Introduction
During the last decade, organic dairy production has increased rapidly in the United States with the consumer market for organic milk growing from 1.9 to 5.0% of total milk sales (USDA-AMS, 2020). While organic livestock typically produce less milk than conventionally-raised animals, producers have historically captured higher milk prices to offset increased input costs (Su et al., Reference Su, Brown and Cook2013). Organic dairy producers are now facing declining milk prices due to an oversupply of milk. As of 2019, total organic milk products declined by 2.2% (USDA-AMS, 2020). However, the organic, grassfed (OGF) milk market continues to show high demand leading to an increase in grassfed livestock production, particularly in the northeastern USA (USDA-NASS, 2017). Grassfed, for the purposes of this study, is defined as dairy production relying on forages (pasture and conserved forages), without grains, to meet nutritional needs. This specialized system, to be certified OGF, must also comply with organic standards as well as a unique set of regulations specific to grassfed dairy.
Existing research suggests that, to secure the future of their farms, producers are choosing to adopt grassfed feeding systems in part due to economics, but also due to other reasons, including personal philosophy, health/safety and environmental sustainability (Flaten et al., Reference Flaten, Lien, Ebbesvik, Koesling and Valle2006; Bouttes et al., Reference Bouttes, Darnhofer and Martin2019). In a Norwegian study, Flaten et al. (Reference Flaten, Lien, Ebbesvik, Koesling and Valle2006) grouped 161 organic dairy producers in three different categories: early organic farming entrants (certified organic since 1995 or earlier), midconverters (certified organic between 1996 and 1999) and late converters (certified organic since 2000 or later). All groups ranked environment, sustainability and the production of high-quality food as their main motivators, although those who transitioned later ranked economics as a major motivator as well. Bouttes et al. (Reference Bouttes, Darnhofer and Martin2019) found that organic producers were motivated to switch from conventional management to achieve increased professional satisfaction as competitive organic milk prices enabled them to reduce the number of cows and workload.
Navigating regulatory policies of the transition process is also commonly perceived as a challenge in the OGF dairy industry. Organic and grassfed dairy are similar in management but have different regulations that dictate some aspects of the production system. This includes livestock feeding requirements, which could directly influence milk production. As per regulations set forth by the USDA National Organic Program (USDA-NOP, 2020), organic dairy cattle over the age of 6 months must receive 30% or more of dry matter intake (DMI) from pasture during the grazing season for a period of at least 120 days. It is further mandated that OGF livestock must be on pasture at least 150 days and receive 60% or more of their DMI from pasture (Organic Trust Plus Inc., 2019). Supplements and feed additives without grain components may also be utilized under strict guidelines. Compliance with these regulations can create challenges on OGF dairies, particularly due to the nutritional reliance on forages and potential limitations in pasture productivity and quality (Brito and Silva, Reference Brito and Silva2020; Wilkinson et al., Reference Wilkinson, Lee, Rivero and Chamberlain2020).
Research supporting grassfed dairy operation needs is currently outpaced by the sector's expansion, creating knowledge gaps for both producers and the allied industry. Demographic (McBride and Greene, Reference McBride and Greene2009), management (Stiglbauer et al., Reference Stiglbauer, Cicconi-Hogan, Richert, Schukken, Ruegg and Gamroth2013) and research and education needs (Pereira et al., Reference Pereira, Brito, Townson and Townson2013) regarding organic dairy production have been previously reported as having perceptions of consumers (Pirog, Reference Pirog2004) and grassfed dairy producers in Europe (Läpple, Reference Läpple2013). There is currently limited research exploring grassfed dairy industry characteristics and social factors that may influence milk production and management decisions in the USA. Our research utilized a nationwide survey of OGF dairy producers to improve the understanding of this farming demographic and their characteristics. Therefore, the objectives of the current study were to (1) assess information regarding current production practices and producer knowledge, and (2) identify agronomic and social factors that may influence milk production on OGF farms across the United States.
Materials and methods
Survey questionnaire
A survey was developed in 2019 with the aim of recording information about OGF producer characteristics, attitudes and perceptions. The survey was developed through a multidisciplinary research team in collaboration with a producer advisory group and industry feedback. Institutional Review Board approval was obtained prior to beginning the research from the University of Vermont (IRB # 00000036). We utilized the Dillman tailored design approach to disseminate the survey to 351 farms from a list of producers obtained from dairy cooperatives located in New York and Wisconsin (Dillman et al., Reference Dillman, Smyth and Christian2016). A postcard first announced the survey, followed by the survey instrument, and a reminder postcard. A second survey was sent to any non-respondents one month later. A total of 167 respondents returned the survey, with three undeliverable, three out of sample as their cattle had been sold, and one explicit refusal, resulting in a response rate of 46.8% according to the American Association for Public Opinion Research calculation methodology (AAPOR, 2015).
Survey instrument and variables
The survey consisted of multiple sections including (1) general farm information, (2) herd management and (3) forage and grazing management. The questions utilized in the statistical analysis are included in Supplementary Table 1, including descriptive statistics. All variables included were specific questions from the survey, apart from the Information Scale, which was an aggregated score of all responses for sources of dairy information and frequency (Cronbach α = 0.82).
Statistical analysis
Data were analyzed in Stata 16.0 (Stata 2019), utilizing descriptive statistics and multivariable analysis. Relationships were analyzed using Pearson correlation coefficients with statistical significance declared when P ⩽ 0.05. Results were interpreted as a weak association being 0.1 to 0.3 or −0.1 to −0.3, medium association at 0.3 to 0.5 or −0.3 to −0.5, and strong at 0.5 to 1.0 or −0.5 to −1.0. A multivariable regression analysis was conducted, first utilizing step-wise regression analyses. We conducted seven separate step-wise regression models focused on characteristics relating to animals, forage management, policy, on-farm management, demographics, producer knowledge and information (Table 1). Factors from individual models were considered significant when P ⩽ 0.10 and used in the final full multivariable regression model to determine influence on milk production. Factors from the multivariable regression model were considered significant at P ⩽ 0.05 and are reported in their respective sections below as it relates to milk production. Model results for all step-wise regression models are included in Supplementary Tables 2, 4–6, 8, 9 and 11. Post-hoc analysis was also performed to identify any relationship between survey response data and self-identification by respondents belonging to the plain community. These relationships were analyzed in SAS 9.4 (SAS Institute) using the PROC FREQ procedure with two-sided χ2 tests. Values were considered significant at P ⩽ 0.05.
Table 1. Full multivariable model, following seven stepwise models, predicting milk production per cow
R 2 = 0.31, n = 135.
Results
Descriptive statistics
Producer demographics and on-farm decision-making influences
Geographically, the largest number of responses (49.7%) were received from OGF dairies located in New York (81 farms). The remaining responses were received from OGF dairies located in 15 states with the majority located in the Northeastern [Pennsylvania (6.1%), Vermont (6.1%), New Hampshire (1.2%), New Jersey (0.6%) and Massachusetts (0.6%)] or Midwestern [Ohio (11.7%), Wisconsin (7.9%), Iowa (3.7%), Minnesota (1.8%), Kansas (0.6%) and Indiana (0.6%)] USA (Supplementary Fig. 1). Outside of the Northeast and Midwest, remaining responses were received from Maryland (3.7%), Virginia (2.5%), Florida (0.6%) and Oregon (2.5%). Demographically, producers predominantly identified as being the farm owner (91.5%), male (91.4%), and as belonging to the plain, or Amish and Mennonite, community (61.0%; Supplementary Table 2). Respondents indicated that they had been dairy farming for 21.4 ± 15.1 years with a mean age of 47.6 ± 12.9 years.
Respondent ranking of factors that influence on-farm decision-making is shown in Figure 1. When producers compared the self-perceived status of their farm to similar farms on a scale from significantly worse to significantly better, 79.3% indicated that they were at least somewhat better compared to other dairies, whereas 19.3% said that they felt they were doing worse than other farms. Based on responses, autonomy, environment, labor and future generations were of equal influence regarding on-farm decision-making across the entire sample. This is also reflected by the number of producers that have succession plans; only half of producers stated that they had at least a partial succession plan. However, when assessing farm priorities by a respondent who is identified as part of the plain community (termed plain producers herein) vs those that did not (termed non-plain producers herein), family and well-being was more influential to non-plain producers (P = 0.02). Autonomy was considerably more important to producers identifying as plain producers (P = 0.03) whereas economics was considered more important by non-plain producers (P = 0.03). Both plain and non-plain producers ranked economics as the greatest influence for decision-making with an average importance of 5.4 ± 2.8, on a scale from 1 to 9. However, impacts on society (neighbors, community) and policies and regulations proved to be slightly less influential with averages of 6.4 ± 2.4 and 6.1 ± 2.5, respectively. Non-plain producers indicated that while policy was not as influential on their decision-making, they still held it in higher regard than plain producers (P = 0.04; Supplementary Table 3).
Fig. 1. Respondent ranking of factors that influence on-farm decisions. Responses are based on a scale from 0, being the most influential, to 9, being the least influential. Values are expressed as the mean with the minimum and maximum response range.
Federal programs and policy factors
The questionnaire included questions about participation in nationally recognized programs, including organic and grassfed certifications, and government cost-share programs such as the Conservation Reserve Program (CRP) and Federal Crop Insurance Program (FCIP). Responses are summarized in Figure 2 and Supplementary Table 4. Most producers were either unaware (23.3% of respondents) of these programs or were aware but did not intend to participate (54.2% of respondents). Non-plain producers were more likely to utilize the Environmental Quality Incentives Program (EQIP; P < 0.0001) and Margin Protection Program for Dairy (MPPD; P < 0.0001) than plain producers. Producers indicated that they utilized both organic certifiers and organic educational organizations, their milk buyer/cooperative, producer publications and other dairy producers monthly or a few times a year for sources of dairy information. On average, 96.3% of farms that responded were certified organic for 10.3 ± 7.2 years or as OGF for 5.1 ± 4.5 years.
Fig. 2. Utilization of United States Department of Agriculture (USDA) programs by organic, grass-fed dairy productions surveyed in spring 2019. Responses are based on a scale from unaware to aware and intending to participate. Values are expressed as a percentage.
Herd characteristics and management
The predominant genetics represented on-farm reported by respondents were crossbred (54.1%), Holstein (21.7%) and Jersey (7.2%). On average, producers had 49 milking cows and an average of 10 dry cows. The calculated average age of cows in a herd was 5.7 ± 1.4 years old. The results of producer satisfaction are summarized in Table 2 and management characteristics in Supplementary Tables 5 and 6. Regarding herd health parameters such as body condition score, animal health, young stock quality, and reproduction and calving, most producers were at least somewhat satisfied with body condition, herd health and young stock quality. Plain producers reported that they were more satisfied with their soil fertility (P = 0.05), reproduction and calving (P = 0.05) and quality of youngstock (P = 0.003) than non-plain producers. Producers indicated that they cull 16.2 ± 8.5% of cows on a yearly basis with four major reasons being reproduction (58.1%), injury or health (35.3%), mastitis (34.7%) and milk quality (30.5%). However, there were no relationships between farmer demographics with culling and age (P = 0.71), years farming (P = 0.09), number of milking cows (P = 0.31) or total hectares managed (P = 0.79) (Supplementary Table 7).
Table 2. Ranking of satisfaction of production parameters by organic, grassfed (OGF) producers across the United States surveyed during the spring of 2019
a The results of 167 surveys were analyzed and ranked according to producer satisfaction. Producers were asked to rank their satisfaction with various production parameters on a scale from very unsatisfied to very satisfied. Values are expressed as percentages of total respondents.
Land and forage management
There was a strong positive relationship between the number of total acres managed and the number of milking cows (R = 0.57); as the total number of hectares managed increased, the number of milking cows increased (P < 0.001). Producers managed 108.3 ± 74.8 hectares of land and owned 74.7 ± 55.4 hectares or rented 49.7 ± 43.7 hectares. Of the land being utilized, 88.8 ± 64.5 hectares was used for pasture and growing perennial and annual forages. However, most producers did not typically grow annual forage crops such as sorghum sudangrass. Those producers who did utilize annual forages indicated that they grew 7.4 ± 7.9 hectares/year. Results are further summarized in Supplementary Table 8.
Farmer knowledge
A total of 93.1% of producers indicated that they believe they have at least an average knowledge regarding grazing management systems (Table 3; Supplementary Table 9) and managed their land for species diversity through grazing management strategies. A typical grazing season lasted 196.8 ± 32.2 days on average with animals receiving between 70 and 90% of DMI from pasture. Producers indicated that their grazing seasons lasted at minimum 140 days and 360 days at maximum. In addition to growing their own stored forages, 63.9% of producers also purchased 38.3 ± 29.1% of their total forage needs. Plain producers were more likely to purchase forages (P < 0.0001). A moderately negative relationship (R = −0.32) was observed between the amount of forage purchased and total hectares managed; as the amount of forage purchased increased, the total amount of hectares managed decreased (P = 0.001).
Table 3. Ranking of self-perceived knowledge of production parameters by organic, grassfed (OGF) producers across the United States surveyed during the spring of 2019
a The results of 167 surveys were analyzed and ranked according to self-perceived producer knowledge. Producers were asked to rank what they believed they knew regarding various production parameters on a scale from very low to very high. Values are expressed as percentages of total respondents.
In the current study, producers were asked about perceived knowledge in growing forages, understanding forage test results, production costs and feed supplementation practices. Approximately 82.9% of respondents reported at least an average knowledge of growing higher-energy forages. Producers also said that they understood the energy requirements of cows according to production levels and were at least somewhat satisfied with their pasture quality and yield (77.3%; P = 0.01), stored forage quality (76.9%; P = 0.003) and stored forage yield (71.4%; P = 0.003; Supplementary Table 10). Respondents (25.6%) indicated that they had a very low or low knowledge regarding forage test result interpretation with similar responses reported about strategies to maximize forage DMI and forage production costs. Plain producers indicated that they were more knowledgeable than their non-plain counterparts regarding the interpretation of forage test results (P = 0.002), growing high-energy forages (P = 0.03), grazing management (P = 0.002) and cattle energy requirements (P = 0.02). However, there were no perceived knowledge differences between plain and non-plain producers regarding forage quality (P = 0.11).
Farmer sources of information
Producers identified key sources of dairy information that influenced their on-farm decision-making. Sources of information included community papers and producer publications, organic certifiers and educational organizations, the USDA, extension groups, feed dealers, consultants, other dairy farmers and milk cooperatives. On a scale from never to daily, producers were asked to identify which sources they utilized the most and found to be most helpful. Private consultants (72.7%) and Farm Bureau (77.6%) were least likely to be utilized, whereas milk cooperatives (90.3%), producer publications (90.3%) and other dairy producers (97.48%) were used as information sources at least a few times a year. Overall, the use of outside sources of information was found to influence milk production in both the step-wise model (P = 0.001; Supplementary Table 11) and the full model (P = 0.001).
Identified agronomic and social influences on milk production
Animal and herd characteristics
In describing OGF herd milk production and milking seasonality, herds were typically milked at least twice daily (84.9%) with an average annual milk production of 4220.6 ± 1044.9 kg cow−1. Results indicate that belonging to the plain community had no bearing on milk production (P = 0.47). Producers (72.0%) indicated that they were at least somewhat satisfied with their current milk production (P = 0.01). A total of 84.8% of respondents reported that their herd produced milk year-round while 15.2% identified as seasonal milking herds. In this study, if seasonal milk production was utilized, the herd dry-off period most frequently occurred between February and March, followed by July and August, and January and February.
As is typical of pasture-based systems, most producers fed their cattle approved energetic supplements or byproducts. Sugar or molasses were the two most commonly utilized feed supplements with 41.4% of surveyed producers stating they utilized energy sources year-round. Other producers indicated that they supplemented as needed during the winter season (29.9%) or during the grazing season (4.6%). Besides molasses and sugar, producers were also utilizing supplements such as minerals, salt, sodium bicarbonate, clay, yeast, probiotics and kelp meal, as well as less-commonly utilized products such as apple cider vinegar and dried carrots. We completed seven separate individual step-wise models (Supplementary Tables 2, 4–6, 8, 9 and 11), which indicated the following significant relationships to milk production: (1) Holstein breed associated with higher milk production (b = 3357.36, P = 0.001), (2) milking twice a day associated with higher production (b = 1577.11, P = 0.006), (3) use of energy supplements associated with higher production (b = 964.74, P = 0.02), (4) length of grazing season associated with lower milk production (b = 31.11, P = 0.03), (5) producers with higher levels of grazing management associated with lower milk production (b = −734.08, P = 0.02), (6) producers with higher levels of reproductive performance management associated with higher milk production (b = 534.87, P = 0.06), and (7) producers with greater frequency and number of dairy information sources associated with higher milk production (b = 1218.97, P = 0.000). The full multivariable model (which incorporated only the statistically significant results from each of the stepwise regression models) indicated that Holstein cow use (b = 1574.95, P < 0.001), reproduction knowledge (b = 578.27, P = 0.008) and greater frequency and depth of dairy information (Information Scale; b = 1036.45, P = 0.001) were all positively associated with per cow milk production (R 2 = 0.31).
Discussion
We aimed to better understand the characteristics and perspectives of OGF dairy producers across the USA, and identify the factors related to higher milk production in OGF dairy herds. Overall Holstein cows, producers with higher knowledge of reproduction, and greater frequency and depth of dairy information sources were found to be positively associated with milk production. Given that pasture-based cows typically produce less milk than those fed in confinement due to less control over nutrient intake (Kolver, Reference Kolver2003), our findings provide a guide for the OGF industry to better understand its capacity to increase production. The reported average milk produced in our study was lower than that of confined and traditional organic (but not grassfed) dairy herds reported in the survey of McBride and Greene (Reference McBride and Greene2009), likely because OGF cows are not permitted to be fed supplemental grains. However, other factors may contribute to lower milk production in OGF systems. Responses provided by US OGF dairy producers have for the first time given insight into production and management practices of this production system. Furthermore, there is now a better understanding of the agronomic and social factors that influence milk production and on-farm decision-making on OGF dairies. Below we discuss further demographics, management and knowledge findings, as well as the factors related to higher milk production.
Demographic factors
The distribution of OGF producers who responded to our survey aligns with data reported by the 2017 USDA-NASS Certified Organic Survey and previous research by McBride and Greene (Reference McBride and Greene2009), whereby New York represented the state with the greatest number of certified organic farms, followed by Wisconsin, Pennsylvania, Indiana, Ohio and Vermont. Little information is available about the geographic distribution of purely grassfed producers but a study by Weber et al. (Reference Weber, Heinze and DeSoucey2008) indicated that grassfed producers were located in all regions of the USA. In the current study, OGF farms were most concentrated in the Northeastern and Midwestern USA due to proximity to dense human populations, diverse milk markets, infrastructure for handling OGF milk and OGF milk buyers (Flack, Reference Flack2016); however, these geographic distributions also likely reflect our sample size based on lists obtained from partner cooperatives.
The large proportion of respondents indicating they belonged to the plain community in this study aligns with reported geographical data as higher densities of Amish communities are located in the Northeastern and Midwestern USA (Cross, Reference Cross2016), and also may explain the low levels of reported technology use for record keeping and finances. Furthermore, plain producers were less likely to participate in government programs, and exhibited different on-farm priorities. These findings are important because it suggests that the organic dairy industry needs to communicate and develop benchmarks for the plain community using non-electronic mechanisms and provide technical assistance outside of government programs. McBride and Greene (Reference McBride and Greene2009) found similar results with organic dairy producers; based on their survey results, 41.0% of organic dairy producers utilized information from the Internet, whereas only 21.0% utilized on-farm computer records. In their study, several technologies and practices tended to increase with the size of the organic operation and with higher education (McBride and Greene, Reference McBride and Greene2009); however, we did not account for education level in the current study.
Management factors
Regarding herd health and culling practices, similar to our results, Rozzi et al. (Reference Rozzi, Miglior and Hand2007) found that two main reasons organic producers culled animals were poor fertility and mastitis. Additionally, Ahlman et al. (Reference Ahlman, Berglund, Rydhmer and Strandberg2011) compared dynamics of culling reasons between conventional and organic herds and reported no genotype × environment interactions for longevity. However, it was determined that the main reason for culling in organic herds was due to poor udder health, whereas fertility issues were the main cause in conventional herds. Furthermore, culling occurred at a younger age in organic herds. This directly contradicts the findings of both Mullen et al. (Reference Mullen, Sparks, Lyman, Washburn and Anderson2013) and Stiglbauer et al. (Reference Stiglbauer, Cicconi-Hogan, Richert, Schukken, Ruegg and Gamroth2013). Mullen et al. (Reference Mullen, Sparks, Lyman, Washburn and Anderson2013) found that organic farms and conventional farms utilizing grazing typically kept animals that were older, which are generally associated with mastitis, foot problems and milk fever. Stiglbauer et al. (Reference Stiglbauer, Cicconi-Hogan, Richert, Schukken, Ruegg and Gamroth2013) also determined that organic farms kept older cattle, although in their study, there was a trend of younger animals on conventional, non-grazing operations. It should be noted that culling decisions are complex, requiring knowledge of current animal status and predicted performance. In OGF operations, decisions also rely on compliance with grassfed rules and certain ethical standards, meaning that antibiotics cannot be utilized when dealing with mastitis or udder health. Therefore, the reasons provided by producers for culling in our survey may depend on what the producers find acceptable which could be different from conventional producers.
Social influences
Both plain and non-plain producers ranked economics as their greatest influence for decision-making. Family and well-being were more influential to on-farm decision-making to non-plain producers as was economics. This could be related to the number of producers that had succession plans, which, in turn, provides potential security for their families. Of the surveyed producers, 54.8% said they did not have a succession plan. These results align with Brock and Barham (Reference Brock and Barham2013) where dairy producers also indicated economics to be one of the primary forces behind decision-making. Organic grassfed producers in the current study reported similar drivers for on-farm decision-making, with economics, environmental reasons and health being the major influences. Producers also noted that they were not interested in participating in government-sponsored programs, which was similar to the results presented by Hanson et al. (Reference Hanson, Dismukes, Chambers, Greene and Kremen2004). Hayden et al. (Reference Hayden, Rocker, Phillips, Heins, Smith and Delate2018) suggested that organic producers feel at odds with normal farming practices and, therefore, did not feel valued as highly by financing, insurance and regulatory institutions. This, in turn, could lead to a distrust in government-based programs. Additionally, there is speculation that producers feel as though some government programs are not adequately promoted and are not adaptive enough (Hayden et al., Reference Hayden, Rocker, Phillips, Heins, Smith and Delate2018). For example, a significant proportion of OGF producers in this study indicated they had no knowledge of these programs, which may also be related to the high percentage of producers in OGF dairy that belong to the plain community, who may have an aversion to federal programs based on personal philosophy. One specifically important finding was the extent to which producer frequency and number of dairy information sources positively correlated with milk production, which agrees with previous research (Lubell et al., Reference Lubell, Niles and Hoffman2014). These results indicate that dairy producers may be getting a diversity of information across varying sources from other farmers to extension to industry professionals, all of which may help them identify practices to improve their productivity.
Farmer satisfaction and knowledge
The results of this study indicated that the length of the average grazing season was approximately 197 days, which is longer than expected considering the weather conditions of the Northeast and Midwest. Similar to Pereira et al. (Reference Pereira, Brito, Townson and Townson2013), despite most producers being located in the Northeast and Midwest, surveyed OGF dairies in this study indicated they operated on a year-round basis, rather than seasonally. This could potentially be due to the use of annual forages to extend the grazing season. However, our results indicated that the use of annual forages and year-round grazing operations did not have a relationship (P = 0.17) as most producers did not utilize annuals in this study. The length of grazing season could potentially be related to the amount of forages purchased. As previously stated, approximately 64% of producers purchased forages to supplement grazing diets. Producers supplemented their diets with an average of 38.3% purchased forages although amounts ranged from 0.33% of the diet to 100%. This is similar to Pereira et al. (Reference Pereira, Brito, Townson and Townson2013) who reported that 73% of organic farmers purchased feed from off-farm sources. However, in this study, there was no relationship between grazing seasonality and whether producers purchased forages or not (P = 0.65).
A large portion of OGF producers indicated that they were satisfied with their pasture quality, yield, and milk production. The majority also indicated that they only had an average knowledge about factors (i.e., growing higher-energy forages, forage improvement and quality, forage DMI) that could affect pasture utilization and milk production. This suggests that OGF producers may be prioritizing other management practices and do not see a need to focus on forage quality and milk production. For example, Pereira et al. (Reference Pereira, Brito, Townson and Townson2013) assessed research needs and topics of priority of Northeastern organic dairy farmers. Respondents indicated that although obtaining a steady, fair milk price and improving forage production was important to them, there were other priorities, such as integrated pest management, that were just as valuable.
Because OGF producers may be prioritizing factors other than milk production and forage management, based on survey responses, it is possible that they are not supplying cows with high-quality forage to meet energetic demands. Energy intake is dependent on DMI and dry matter energy value (Van Vuuren and Van Den Pol-Van Dasselaar, Reference Van Vuuren, Van Den Pol, Elgersma, Dijkstra and Tamminga2006). Lower DMI in pasture-based systems is one of the main limiting factors for milk production as this affects metabolizable energy supply (Kolver, Reference Kolver2003). In our study, DMI as reported by producers was not a significant factor relating to milk production but this could be due to other on-farm factors such as length of grazing season and management intensity (Weiss and Jeltsch, Reference Weiss and Jeltsch2015).
Approved non-grain, energy sources and feed additives are utilized in OGF systems. One such supplement is molasses, as it is palatable, energy-dense, and approved for organic dairy production (Soder et al., Reference Soder, Brito and Hoffman2011) and by some grassfed labels (American Grassfed Association, 2018). Producers reported greater milk yield with energy supplements, as well as greater satisfaction with their milk production but still reported using energy-based supplements year-round, which, depending on the supplement, could prove costly, as compared to using high-energy forages (Soder et al., Reference Soder, Brito and Hoffman2011). Kelp meal was also commonly utilized by respondents (72.5%) in this survey. Although more costly, according to Antaya et al. (Reference Antaya, Soder, Kraft, Whitehouse, Guindon, Erickson, Conroy and Brito2015), organic dairy producers have anecdotally linked kelp meal to improved body condition score and animal appearance, decreased milk somatic cells count, and control of nuisance flies. Previous surveys conducted in the Northeast (Antaya et al., Reference Antaya, Soder, Kraft, Whitehouse, Guindon, Erickson, Conroy and Brito2015) and Midwest (Hardie et al., Reference Hardie, Wattiaux, Dutreuil, Gildersleeve, Keuler and Cabrera2014; Sorge et al., Reference Sorge, Moon, Wolff, Michels, Schroth, Kelton and Heins2016) revealed that between 49.0 and 83.0% of organic dairy producers feed kelp meal, thus consistent with our results.
Forage quality and quantity was a common issue as home-grown forages were lacking in either amount or quality. Utilization of energetic supplements and outsourced forages could suggest a forage quality issue, leading to a dietary deficiency. The need to purchase additional forages could also be due to resource constraints or inappropriate stocking rate/density, both of which can affect forage quantity and quality. Economic viability may also play a role as producers in our study indicate they are highly driven by economics; purchasing forages represents a lower initial investment relative to growing forages, which could be economically advantageous.
Reproduction, genetics and breed type had an influence on milk production. In our study, farms comprised of Holstein cows reported that they had the greatest milk production. These results are similar to Coffey et al. (Reference Coffey, Horan, Evans and Berry2016) who found that Holstein herds in seasonal calving systems had a greater milk production than Friesian and Jersey herds. Jersey was found to have greater milk protein and fat concentrations, whereas Holstein had lower concentrations (7.75%), similar to data presented here in which the average concentration of milk fat and protein combined was 7.77%. Apart from breed, reproductive performance knowledge correlated with increased milk production, which agrees with the suggestion that OGF producers are prioritizing factors other than forage management. The economic impact of reproductive performance and knowledge makes management a key priority to producers (Denis-Robichaud et al., Reference Denis-Robichaud, Cerri, Jones-Bitton and LeBlanc2018), thus consistent with the finding that a large proportion of culling decisions are made based on cow reproductive status. Denis-Robichaud et al. (Reference Denis-Robichaud, Cerri, Jones-Bitton and LeBlanc2018) reported that dairy reproductive performance was one of the most important priorities for research. However, unlike the producers in the current study, almost half of the producers in the study by Denis-Robichaud et al. (Reference Denis-Robichaud, Cerri, Jones-Bitton and LeBlanc2018) were not satisfied with their milk production. This could indicate that because OGF dairy producers prioritize reproductive performance, and possible health and welfare, more so than other factors, they may not encounter the same challenges as conventional producers.
Conclusions and implications
Results of this study showed a need for further research to develop financial and production benchmarks that will aid OGF dairy producers in improving management practices thereby enhancing economic sustainability on their operations. However, traditional methods of communication and outreach may not reach a significant portion of OGF dairy producers from the plain community who do not utilize modern information technologies. This is an important finding, especially because greater frequency and diversity of information sources were found to positively associate with animal production. Future efforts must focus on a wider range of outreach to better meet the educational needs of OGF dairy producers through a broader array of methods to disseminate information. Additionally, the results of this study revealed a high level of misunderstanding and disconnect, specifically in the areas of producing high-quality forage, meeting energy needs of OGF dairy cows, feed supplements and their implications to overall animal productivity and farm profitability.
In addition to creating financial and production benchmarks, our results revealed knowledge gaps in the areas of forage quality, forage energetics, feed supplements and their implications to grazing nutrition. These identified critical areas indicate that research needs to be developed to determine forage energetics and help producers create pasture plans that will ensure that their animals receive nutrient and energy-dense forages. Future research efforts are needed to better meet the educational needs of OGF dairy producers through a broader array of methods to disseminate information.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S1742170521000284
Acknowledgements
We would like to acknowledge and thank all survey respondents for their participation. We would also like to thank Thomas Wentworth and Courtney Hammond-Wagner for their assistance with survey development and analysis. Portions of this work were presented previously at the 2020 Northeast Pasture Consortium (NEPC) held January 17–18, 2020 in Fairlee, Vermont and at the 2020 American Dairy Science Association Annual Meeting held virtually on June 22–24, 2020.
Financial support
The project was supported by the USDA—Organic Agriculture Research and Extension Initiative (OREI) program (grant # 2018-02802). Figures and images were created using Datawrapper.
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