More farmers in the Northeast have become interested in planting winter cereals as double crops in a corn silage rotation to increase per acre crop yields and reduce feed imports while also taking advantage of the environmental and soil health benefits of year-round ground coverage. Although the current recommendation is to harvest winter cereals at flag leaf stage (Zadoks 39, Feekes 9), harvest timing can impact the yield and quality of both crops in a double cropping system. In May 2014 and 2015, we collected forage samples of cereal rye, triticale and winter wheat over time on seven NY farms, targeting two visible nodes (Zadoks 32, Feekes 7), flag leaf visible but still rolled up (Zadoks 37, Feekes 8), flag leaf completely unrolled with the ligule visible (Zadoks 39, Feekes 9), boot stage with the flag leaf sheath swollen but not yet open (Zadoks 45, Feekes 10), and the end of heading, just prior to flowering (Zadoks 59, Feekes 10.5). Here we focus on cereal rye and triticale. There was a 37% relative yield gain and a reduction in Relative Forage Quality (RFQ) of 14 for cereal rye when harvest was delayed from flag leaf stage to boot stage (a 2-day delay in harvest date). When harvested three days earlier than flag leaf stage at Zadoks 37, relative yield decreased by 15% and RFQ increased by 5. Triticale harvested at boot stage—also an average of just two days after flag leaf—resulted in a relative yield gain of 23%, yet RFQ only declined from 183 to 178 across both years. Harvesting triticale five days earlier at Zadoks 37 resulted in a 19% relative yield loss and a negligible increase of less than 2 RFQ. Thus, delaying triticale harvest until boot stage resulted in a substantial yield gain with a minor reduction in forage quality and minimal delay in corn planting. Although RFQ declined more rapidly for cereal rye, RFQ was still ≥164 at boot stage, which is typically considered suitable for dairy cows and calves. Boot stage was reached between May 15 and 23, depending on the site and species. Triticale provided a more flexible harvest window, but cereal rye matured a little earlier (e.g., 3 days earlier to boot stage on average), which allows for earlier corn planting. To alleviate issues with timely corn planting, further research is needed on alternative summer crops that can be planted later, such as forage sorghum.
As the third largest producer of milk nationally, the New York (NY) dairy industry is the leading agricultural sector in the state (USDA NASS 2013). Amidst increasing concerns over the environmental impact and sustainability of agricultural practices, dairy farms have been under particular scrutiny as significant contributors of non-point source pollution. In an effort to improve the water quality of impaired watersheds such as Lake Champlain, the United States Department of Agriculture Natural Resources Conservation Service has outlined a variety of best management practices (BMPs). Over-wintering cover crops, such as cereal rye, have been heavily promoted to improve soil conservation and decrease water pollution by limiting soil erosion and reducing nitrate leaching and phosphorus runoff. For example, the Maryland Department of Agriculture pays farmers to plant winter cereal cover crops to reduce sediment and nutrient pollution in the Chesapeake Bay. A recent analysis of different BMPs lends support to such incentive programs and showed that cover crops were more effective than no-till and filter strips at reducing pollution in Lake Erie (Bosch et al. 2013). Despite the many environmental benefits of cover crops, adoption is still limited in the Northeast where the growing season is shorter and fall seeded cover crops are typically limited by the harvest date of the preceding crop.
The most common barriers to cover crop adoption among dairy farmers in NY include the concern that over-wintering cover crops will delay planting in the spring and the perception that cover crops provide no tangible short-term benefits (Long et al. 2013). As an alternative to growing cover crops solely to enhance environmental sustainability, winter cereals can be planted in the fall and harvested in the spring as forage double crops. In a double cropping system, winter cereals can still provide important soil conservation benefits, but they can also increase forage production on a per acre basis. Therefore, double cropping can alleviate one of the greatest barriers to adoption and simultaneously improve farm productivity and profitability.
Beyond overcoming the barriers to cover crop adoption, there are many benefits to harvesting a winter cereal cover crop for forage. For dairy farmers, double crops can increase the percentage of homegrown forage, reduce the quantity of imported feed, increase feed inventory when it is typically low, and provide additional off-cycle opportunities to spread manure. Compared to corn, winter cereal cover crops provide yields that are less prone to drought, and double cropping enhances crop rotation diversity, which reduces production risk (Roth et al. 2002).
The purpose of this project was to assess the tradeoffs between forage quality and yield for winter cereals grown as forage double crops when harvested at different crop growth stages. As the cost of importing feed increases, dairy farmers are looking for ways to reduce imports while still maintaining high milk production. Without the need to buy or rent more land, winter cereal double crops can increase forage production on a per acre basis. Increasing the amount of forage in dairy cow rations can, however, reduce milk production if the forages are not managed for optimal quality. Consequently, precise crop management is critical for obtaining high milk production with high forage diets. By sampling at five growth stages, our project aimed to provide farmers with the information they need to select a species and coordinate the timing of winter cereal forage crop harvest with the nutritional needs of their dairy cows, heifers, dry cows, or other livestock.
Bosch, NS, JD Allan, JP Selegean, D Scavia (2013) Scenario-testing of agricultural best management practices in Lake Erie watersheds. J Great Lakes Res 39:429–436
Long, E, Q Ketterings, K Czymmek (2013) Survey of Cover Crop Use on New York Dairy Farms. Crop Manag 12. doi:10.1094/CM-2013-0019-RS
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[USDA NASS] United States Department of Agriculture National Agricultural Statistics Service (2013) New York State Department of Agriculture & Markets Annual Report 2013. Pages 1-24. Albany, NY
- Determine the forage quality and yield of four species of winter cereal cover crops (triticale, cereal rye, wheat, and barley) when harvested for forage at five different growth stages in the spring.
- This objective was completed, although wheat was included in the on-farm trials in 2014 only, as dictated by farmer preference. Also, as barley was only present on one of our research farm sites, we did not include it in our final report due to a focus on our on-farm results. It is important to note that barley is less winter-hardy than cereal rye, triticale, and wheat. This trait makes barley riskier to grow in more northern regions, which is the primary reason farmers did not select winter barley for the on-farm trials.
- Quantify tradeoffs between forage quality and yield across winter cereal species and cultivars and determine the timing and duration of optimal harvest windows prior to double-cropped corn for silage.
- We were able to compare the tradeoffs between forage quality and yield for the three species we sampled, and we were able to quantify how quickly forage quality declined across growth stages as a description of the harvest window. As there was only one species and cultivar for each on-farm site, we could not assess differences between cultivars for a given species.
- Disseminate results and information about the potential of double cropping with winter cereal cover crops prior to corn silage (or other summer annual forages) to enhance farm productivity, reduce feed imports, increase profitability, and improve soil and water conservation.
- This objective was met through presentations at farmer and extension educator meetings, academic conferences, and field day demonstrations.
This project was a collaboration among our team at Cornell University, extension educators, a crop consultant, a crop manager, and the NY farmers that provided field sites for sampling. The farmers that volunteered for this study are located in south central, north central, northern, and eastern NY. At the on-farm locations, data were collected on a single cultivar for cereal rye, triticale, or winter wheat.
In-field sampling and lab procedures
Identification of winter cereal growth stages was based on the Feekes scale, which was later converted to the Zadoks scale (Zadoks et al. 1974) for statistical analyses. Using a photograph-based sampling protocol that we developed to help aid with growth stage identification, the winter cereals were harvested at five stages: two visible nodes (Zadoks 32, Feekes 7), flag leaf visible but still rolled up (Zadoks 37, Feekes 8), flag leaf completely unrolled with the ligule visible (“flag leaf stage”, Zadoks 39, Feekes 9), boot stage with the flag leaf sheath swollen but not yet open (Zadoks 45, Feekes 10), and the end of heading, just prior to flowering (Zadoks 59, Feekes 10.5). At each growth stage, four replicates were harvested with hand clippers from within a standard 20 by 100-cm sampling frame (Figure 1). These biomass samples were dried in forced-air ovens at 60°C (Undersander et al. 1993), weighed to calculate dry matter (DM) yield, and then ground in a Model 4 Wiley Mill (Thomas Scientific, Swedesboro, NJ) to 1-mm particle size.
In addition to calculating yield, the samples were analyzed for multiple forage quality indicators, including neutral detergent fiber (NDF), acid detergent fiber (ADF), 48-hour in vitro digestible NDF (NDFD), and crude protein (CP). In 2014, the laboratory analyses, with the exception of CP, were carried out at Cornell. Dry matter for the laboratory analyses was determined by drying the ground samples at 105°C for 24 hours (Undersander et al. 1993). Using the ANKOM system for fiber analysis, NDF and ADF were determined sequentially according to Van Soest et al. (1991). ANKOM F57 polyester/polyethylene filter bags with a pore size of 25 μm were used, and heat-stable alpha-amylase was used for the NDF analysis of all samples. Using the urea-containing rumen buffer detailed by Marten and Barnes (1980), the methods described by Cherney et al. (1997) were used to determine in vitro digestibility with a DAISY II200 Incubator (ANKOM Technology, Macedon, NY) and an ANKOM200 Fiber Analyzer (ANKOM Technology, Macedon, NY). The ANKOM F57 fiber filter bags were prewashed in acetone to remove a surfactant that can inhibit digestibility analyses. Rumen fluid inoculum was collected from a non-lactating, rumen-fistulated Holstein cow offered a medium quality mixed hay diet for ad libitum intake (Cherney et al. 2004). Samples weighed to 0.25 g were incubated in sealed fiber filter bags for 48 hours at 39°C, and undigested residues were then treated with neutral detergent solution. The NDFD was determined using the following formula: NDFD = [100 – (NDF remaining at t = 48 h / NDF at t = 0 h)] × 100. Duplicate samples were used for all fiber analyses and in vitro fermentation procedures, and the results were averaged. The CP content of the forage samples was determined by DairyOne Cooperative in Ithaca, NY. At DairyOne, dried, ground (1-mm particle size) samples were analyzed by combustion (Method 990.03; AOAC 2005) using a CN628 Carbon/Nitrogen Determinator (LECO, Saint Joseph, MI).
Due to a greater number of samples obtained in the second year of the project, we were not able to process the samples at Cornell. Instead, we submitted the samples to Dairyland Laboratories in Arcadia, WI, and Near Infrared Spectroscopy (NIR) was used to complete the same suite of analyses (AOAC 1990; Martin et al. 1989) that were carried out for the 2014 samples with traditional “wet chemistry” methods. Analyzing forage samples with NIR is substantially more cost effective compared with wet chemistry, which made NIR the only economically feasible approach for the larger number of samples we collected in 2015. Importantly, Dairyland Laboratories has a robust database of cool-season grass forages, which is critical for obtaining accurate results with NIR analyses.
All data were analyzed using R version 3.2.5 (R Core Team 2016), and regression diagnostics were performed to ensure that there were no outliers or influential observations, that errors exhibited independence and residuals were normally distributed, and that there was homogeneity of variances. As different laboratory methods were used to assess forage quality in 2014 and 2015, data from each year were analyzed separately. An asymptotic model that was modified with an offset (Pinheiro and Bates 2000) was used to provide more stable parameterization of the yield data and describe the relationship between winter cereal growth stage and yield,
Yr = a [ 1 – e(-e^b(Z – c)) ] , 
where Yr is the relativized winter cereal yield (%); a represents the horizontal asymptote; b denotes the logarithm of the rate constant, which corresponds to a half-life of t0.5 = log2/eb; Z represents the Zadoks growth stage; and c is the value of Z at which Yr = 0. Our data represents the exponential phase of vegetative growth captured from Zadoks 32 to 59; as such, this model provides a useful visualization of the exponential growth phase, rather than a representation of the entire growth curve of a winter cereal (which would likely be better represented with a sigmoidal model). Relativized yield (%) was calculated for each site by dividing winter cereal yield (ton DM/acre) by the maximum yield obtained at a given site and then multiplying the product by 100. Due to high winter cereal yield variability across sites, this relativization by site makes it easier to observe the relationship between growth stage and yield. The nlme function in R (nlme package; Pinheiro et al. 2016) was used for this nonlinear mixed-effects model, with block nested in site as a random effect.
To test for relationships between growth stage and RFQ among winter cereal species, we used linear mixed-effects models (lmer function in the lme4 package in R; Bates et al 2016) with block nested in site as a random effect. The University of Wisconsin Alfalfa/Grass Evaluation System (also known as MILK 2013) was used to calculate RFQ, which is described as
RFQ = [ (DMIgrass, % of BW) × (TDNgrass, % of DM) ] / 1.23 , 
where RFQ represents Relative Forage Quality; DMIgrass is the dry matter intake of a cool season grass as a percentage of body weight, BW (Moore and Kunkle 1999); TDNgrass is the total digestible nutrients for a cool season grass as a percentage of dry matter, DM (Moore and Undersander 2002); and the divisor, 1.23, is used to adjust the equation so that the mean and range is similar to Relative Feed Value (RFV) (Undersander et al. 2010).
We also used linear mixed-effects models with block nested in site as a random effect to quantify the relationship between growth stage and milk production per acre. The milk per acre calculation, which includes yield and quality metrics, is intended for ranking forage samples as opposed to predicting actual milk production (Shaver et al. nd).
[AOAC] Association of Official Analytical Chemists (1990) Official Methods of Analysis of AOAC International, 15th edn. Gaithersburg, MD: AOAC International
[AOAC] Association of Official Analytical Chemists (2005) Official Methods of Analysis of AOAC International, 18th edn. Gaithersburg, MD: AOAC International
Bates D, Maechler M, Bolker B, Walker S (2016) lme4: Linear mixed-effects models using Eigen and S4. R package version 1.1-8. http://CRAN.R-project.org/package=lme4. Accessed July 7, 2016
Cherney DJR, Cherney JH, Chase LE (2004) Lactation performance of Holstein cows fed fescue, orchardgrass, or alfalfa silage. J Dairy Sci 87:2268–2276
Cherney DJR, Traxler MJ, Robertson JB (1997) Use of ANKOM fiber determination systems to determine digestibility. In Proceedings of the NIRS Forage and Feed Testing Consortium Annual Conference. Madison, WI: NIRS Forage and Feed Testing Consortium
Marten GC, Barnes RF (1980) Prediction of energy digestibility of forages with in vitro rumen fermentation and fungal enzyme systems. Pages 61–71 in Pigden WJ, Balch CC, Graham M, eds. Standardization of Analytical Methodology for Feeds. Ottawa, ON: International Development Research Centre
Marten GC, Shenk JS, Barton II FE, eds (1989) Near Infrared Reflectance Spectroscopy (NIRS): Analysis of Forage Quality. United States Department of Agriculture, Agricultural Research Service. Agricultural Handbook No. 643. 110 p
Moore JE, Kunkle WE (1999) Evaluation of equations for estimating voluntary intake of forages and forage-based diets. J. Animal Sci. (Suppl. 1):204
Moore JE, Undersander DJ (2002) Relative Forage Quality: A proposal for replacement for Relative Feed Value. In Proceedings from the National Forage Testing Association 2002.
Pinheiro J, Bates D (2000) Mixed-effects models in S and S-PLUS. New York, NY: Springer-Verlag. Pp 337–410
Pinheiro J, Bates D, R Core Team (2016) nlme: Linear and nonlinear mixed effects models. R package version 3.1-126. http://CRAN.R-project.org/package=nlme. Accessed August 13, 2016
R Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org. Accessed June 18, 2016
Shaver R, Undersander DJ, Schwab E, Hoffman P, Lauer J, Combs D, Coors J (nd) Milk: Combining yield and quality into a single term. http://www.uwex.edu/ces/forage/pubs/milk2000.htm. Accessed September 3, 2016
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Van Soest PJ, Robertson JB, Lewis BA (1991) Methods for dietary fiber, neutral detergent fiber, and nonstarch polysaccharides in relation to animal nutrition. J Dairy Sci 74:3583–3597
Zadoks JC, Chang TT, Konzak CF (1974) A decimal code for the growth stages of cereals. Weed Res 14:415–421
Winter Cereal Yield
Due to diverse climatic conditions, soil characteristics, and crop management, winter cereal yield was variable across sites. If we were to compare absolute yields from each site, forages that received fertilizer or manure in the spring, for example, might be expected to produce greater yields than the same species grown without a spring nutrient amendment. To account for this, we expressed yield (Figure 2) as relative to the maximum yield at Zadoks 59 for a given site (Table 1). The log-rate constant (b) corresponds to the half-life of the nonlinear relationship between growth stage and relative yield in Figure 2. For triticale in 2014, the slope of the curve is less steep than cereal rye or wheat, which is reflected in the lower log-rate constant: b = -4.3, P = 0.03 for triticale; b = -2.9, P < 0.001 for cereal rye; and b = -2.7, P < 0.001 for wheat. This indicates that as triticale advanced in maturity, yield increased more slowly than cereal rye or wheat. In 2015, the regression curves are effectively the same for cereal rye (b = -2.3, P = 0.002) and triticale (b = -2.3, P < 0.001). However, interpretation of these models is limited by the differences in climate, soil type, and management. As such, Figure 2 is primarily intended to help visualize the relationship between growth stage and yield, rather than describe the differences in biomass (yield) accumulation over time.
When harvesting a winter cereal for forage in a double cropping system, flag leaf stage (Zadoks 39) is often recommended for high forage quality (Kilcer et al. 2010). For the sites in this study, the yields at flag leaf (over both years) ranged from 0.8 to 2.2 ton DM/acre for cereal rye and 1.4 to 2.2 ton DM/acre for triticale, and wheat yielded 1.1 ton DM/acre in 2014 (Table 1). In comparison, yields harvested at boot stage (Zadoks 45) ranged from 1.1 to 2.6 ton DM/acre for cereal rye and 1.9 to 2.8 ton DM/acre for triticale, and wheat yielded 1.3 ton DM/acre.
In relative terms, harvesting winter cereals prior to flag leaf stage (Zadoks 39) represented a reduction in yield of 31 to 61% when two nodes are visible (Zadoks 32) and 10 to 47% when the flag is visible but still rolled up (Zadoks 37) (Table 2). Delaying harvest until after flag leaf stage resulted in a relative yield gain of 17 to 43% at boot stage (Zadoks 45) and 39 to 91% when fully headed out (Zadoks 59). However, it is helpful to consider relative yield loss or gain in relation to the number of days gained by harvesting before flag leaf stage or lost by harvesting after flag leaf stage. For cereal rye, Zadoks 37 occurred three days earlier than flag leaf on average, and relative yield was 15% lower across both years. Delaying cereal rye harvest until boot stage took only two additional days, and relative yield was 37% higher. For triticale, harvesting five days earlier than flag leaf stage resulted in a 19% relative yield loss at Zadoks 37, whereas delaying harvest three days until boot stage resulted in a 23% relative yield gain on average. Relative yield gain for wheat was similar to triticale when delaying until Zadoks boot stage, but yield loss at Zadoks 37 was high at 47%. It should also be emphasized that the number of days between growth stages (days gained or lost) can be variable, so average day estimates are not a substitute for regular scouting and visual growth stage identification.
Relative Forage Quality
Using RFV to determine when to harvest forages, rank forages for sale, and aid in the allocation of forage to animal groups according to their quality needs has been the standard approach for many years (Undersander and Moore 2002). When calculating RFV, dry matter intake is estimated from NDF and digestible dry matter (DDM) is estimated from ADF. This approach assumes that ADF has an invariable relationship with digestibility (Undersander and Moore 2002), which has resulted in poor estimates of forage energy due to the high variability in digestibility that exists, particularly across species (Undersander and Moore 2004). Introduced more recently as an improvement over RFV, RFQ uses TDN rather than DDM (Equation 2), and thus intake has been adjusted for digestible fiber content (NDFD). As voluntary intake is affected by the digestibility of the fiber in a forage, this adjustment provides an improved estimation of forage quality and a better prediction of animal performance.As the winter cereals matured from Zadoks 32 to 59 in 2014, RFQ ranged from 222 to 144 in cereal rye, 180 to 151 in triticale, and 218 to 140 in wheat (Figure 3). Based on the linear mixed-effects model, RFQ decreased by 2.88, 1.08, and 2.90 for each increase in Zadoks growth stage for cereal rye, triticale, and wheat, respectively. Although there was a negative relationship between increasing plant maturity and decreasing RFQ across all species in 2014, triticale exhibited a much shallower slope compared with cereal rye and wheat. In 2015, RFQ for cereal rye ranged from 178 to 139 (Zadoks 37 to 59), and RFQ for triticale ranged from 198 to 179 (Zadoks 32 to 59). These ranges correspond to a decline in RFQ of 1.79 for cereal rye and 0.68 for triticale for each increase in Zadoks growth stage. Again, the slope for triticale is shallower than for cereal rye, indicating that forage quality degrades more slowly as the crop matures from two visible nodes to being fully headed out.
As the winter cereals matured from Zadoks 32 to 59 in 2014, RFQ ranged from 222 to 144 in cereal rye, 180 to 151 in triticale, and 218 to 140 in wheat (Figure 3). Based on the linear mixed-effects model, RFQ decreased by 2.88, 1.08, and 2.90 for each increase in Zadoks growth stage for cereal rye, triticale, and wheat, respectively. Although there was a negative relationship between increasing plant maturity and decreasing RFQ across all species in 2014, triticale exhibited a much shallower slope compared with cereal rye and wheat. In 2015, RFQ for cereal rye ranged from 178 to 139 (Zadoks 37 to 59), and RFQ for triticale ranged from 198 to 179 (Zadoks 32 to 59). These ranges correspond to a decline in RFQ of 1.79 for cereal rye and 0.68 for triticale for each increase in Zadoks growth stage. Again, the slope for triticale is shallower than for cereal rye, indicating that forage quality degrades more slowly as the crop matures from two visible nodes to being fully headed out.
For the sites included in our study, Zadoks 32 to 59 corresponded to sampling dates that ranged from May 7 to May 28. As this is typically a busy time of year for dairy farmers in the Northeast, an extended harvest window for obtaining high quality forage can be a major advantage. Under ideal growing conditions, cereal rye is known to mature quickly during its vegetative growth stage, which means forage quality can also decline rapidly as CP and NDFD decline and lignin content increases (Khorasani et al. 1997). Although this decline in quality tends to occur more slowly in triticale than in cereal rye, triticale typically matures later in the season. If corn silage planting is delayed in order to harvest triticale at a desired growth stage and RFQ, there might be a tradeoff between the yield and quality of triticale and corn silage yield potential.
Harvest timing is influenced by numerous considerations, such as the weather, soil conditions for field operations, and other field tasks that must be completed. Targeting a winter cereal growth stage based on its corresponding RFQ can be a useful management tool when trying to match forage with the nutritional needs of a particular group of cattle (Table 3). Knowing approximately how long the harvest window is to obtain a specific RFQ allows farmers to prioritize their management goals and associated field operations, which can improve crop and herd management. Notably, predicted RFQ was ≥140 across all growth stages, species, and years, except for cereal rye at Zadoks 59 in 2015. As a RFQ of 140 is the lower threshold suggested for first trimester dairy cows and calves (i.e., the cattle group with the highest nutrient requirements; Undersander 2003), our results suggest that harvesting winter cereals immediately prior to anthesis can still yield high quality forage. The tradeoff with corn silage yield when harvesting a winter cereal so late, however, would not be a realistic option in most situations.An alternative to the most common winter cereal-corn silage double cropping system is to replace corn silage with a later-planted summer annual forage, such as forage sorghum, which would alleviate the time constraint associated with planting corn silage in early spring. Although previous research suggests that double cropping systems are more energetically efficient and can produce more feed or forage per unit area (Jemison et al. 2012), it will be important for future research to directly compare the combined yield and forage quality potential of alternative double cropping systems to that of a single cropping system, specifically in the northeastern U.S.
An alternative to the most common winter cereal-corn silage double cropping system is to replace corn silage with a later-planted summer annual forage, such as forage sorghum, which would alleviate the time constraint associated with planting corn silage in early spring. Although previous research suggests that double cropping systems are more energetically efficient and can produce more feed or forage per unit area (Jemison et al. 2012), it will be important for future research to directly compare the combined yield and forage quality potential of alternative double cropping systems to that of a single cropping system, specifically in the northeastern U.S.
Intended as a summative assessment for ranking forages, the milk per acre calculation should not be considered predictive of actual milk production for a given winter cereal species due to difference among farms, the simplification of the actual calculation for ease of use in MILK 2013, and the omission of other important factors in the calculation that affect feed utilization, such genetic, dietary, and environmental variability (Shaver et al. nd).
As winter cereals advance in maturity from Zadoks 32 to 59, yield increases (Figure 2) and forage quality tends to decline (Figure 3). Milk production (ton/acre) shows the inherent tradeoff between yield and forage quality by incorporating winter cereal yield and numerous quality metrics (e.g., CP and 48-hour NDFD) in the calculation. In 2014, milk production increased from 2.6 to 5.2 ton/acre for cereal rye, 2.3 to 5.9 ton/acre for triticale, and 1.0 to 2.5 ton/acre for wheat (Figure 4). Predicted milk production was substantially lower in wheat compared with cereal rye and triticale due to the relatively low yields at Site 3 (Table 1). Also, the steeper slope for triticale represents the combined value of high RFQ that degrades slowly and relatively high yield. Although the cereal rye yield was high in 2014, the more rapid decline in forage quality resulted in a shallower slope than triticale. In 2015, triticale exhibited greater milk production (2.2 to 3.9 ton/acre) than cereal rye (1.0 to 2.4 ton/acre) due to higher yields (Figure 2) and RFQ across the sampled growth stages (Figure 3). In terms of milk production as a summary of yield and quality, our results indicate that triticale was the best overall performer in our experiment.
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The knowledge gained from our experiment has the potential to both increase the adoption of winter cereals grown as forage in double cropping systems, as well as improve the management of this practice. Our results have shown the important tradeoffs that farmers must consider when selecting a winter cereal species for use in a double cropping system. This information will help farmers design their crop rotation, attain their forage production goals, and improve their herd management. Without this fundamental knowledge about species selection and harvest timing, farmers that encounter challenges when growing winter cereals as double crops might be discouraged from continuing this practice. As double cropping systems can increase on-farm forage production, reduce imported feed and the greenhouse gases associated with transportation, and re-localize whole-farm nutrient cycles, increasing adoption and reducing barriers to adoption have important implications for the sustainability of our agricultural systems in the Northeast.
The feedback we have received from our farmer and extension educator collaborators suggests that we have helped lay the groundwork for future collaborations between Cornell University researchers and extension educators on double cropping systems for NY dairy farmers. This potential future effect is particularly encouraging as there are a few projects at Cornell that will be assessing both of the crops in double cropping systems, effectively building off of the work we completed in this project. Expanding on the findings from our project is an important step that will eventually provide a more complete picture of this alternative approach to annual forage production in the Northeast, thereby improving our efforts to increase the adoption of double cropping practices.
Education & Outreach Activities and Participation Summary
Our presentations of the preliminary results have emphasized the practical importance of basing the timing of winter cereal harvest on growth stage and the tradeoffs that occur when making this decision. Our outreach efforts can be summarized as follows:
- Cornell University Musgrave Research Farm Field Day, Aurora, NY, July 16, 2015 (~50 people)
- Northeast Organic Farming Association of New York Twilight Tour, Aurora, NY, June 18, 2015 (~35 people)
- Northeast Organic Farming Association of New York Winter Conference, Saratoga Springs, NY, January 22, 2016 (~20 people)
- Cornell Soil Health Train-the-Trainer Workshop, Ithaca, NY, August 5, 2015 (~50 people)
- Northeast Region Certified Crop Adviser Training Conference, Syracuse, NY, December 2, 2014 (~60 people)
- Cornell Cooperative Extension: Agriculture and Food Systems In-Service, Ithaca, NY, November 18, 2014 (~25 people)
- American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America International Annual Meeting, Long Beach, CA, November 3, 2014 (~35 people)
Now that we have completed the statistical analyses for both years of the experiment, we will summarize the findings and publish them as a New York On-Farm Research Partnership report, Cornell Cooperative Extension Agronomy Fact Sheet, and in What’s Cropping Up? (Cornell University’s field crops newsletter), all of which will stimulate greater dissemination of the benefits of this practice throughout the state. We will also analyze the complementary data from our research farm trials, synthesize the findings with our on-farm results, and submit a manuscript to a peer-reviewed academic journal for publication.
In this project, we did not conduct any direct economic analyses. However, the results of this research demonstrate that competitive yields and high quality forage can be obtained across a wide range of growth stages. As cows that are fed rations comprised of higher quality forage have been shown to produce more milk (St Pierre and Weiss 2011), improved winter cereal forage management and harvest timing for optimal quality have the potential to increase yield per acre and milk production, and thus farm income.
Hanchar et al. (2015) presented numerous scenarios for a partial budget analysis on double cropping winter cereal forage and corn silage in NY. If corn yield is not reduced due to later planting, the breakeven winter cereal yields ranged from 0.7 to 1.0 ton DM/acre if no nitrogen (N) is applied and 0.9 to 1.3 ton DM/acre if 75 lb N/acre are applied to the winter cereal at green-up. These breakeven yields increase for the scenarios in which corn yield is reduced. Based on our results, harvesting at flag leaf stage (Zadoks 39) would meet the breakeven yield with or without an N application at green-up if corn yield is not affected. However, if some corn yield loss was anticipated due to later planting, our results indicate that it might be more profitable to delay winter cereal harvest two or three days until boot stage (Zadoks 45) for a 17 to 43% yield gain. The higher yields at boot stage in our experiment (Table 1) are within range of the breakeven yields, even in the worst-case scenario (assuming a 1 ton DM/acre reduction in corn yield due to delayed planting) as reported by Hanchar et al. (2015).
These types of economic analyses highlight the importance of using optimal management practices throughout the crop rotation. Planting a winter cereal early enough in the fall, for instance, can affect winter survivability, early ground cover and weed suppression, and yield, which can in turn impact farm profitability. Consequently, our work on harvest timing for different species has important implications for the overall success and economic profitability of double cropping systems in NY.
Hanchar JJ, Ketterings QM, Kilcer T, Miller J, O’Neil K, Hunter M, Verbeten B, Swink SN, Czymmek KJ (2015) Double Cropping Winter Cereals for Forage Following Corn Silage: Costs of Production and Expected Changes in Profit for New York Dairy Farms. Ithaca, NY: Cornell Field Crops Newsletter
St Pierre N, Weiss WP (2011) How do forage quality measurements translate to value to the dairy farmer? In Proceedings of the Western Alfalfa & Forage Conference. Las Vegas, NV
All of the on-farm sites were sampled through our collaboration with extension educators, a crop consultant, and a crop manager. Farmers volunteered their fields for our research both years, reflecting their interest in the project. As previously discussed, our project has the potential to lead to greater adoption of double cropping systems among farmers through our future outreach plans, as well as the secondary effect it has had on stimulating larger projects on double cropping systems at Cornell. We are confident that through further research and outreach, the many ecological and economic benefits of double cropping systems will persuade more farmers in the Northeast to adopt these practices, especially if more effort is placed on supporting farmer-to-farmer knowledge sharing and field day-type events, and alternatives to long-season corn silage, such as forage sorghum, are considered in years with challenging weather conditions.
Areas needing additional study
As our project addressed objectives for one of the two crops in a double cropping system, there is a need for future research to assess both the winter and summer annual crop in the system. As there are numerous crop combinations that can be used, comparisons at the cropping system level will provide valuable information regarding tradeoffs and help identify optimal combinations for various management goals. Directly comparing a double cropping system to a single cropping system will also yield valuable information regarding the relative productivity, profitability, and provision of ecosystem services that are associated with each cropping system approach. Quantifying agronomic and ecological performance will be important for this research, but an economic analysis will be essential for convincing farmers to adopt these practices. Although the scope of our project was limited to one of the two crops in a double cropping system, it has produced encouraging results that these additional studies can build upon.