? Use the information provided here as you plan for additions to your farm team.

(Sources: www.wmfc.org/uploads/GenerationalDifferencesChart; https://www.forbes.com/sites/deeppatel/2017/09/21/8-ways-generation-z-will-differ-from-millennials-in-the-workplace/#34be355576e5)

(Note: This article was published originally in the Farm and Dairy, July 26, 2018)

Ohio Farm Custom Rates 2018

Part 1: Soil Preparation, Fertilizer Application, Spraying Pesticides, Mechanical Weed Control, Aerial Applications, Planting Operations, Harvest Operations, Grain Drying and Storage, Hay Harvest

by: Barry Ward, Leader, Production Business Management, Department of Agricultural, Environmental and Development Economics & John Barker, Extension Educator Agriculture/Amos Program, County Director, Ohio State University Extension Knox County

Farming is a complex business and many Ohio farmers utilize outside assistance for specific farm-related work. This option is appealing for tasks requiring specialized equipment or technical expertise. Often, having someone else with specialized tools perform a tasks is more cost effective and saves time. Farm work completed by others is often referred to as “custom farm work” or more simply, “custom work”. A “custom rate” is the amount agreed upon by both parties to be paid by the custom work customer to the custom work provider.

Ohio Farm Custom Rates

This survey summary reports custom rates based on a statewide survey of 352 farmers, custom operators, farm managers, and landowners conducted in 2018. These rates, except where noted, include the implement and tractor if required, all variable machinery costs such as fuel, oil, lube, twine, etc., and the labor for the operation.

Some custom rates published in this study vary widely, possibly influenced by:

  • Type or size of equipment used (e.g. 20-shank chisel plow versus a 6-shank)
  • Size and shape of fields,
  • Condition of the crop (for harvesting operations)
  • Skill level of labor
  • Amount of labor needed in relation to the equipment capabilities
  • Cost margin differences for full-time custom operators compared to farmers supplementing current income

Some custom rates reflect discounted rates as the parties involved have family relationships or are strengthening a relationship to help secure the custom farmed land in a cash or other rental agreement. Some providers charge differently because they are simply attempting to spread their fixed costs over more acreage to decrease fixed costs per acre and are willing to forgo complete cost recovery.

The measures shown in the summary tables are the summaries of the survey respondents. The measures are the average (or mean), range, median, minimum, and maximum. Average custom rates reported in this publication are a simple average of all the survey responses. Range identified in the tables consists of two numbers. The first is the average plus the standard deviation, which is the variability of the data from the average measure. The second number of the range is the average minus the standard deviation. The median represents the middle value in the survey responses. The minimum and maximum reported in the table are the minimum and maximum amounts reported from the survey data for a given custom operation.

The complete summary of part 1 is available online at the Farmoffice website:

https://farmoffice.osu.edu/farm-management-tools/custom-rates-and-machinery-costs

 

 

 

Economic Contribution of Agricultural and Food Production Cluster to Ohio Economy – County Level Analysis

 

Contributors: Ben Brown, Ryan Brune, Connor Frame, and Megan Ritter

Click here for the entire PDF Article for the County Level Report

In November of 2017, researchers in the Department of Agricultural, Environmental, and Development Economics released The Economic Contribution of Agricultural and Food Production to the Ohio Economy report with analysis of Ohio’s entire Agricultural and Food Production Cluster. Details of that report are included, but this serves as a parallel analysis of agriculture to each of Ohio’s eighty-eight counties. Key results match initial assumptions in those counties with large concentrations of equipment manufacturing, professional services and diary & milk production led total economic contribution by the production agriculture subsector. In addition, counties containing relatively high food processing see the largest total sector contributions, and that counties with relatively small populations experience a higher percentage of employers involved in food and agriculture related careers. Large population centers within Cuyahoga, Franklin and Hamilton counties produced high economic contributions, but had low total population participation in agriculture. Data obtained from IMPLAN, a North Carolina based economic software company, provided the most recent total values, while the North American Industry Classification System was used to determine the percent agriculture contributed to each sector. The IMPLAN model estimates value added for 536 separate subsectors within Ohio’s economy. Unlike the statewide report, these county level calculations do not include the contribution from restaurants and bars. It also includes Farm Inputs, Equipment and Farm Professional Services in the agricultural production subsector.

Key findings in the statewide report: Ohio’s Agricultural and Food Production Cluster plus Restaurant and Bars account for $1 in every $13 of Ohio’s GSP and 1 in 8 jobs in Ohio. Each county differed in these ratios, but as expected large population counties were negatively correlated with small population counties in economic contribution and percentage of workforce involved in agriculture. The total statewide economic value added contribution of the Agricultural and Food Production Cluster minus Restaurants and Bars was $32.5 billion dollars and accounted for a little over 5 percent of the state’s gross state product. Value added being the sum of sales minus input costs for each sector. Example: corn production minus seed, fertilizer, ext. The sector employed 402,874 Ohioans in 2015 and because of purchases outside the cluster; a multiplier of 1.6 was used for every dollar of valued added making the total contribution $53 billion. Multipliers are a way of capturing the money spent within Ohio made from an agricultural sector that is then used to purchase additional products, like household items, into the economic contribution.

Declining commodity prices for corn, soybeans and milk in recent years have lowered the value added contribution of some counties, especially those that have corn, soybeans and milk ranked in the top three subsectors. Other subsectors including fruit and vegetable production have shown an increase to the value added contribution. Along with decreasing commodity prices, increasing productivity due to technology advancements have correlated with a decrease in employment within agriculture and food production. Ohio’s characteristic as a top agriculture producing state remains strong, but external factors like increasing pressure on land values could be seen as a potential challenge for the production agriculture subsector.

The three main divisions of the Agricultural and Food Production Cluster: Agricultural Production, Agricultural and Food Processing and Food Wholesale/Retail are included in Table 1 with subsectors broken out under their respective division. Different from the statewide report is the inclusion of Farm Inputs, Equipment and Professional Services under the division of Agricultural Production instead of an isolated division.

Table 1: Classification of Sectors

Agricultural Production Agricultural and Food Processing Food Wholesale/ Retail  
Farm Inputs, Equipment and Professional Services Processed Meat, Fish, Poultry & Eggs Food and Forestry Wholesale
Dairy Cattle and Milk Production Dairy Processing Food and Forestry Retail
Beef Cattle Production

 

Processed Food & Kindred Products
Poultry & Egg Production

 

Grain Milling & Flour
Hogs & Other Farm Animals Fats & Oils Processing
Grain Production

 

Beverage Processing
Soybeans & Other Oil Seeds Wood/ Paper/ Furniture Manufacturing
Misc. Crops, Hay, Sugar, Tobacco & Nuts
Fruit & Vegetable Production
Greenhouse, Nursery & Floriculture Production
Forestry, Hunting & Fishing
Sum of Agriculture Production Sum of Food Processing Sum of Food Wholesale/ Retail Total Agricultural and Food Production Cluster

 

Starting with Total Value Added from the Agriculture and Food Production Cluster it is not surprising to see in Figure 1 that the top five counties also match five counties with large population centers. With Franklin, Hamilton, and Cuyahoga counties being the location of Columbus, Cincinnati, and Cleveland respectively, it was expected and found that the contribution of production agriculture in terms of both value added and employment was the smallest division contributor, with food processing being the largest contributing division in Franklin, Hamilton, Butler, and Stark Counties. Food wholesale/ retail was the largest contributing division for Cuyahoga County.  Statewide, the food processing sector was the largest contributing division at $14,986 million and 2.43% of the states’ Gross State Product (GSP).

Franklin County had a high food processing contribution due to the beverage processing sector at $916 million. Notable companies in the area include Anheuser-Busch Companies Inc., BrewDog USA, Coca-Cola and others according to the Columbus Economic Development Annual Report. Employment within the Cluster was also largely contributed from the beverage processing subsector. For Hamilton county, the beverage processing subsector was also the largest contribution to the food processing division. Boston Beer Company, the parent company of Sam Adams Beer, and The J.M. Smuckers Co., parent company to Folgers Coffee are major contributors to the subsector. Boston Beer Company produces 20 percent of all Sam Adams Beer within Hamilton County. Cuyahoga County was the lone county in the top five where the top contributing division was Food Wholesale/Retail. Multiple subsectors in this division contributed to the large value, but noticeable was the smaller value for the beverage processing subsector in the Food Processing division. Analysis was not conducted across all 88 counties, but based on the top total value added counties, counties with large beverage processing subsectors had food processing divisions that made up the largest portion of the county’s Agricultural and Food Cluster contribution. While Cuyahoga, Franklin and Hamilton Counties are only 3 out of 88, the population represents roughly 29 percent of Ohio’s population based on U.S. Census Bureau data and make up a large portion of the Cluster’s impact to Ohio.

Figure 1: Top Five Value Added Counties

In Figure 1, counties producing the largest total values of economic contribution from agriculture and food were identified, and it isn’t surprising that counties with relatively large total economies also had the largest contributions of agriculture. However, in none of the top five producing counties was production agriculture the top contributing division. To look at the relative value of production agriculture to a county’s economy we can use the value added from agricultural production as a percent of the counties total economic output and indeed counties with larger agricultural output in regards to the National Agricultural Statistics Service (NASS) do rise to the top.  However, this should not be interpreted as the five counties with the largest total value contribution from production agriculture. The 2016-2017 Ohio Agricultural Statistics Annual Bulletin shows that land use for agricultural purposes in Mercer, Darke, Paulding, Putnam and Union Counties are 93%, 89%, 83%, 99% and 88% respectively, where land use is the sum of cropland, pastureland, and woodland. Figure 2 illustrates where the five counties lie within Ohio.

Figure 2: Top Five Counties

County agriculture contribution profiles for Mercer and Darke counties were similar as both counties had the same two subsectors contributing the majority of value added products to the county economies: Poultry & Egg Production and Pork Production. For Paulding and Putnam counties there was not a specific subsector that stood above the rest, but more of a balanced distribution. Soybeans & Other Oil Crops had relatively high values for both counties. In contrast, Union County had a top contributing subsector of Farm Inputs, Equipment & Other Professional services that made up 9% of the entire counties economy. This subsector made up 90% of the contribution of the Agricultural and Food Cluster.

While one indication of contribution to a county’s economy is through the value added calculation, another indicator is the number of people employed with-in the Cluster. Similar to the total contribution illustration above in Figure 1, counties with high food processing and relatively large populations also have the largest total number of employment in agriculture, but have a low percentage in relation to the entire county population. Figure 3, identifies the five counties with the highest percentage of the population involved in the Agriculture and Food Cluster. As seen above, Franklin County had the largest total value added to the economy and the highest employment at almost 38 thousand people, but represents roughly 4% of the counties workforce. Whereas Jackson County did not make the top five in total value added contribution, but has 25 percent of its workforce involved in the Cluster.

Figure 3: Percent of Population involved in Agriculture and Food

Summary

Understanding components of the statewide economy are important, as trends within the sector help identify strengths and weaknesses. However, county analysis helps those within and around the industry become stronger more informed decision makers in issues relevant to the Agricultural and Food Production Cluster. Not surprising, counties with larger populations had the highest total value added contribution to the county’s economy and the highest number of employees within the work force, but had lower percentages of the county total in values and employees to those counties with small populations. In counties with large value added from the entire cluster, Food Processing was the largest contributing division for the majority of counties in the top five. A strong beverage-processing subsector helped elevate the Food Processing division for these counties.  Isolating the Production Agriculture division including Farm Inputs, Equipment and Professional Services as a percent of the county’s total economy identified five counties that have relatively high land use in agriculture and high total sales from agriculture commodities.

Individual county fact sheets for all eighty-eight Ohio counties are listed here:

https://aede.osu.edu/research/osu-farm-management/agricultural-impact/contribution-agriculture-county

Appendix I. includes a list of counties and their value of total contribution, value of production agriculture contribution, and employment. State rankings are in parentheses.

References:

“Columbus Region: Food and Beverage.” Columbus 2020, 2017.

DiCarolis, Janice. et al. The Economic Contribution of Agricultural and Food Production to the Ohio Economy. 2017.

IMPLAN. 2017. 2015 Ohio state data package. www.implan.com

Turner, Cheryl, and Brooke Morris. Ohio Agricultural Statistics 2016-2017 Annual Bulletin. USDA, National Agricultural Statistics Service, 2017.

US Census. 2017a. County Business Profiles. https://www.census.gov/programs-surveys/cbp.html

  Agriculture Production Value Added Ag Production % of Employment Total Cluster Value Added Total % of Employment
Adams $26,132,407 (72) 10% (5) $56,773,236 (77) 14% (13)
Allen $78,125,934 (19) 2% (65) $319,126,539 (24) 7% (64)
Ashland $74,074,340 (24) 5% (36) $184,902,491 (48) 10% (38)
Ashtabula $46,313,267 (52) 3% (56) $170,069,071 (50) 8% (59)
Athens $10,832,931 (82) 2% (63) $84,474,544 (69) 6% (74)
Auglaize $71,793,513 (25) 4% (39) $265,307,529 (34) 10% (33)
Belmont $48,773,321 (50) 3% (58) $145,203,550 (55) 8% (54)
Brown $32,761,777 (68) 7% (20) $61,715,467 (76) 11% (28)
Butler $49,768,789 (48) 1% (81) $1,323,431,575 (4) 6% (73)
Carroll $27,087,761 (71) 6% (22) $52,115,212 (78) 10% (37)
Champaign $41,533,589 (62) 5% (28) $106,563,182 (65) 11% (30)
Clark $49,500,983 (49) 2% (69) $287,447,821 (29) 7% (62)
Clermont $38,145,667 (65) 2% (70) $290,746,554 (27) 6% (75)
Clinton $53,920,762 (42) 4% (42) $132,673,146 (57) 8% (51)
Columbiana $56,804,685 (35) 3% (54) $264,737,172 (35) 9% (41)
Coshocton $60,830,386 (31) 7% (17) $187,589,390 (47) 15% (7)
Crawford $56,705,986 (37) 4% (40) $94,785,325 (67) 8% (57)
Cuyahoga $97,944,901 (9) >1% (86) $2,870,230,295 (3) 4% (88)
Darke $239,806,461 (4) 8% (12) $301,799,993 (25) 12% (23)
Defiance $74,738,470 (21) 5% (33) $132,202,917 (58) 9% (43)
Delaware $71,115,273 (26) 1% (78) $414,656,942 (15) 5% (81)
Erie $40,597,271 (64) 2% (62) $168,143,020 (51) 6% (68)
Fairfield $57,092,509 (34) 2% (64) $286,483,386 (30) 7% (66)
Fayette $41,430,653 (63) 4% (44) $203,165,951 (45) 13% (19)
Franklin $163,203,968 (5) > 1% (87) $4,233,913,386 (1) 4% (86)
Fulton $74,574,695 (22) 5% (34) $195,829,037 (46) 10% (32)
Gallia $15,592,444 (77) 6% (27) $37,233,957 (81) 8% (49)
Geauga $56,208,056 (38) 3% (61) $237,554,367 (40) 7% (63)
Greene $43,688,591 (56) 1% (77) $215,452,629 (43) 4% (83)
Guernsey $20,965,101 (75) 6% (26) $74,530,511 (72) 10% (35)
Hamilton $111,589,093 (8) >1% (88) $3,094,701,906 (2) 4% (85)
Hancock $56,118,896 (39) 2% (67) $385,962,349 (18) 9% (46)
Hardin $82,800,471 (14) 8% (13) $138,666,355 (56) 15% (11)
Harrison $10,621,659 (83) 7% (18) $28,975,047 (84) 13% (16)
Henry $54,239,652 (41) 6% (24) $334,766,774 (21) 18% (3)
Highland $45,616,926 (55) 9% (9) $76,818,531 (71) 13% (18)
Hocking $6,799,259 (87) 5% (32) $47,538,393 (79) 12% (22)
Holmes $132,411,907 (7) 7% (16) $385,876,069 (19) 22% (2)
Huron $89,862,265 (11) 5% (37) $324,490,460 (22) 11% (26)
Jackson $22,937,105 (74) 4% (47) $239,977,669 (39) 25% (1)
Jefferson $9,879,886 (84) 2% (71) $79,391,785 (70) 6% (69)
Knox $50,521,887 (47) 5% (30) $122,081,622 (62) 10% (34)
Lake $82,942,001 (13) 1% (80) $630,592,252 (10) 6% (76)
Lawrence $6,325,381 (88) 4% (41) $40,616,956 (80) 8% (55)
  Agriculture Production Value Added Ag Production % of Employment Total Cluster Value Added Total % of Employment
Licking $80,959,369 (17) 3% (57) $290,176,991 (28) 7% (61)
Logan $47,525,570 (51) 4% (45) $129,164,871 (61) 8% (58)
Lorain $75,209,443 (20) 1%  (73) $376,334,667 (20) 5% (78)
Lucas $65,557,760 (29) >1% (84) $745,401,227 (9) 4% (87)
Madison $69,435,722 (27) 4% (38) $113,507,126 (64) 8% (53)
Mahoning $43,627,477 (57) 1% (82) $413,002,404 (16) 5% (80)
Marion $82,089,222 (16) 3% (50) $261,902,767 (36) 10% (36)
Medina $67,362,783 (28) 2% (72) $492,849,630 (13) 6% (67)
Meigs $12,179,096 (81) 9% (6) $22,478,977 (87) 13% (17)
Mercer $287,020,607 (2) 7% (15) $486,428,489 (14) 16% (5)
Miami $41,628,715 (61) 3% (59) $320,490,163 (23) 9% (44)
Monroe $13,856,148 (79) 12% (2) $21,979,543 (88) 15% (12)
Montgomery $53,434,618 (43) >1% (83) $965,102,826 (8) 4% (84)
Morgan $13,011,573 (80) 9% (8) $23,902,013 (86) 14% (14)
Morrow $42,743,432 (59) 9% (7) $70,494,294 (73) 12% (20)
Muskingum $30,721,596 (70) 3% (55) $272,726,649 (33) 8% (47)
Noble $8,823,935 (85) 10% (4) $29,242,985 (83) 16% (6)
Ottawa $42,511,100 (60) 4% (43) $89,869,973 (68) 8% (56)
Paulding $54,709,543 (40) 12% (1) $66,561,674 (75) 15% (9)
Perry $14,860,292 (78) 8% (14) $30,646,479 (82) 12% (24)
Pickaway $58,449,378 (33) 5% (29) $104,937,335 (66) 9% (42)
Pike $20,526,229 (76) 4% (48) $69,036,918 (74) 11% (25)
Portage $33,902,467 (66) 1% (75) $282,939,009 (31) 5% (79)
Preble $52,009,567 (46) 9% (10) $171,486,943 (49) 15% (10)
Putnam $145,093,953 (6) 10% (3) $299,022,297 (26) 13% (15)
Richland $60,865,434 (30) 2% (66) $241,549,299 (38) 6% (72)
Ross $33,267,855 (67) 3% (52) $281,600,791 (32) 10% (39)
Sandusky $74,346,534 (23) 3% (53) $212,843,255 (44) 8% (48)
Scioto $24,304,785 (73) 3% (51) $117,445,217 (63) 7% (60)
Seneca $56,748,780 (36) 6% (25) $153,350,740 (52) 10% (31)
Shelby $80,446,033 (18) 3% (49) $226,462,435 (42) 9% (45)
Stark $82,669,192 (15) 1% (79) $1,225,863,198 (5) 7% (65)
Summit $53,052,421 (44) >1% (85) $1,086,245,523 (6) 4% (82)
Trumbull $46,191,278 (54) 1% (74) $256,092,067 (37) 6% (77)
Tuscarawas $52,437,891 (45) 3% (60) $227,995,907 (41) 8% (52)
Union $459,647,601 (1) 7% (21) $549,639,730 (12) 9% (40)
Van Wert $89,276,531 (12) 7% (19) $131,848,326 (60) 11% (27)
Vinton $8,559,330 (86) 8% (11) $27,107,972 (85) 17% (4)
Warren $46,224,334 (53) 1% (76) $552,430,711 (11) 6% (70)
Washington $30,966,222 (69) 4% (46) $132,119,584 (59) 8% (50)
Wayne $283,008,467 (3) 5% (31) $1,002,275,825 (7) 15% (8)
Williams $43,262,519 (58) 5% (35) $145,407,816 (54) 11% (29)
Wood $97,920,671 (10) 2% (68) $387,193,635 (17) 6% (71)
Wyandot $60,516,234 (32) 6% (23) $149,052,657 (53) 12% (21)

 

Ohio State Researchers: Milk Date Labels Contribute to Food Waste

Written by Tracy Turner; Sources by Brain Roe and Dennis Heldman

COLUMBUS, Ohio — Got milk?

If so, you may be among the majority of consumers who throw that milk out once the date on the carton or jug label has passed.

But Ohio State University researchers say not so fast — that pasteurized milk is still good to drink past its sell-by date.

Scientists in the College of Food, Agricultural, and Environmental Sciences (CFAES) say that arbitrary date labels on food contribute to significant food waste because the date labels serve only as an indicator of shelf life, which relates more to food quality than safety.

Brian Roe, a CFAES professor of agricultural economics, co-authored a new study examining consumer behavior regarding date labeling on milk containers. The goal of the research is to help consumers reduce food waste through improved food labeling systems and consumer education.

The study, which will appear in the June 2018 edition of Food Quality and Preference Journal, surveyed 88 consumers who were asked to sniff half-gallon jugs of milk that were 15, 25, 30 and 40 days past the date they were bottled. Some milk samples were dated and some were not dated.

The study found that 64 percent of respondents said they would throw the milk out that had a date label, while only 45.8 percent of respondents said they would throw the same milk out if they didn’t know the date label of the milk.

“Date labeling doesn’t tell you when a food will spoil,” said Roe, who also leads the Ohio State Food Waste Collaborative, a collection of researchers, practitioners and students working together to promote the reduction and redirection of food waste.

“Consumers often view dates as if they indicated health or safety, but those dates are really just about the quality of a product determined by manufacturers,” Roe said. “There’s a difference between quality and safety.

“Pasteurized milk is safe past the sell-by date unless it has been cross-contaminated. While it may not taste as good — it can go sour and have flavors that people don’t like and may make them feel nausea — but it isn’t going to make them sick.”

Roe said the study focused on milk because it is one of the most wasted food products in the United States, representing 12 percent of consumer food waste by weight. And past research suggests the date label is a critical reason why milk is discarded, he said.

“Innovations in date labels and explaining what the date labels mean will allow more consumers to save money by keeping milk longer and reducing food waste, which has social implications as well,” Roe said. “It’s very resource intensive to produce milk — from the land needed to grow feed for the cows, to the water used for cows to produce the milk, to the energy that goes into housing cows and to processing and transporting the milk.

“Not to mention the retailers, who spend a lot of time managing the milk case at the grocery store as well.”

Confusion regarding food label dates leads to significant food waste nationwide, with the average American household spending more than $2,000 annually on wasted food, according to a study by the Natural Resources Defense Council.

So what do the date labels on food mean?

According to the U.S. Department of Agriculture, the:

  • “Best if used by/before” date indicates when a product will be of best flavor or quality. It is not a purchase or a safety date.
  • “Sell-by” date tells the store how long to display the product for sale for inventory management. It is not a safety date.
  • “Use-by” date is the last date recommended for the use of the product while at peak quality. It is not a safety date except when used on infant formula.

“If we make changes to the date labeling, we have to make sure the regulatory system understands how the changes will impact their regulations,” said Dennis R. Heldman, a CFAES professor of food engineering, a member of the Food Waste Collaborative and a study co-author.

Heldman is also studying the effect on consumers of an indicator that would be attached to containers of perishable foods to monitor their shelf life. The indicator would gradually change color during storage and distribution of a food or beverage.

So a change in color, say, from blue to red, would tell consumers that the product has reached the end of its shelf life.

“Using this method, consumers can be confident as to when the product should and shouldn’t be consumed,” he said.

 

Ohio Farm Custom Rate Survey 2018

by Barry Ward, Leader, Production Business Management, OSU Extension, Ag and Natural Resources

 A large number of Ohio farmers hire machinery operations and other farm related work to be completed by others. This is often due to lack of proper equipment, lack of time or lack of expertise for a particular operation.  Many farm business owners do not own equipment for every possible job that they may encounter in the course of operating a farm and may, instead of purchasing the equipment needed, seek out someone with the proper tools necessary to complete the job. This farm work completed by others is often referred to as “custom farm work” or more simply “custom work”. A “custom rate” is the amount agreed upon by both parties to be paid by the custom work customer to the custom work provider.

Custom farming providers and customers often negotiate an agreeable custom farming machinery rate by utilizing Extension surveys results as a starting point. Ohio State University Extension collects surveys and publishes survey results from the Ohio Farm Custom Survey every other year. This year we are updating our published custom farm rates for Ohio.

We need your assistance in securing up-to-date information about farm custom work rates, machinery and building rental rates and hired labor costs in Ohio.

Please provide rates that are current including the latest price increases or planned increases.

An online option for this survey is available at: OhioFarmCustomRatesSurvey2018

or: https://osu.az1.qualtrics.com/jfe/form/SV_cJa90YBYdWOa6DX

We would ask you to please respond even if you know only have a few operations with data.  We want information on actual rates, either what you paid to hire work or what you charged to perform custom work.

Deadline for Surveys to be returned: March 31st, 2018

Taking Measure of Ohio’s Opioid Crisis

by: Mark Rembert, Michael Betz,  Bo Feng, and Mark Partridge
Opioid addiction, abuse, and overdose deaths have become the most pressing public health issue facing Ohio. Ohio leads the country in drug overdose deaths per capita, a rate that continues to rise, overwhelming families, communities, and local governments across the state. In this policy brief, we aim to contribute to the understanding of this unfolding crisis and highlight insights that can inform policymaking.
One important motivation for us to consider this topic is its significant costs. We estimate that there were likely 92,000 to 170,000 Ohioans abusing or dependent upon opioids in 2015, resulting in annual costs associated with treatment, criminal justice, and lost productivity of $2.8 billion to $5.0 billion. Additionally, we estimate that the lifetime lost productivity of those who died from an opioid overdose in 2015 to be $3.8 billion, for an annual total cost of opioid addition, abuse, and overdose deaths ranging from $6.6 billion to $8.8 billion. To put this into perspective, Ohio spent $8.2 billion of General Revenue Funds and Lottery Profits money on K-12 public education in 2015, thus, the opioid crisis was likely as costly as the state’s spending on K-12 education.
The emergence of the opioid crisis has been unevenly distributed across the state. We consider the relationship between drug overdose deaths in 2015 and several county level economic, demographic, and health factors. We find that areas of the state experiencing lagging economic growth and low economic mobility had higher drug overdose death rates. We also find that overdose deaths were strongly linked to educational attainment. In 2015, the drug overdose rate for those in Ohio with just a high school degree was 14 times higher than those with a college degree. Finally, we note the link between prescription opioids and overdose rates, finding that counties that had higher levels of prescription opioids per capita in 2010 also had higher overdose death rates in 2015.
Research has shown that the most clinically and cost effective method for reducing opioid addiction, abuse, and overdose death is medication-assisted treatment. We consider the prominent treatment options, and discuss their availability across the state. We estimate that in the best-case scenario, Ohio likely only has the capacity to treat 20-percent to 40-percent of population abusing or dependent upon opioids. We find distinct geographic disparities in access to treatment, especially between urban and rural areas of the state. Many people in rural areas of Ohio have extremely limited access to medication-assisted treatment. This is a particularly critical issue in the rural areas of Southwest Ohio where opioid abuse rates are high but local access to treatment is limited.
We conclude by offering two policy recommendations based on our analysis. In the near term, the state should prioritize expanding access to treatment in underserved areas. This would require working with physicians and hospitals in underserved areas to encourage providers to obtain the waiver required to prescribe opioid treatments to their patients. We note that Vermont offers an excellent model for expanding access to opioid treatment. In the long term, the state should focus on improving the labor market outcomes of residents in areas severely impacted by the crisis. Specifically, we recommend that the state focus on improving educational investments in as a way of deterring drug abuse and overdose, particularly noting the substantial evidence linking early childhood interventions on improved employment outcomes later in life.

Management Implications from the Scientific Journals

by: Brian E. Roe, Van Buren Professor, AED Economics, Ohio State University Leader, Ohio State Food Waste Collaborative

Sometimes good management advice is difficult to parse from cutting edge academic research.  Below I share a few articles I’ve run across from my reading of the journals that might have some ready implications for managers across the state

Marketing your food locally, and looking for another angle to enhance that ‘local’ premium?

David Willis and colleagues found that consumers were willing to pay nearly twice the premium for local (versus non-local) produce and animal products if the farmer made a donation to a local food bank as part of the sale price. This created a win-win – for the farmer with the enhanced price premium and the local food bank with the donation

Ever wonder if those commercials telling you to drink milk or eat beef are worth the check-off dollars?

It’s tough to tell for sure unless the advertising totally stops, like it did for orange growers a few years ago. Oral Capps and colleague at Texas A&M found that when generic orange juice promotion essentially stopped for a few months during 2001, it resulted in more than a $50 million drop in sales.

 Better to shift to fall calving for beef cow herds?

According to Gavin Henry and colleagues from the University of Tennessee, fall calving was better in their simulations based on the last 20 years of data. Fall calving was more profitable than the spring calving for all feed rations and weaning months. Fall calving was also preferred because it was less risky in terms of profits than spring calving.

How often is it economically optimal to test your soil?

For cotton producers, when considering Potassium, it turns that about every other year makes the most sense, though not much is lost if an every third year schedule is followed instead. Check out these results from Xavier Harmon and colleagues from the University of Tennessee.

Farm Management Program Manager Career Opportunity at Ohio State University

Source: The Department of Agricultural, Environmental, and Development Economics

The Department of Agricultural, Environmental, and Development Economics (AEDE) at The Ohio State University (OSU) is searching for new position as Manager for our Farm Management Program. The incumbent will develop and implement a comprehensive and innovative farm management program that addresses critical farm management issues affecting Ohioans, including marketing and price analysis, farm financial management and investing, risk evaluation and management, agricultural processing, environmental issues, and farm entry among other issues, and integrates AEDE’s research, teaching and outreach in the area.  The Farm Management Program Manager acts as a liaison between faculty members in the AEDE Department who are conducting research on areas related to Farm Management, and with OSU Extension faculty and field specialists throughout the state.  The Manager will also lead and/or collaborate on externally funded research projects that integrate OSU faculty in the field with AEDE faculty.  In collaboration with the AEDE Outreach and Communications Manager, the Farm Management Program Manager will develop and manage communications and outreach strategies, and contributes communications content for AEDE’s farm management program and serves as an active member of the AEDE Outreach Committee.

Candidates for this position will have at least a Master’s Degree in economics, applied economics or agribusiness or equivalent educ/exp, and are also required to have experience in agribusiness/farm management.  Candidates will have the ability to lead integrated initiatives from inception to implementation, will have experience in program planning and administration, and must be collaborative and work well in a diverse team-oriented environment with superior verbal and written communications skills.

Applications will be accepted through the Careers at OSU website, https://www.jobsatosu.com/postings/79928, starting July 8 and running through August 6.  We will begin screening application on August 6.

For question on this position, please contact the AEDE Outreach Program Director, Professor Brent Sohgnen, at sohngen.1@osu.edu.