Technological advancements have made hand-held near infrared (NIR) spectrometers more affordable and more accurate, creating interest in on-farm application for forage management. The objective of this study was to evaluate the ability of a hand-held NIR spectrometer to predict grass percentage within fresh alfalfa (Medicago sativa L.):grass mixtures. Forage samples were collected at a range of maturities and varieties during the 2021 and 2022 growing seasons from multiple locations in New York. Fresh forage samples were chopped, and pure species were combined into known proportions on a dry matter basis, resulting in 534 samples. Analysis was carried out on NIR spectra collected from a hand-held NeoSpectra spectrometer using stationary and sliding scanning techniques. Development of calibration models was completed using partial least squares regression with cross validation. The best performing calibration model using absorbance was from the sliding scanning technique with preprocessing consisting of mean-centering (R2 = 0.89, root mean square error of prediction [RMSEP] = 13.7%, and ratio of prediction to deviation = 2.53). A total of 84% of the samples were correctly classified when the grass component was lower than 40%. For samples with the grass component above 40%, a total of 94% of the samples were correctly classified. Correct sample classification is critical considering that the extension recommendation in New York is to reseed alfalfa fields when the grass component exceeds 40% of the sward on a botanical composition basis. This research demonstrates that NIR technology has potential to provide the agricultural industry with rapid, non-destructive, and affordable information to allow farmers and consultants to predict grass proportion within alfalfa:grass fresh forage mixtures in real time. Hand-held NIR spectrometers show promise for estimating grass proportion in fresh alfalfa:grass forage mixtures. The sliding scan technique best predicts grass proportions in alfalfa:grass mixtures over stationary techniques. On-farm NIRS analysis of forage crops has the potential to optimize nutrient management on dairy farms.

Much of the corn acreage in New York state is harvested as corn silage and moisture assessment in the field is necessary for predicting harvest timing, but moisture estimation visually is very problematic, particularly for brown-midrib (BMR) hybrids. Our goal was to assess plant moisture relationships between BMR and conventional (CONV) corn hybrids, and to identify metadata that may assist in the prediction of whole plant moisture based on ear moisture estimations. In 2023, 202 corn fields were sampled in central New York from August 18 to September 27. A total of 41 different corn hybrids were sampled, with relative maturity (RM) ranging from 84 to 112 days, and 29% of the fields sampled were planted to BMR hybrids. Five representative plants per field were evaluated for plant height, ear length and width, and ear, stover, and whole plant moisture. Estimation of dry ear:stover ratio would be helpful in estimating whole plant moisture based on ear moisture. Ear length was not related to ear:stover ratio, while plant height and ear width were weakly but significantly correlated with ear:stover ratio. Ear moisture was highly correlated with ear:stover ratio (BMR, r = -0.95; CONV, r = -0.90), and highly correlated with whole plant moisture (BMR, r = 0.97; CONV, r = 0.98). Ear moisture averaged 1 to 2% units lower throughout the sampling season for BMR compared to CONV hybrids, while stover moisture averaged 1 to 2% units higher for BMR compared to CONV hybrids prior to optimum harvest moisture. Whole plant moisture declined about 0.6%units/day and was relatively similar across RM groups. The primary key to making good corn silage is harvesting at the proper moisture content. It is extremely difficult to visually evaluate standing corn for moisture content. In particular, brown-midrib hybrids often are diseased and lose their green color but maintain much of their moisture content. It is possible to scan corn ears with a handheld near infrared reflectance (NIR) spectrometer and predict ear moisture, as well as whole plant moisture, from only an ear scan. Although ear moisture is well related to ear:stover ratio and to whole plant moisture, it would be helpful if other data could be used to provide more information on the ear:stover ratio. We collected five corn plants per field from 202 fields in central New York in 2023, and we measured plant height, ear length, ear width, as well as ear, stover and whole plant moisture. Twenty-nine percent of the plants collected were brown-midrib hybrids. Ear width and plant height were weakly but significantly correlated with ear:stover.

Alfalfa-grass mixtures sown in the northeastern United States provide high-quality dairy forage, and meadow fescue (Festuca pratensis Huds.) may improve the quality of these mixtures. Our objectives were to evaluate competitiveness and nutritive value of nine meadow fescue (MF) cultivars in New York State at spring harvest. Three farms, two in central New York State and one in northern New York state, were used. Conventional alfalfa (Medicago sativa L.) was sown (15 lb acre-1) to nine MF cultivars (three tetraploid and six diploid) and one tall fescue Lolium arundinaceum (Schreb.) 'Darbysh' cultivar in a randomized complete block design with four field replicates at each field site at three seeding rates (1, 2, and 3 lb acre-1). Grass proportion in mixtures was estimated visually. Grass samples were collected shortly before first harvest and analyzed for neutral detergent fiber, neutral detergent fiber digestibility (NDFD), acid detergent fiber, in vitro digestibility, and crude protein. Most meadow fescue cultivars maintained a grass proportion between 20%-45% across farms and growing seasons when seeded at 1lb acre-1. Seeding rates above 1lb acre-1 resulted in grass proportions above the recommended 20-30% grass proportion rate. Drought in early 2022 resulted in an average drop in grass percentage of 16.9% units for meadow fescue in mixtures, compared to 2021. Nutritive value of cultivars varied among farms and over growing seasons. Meadow fescue cultivars averaged 2.7% units higher NDFD than tall fescue, and cultivars with consistently high NDFD were Hidden Valley, SW Revansch, SW Minto, and Schwetra. Tetraploid cultivars averaged 4.0% units lower NDF compared to diploid cultivars, which is very advantageous for grass in alfalfa-grass mixtures. Dairy cattle require high-quality feed to produce optimal milk quantity and quality. The forage included in cattle diets makes up the single largest component of cows' diet. Perennial grasses and legumes, especially alfalfa, are environmentally sustainable. As new varieties continually develop to provide better forage, management of forage mixtures must be reevaluated. Our study evaluated nine new varieties of meadow fescue and one tall fescue variety in a mix with alfalfa. Grass was planted at three New York producers' farms at 1 pound, 2 pounds, and 3 pounds of pure live grass seed per acre with the alfalfa. Past research has suggested that grass proportion of the grass-alfalfa not exceed 20-30% and not be above a 50% proportion after establishment. Meadow fescue cultivars 'Pradel' and 'Tetrolina' were within the suggested limits of grass-alfalfa proportion at the 1 pound seeding rate. Other seeding rates resulted in grass proportion higher than the suggested range. Dairy cattle require high-quality feed to produce optimal milk quantity and quality. The forage included in cattle diets makes up the single largest component of cows' diet. Perennial grasses and legumes, especially alfalfa, are environmentally sustainable. As new varieties continually develop to provide better forage, management of forage mixtures must be reevaluated. Our study evaluated nine new varieties of meadow fescue and one tall fescue variety in a mix with alfalfa. Grass was planted at three New York producers' farms at 1 pound, 2 pounds, and 3 pounds of pure live grass seed per acre with the alfalfa. Past research has suggested that grass proportion of the grass-alfalfa not exceed 20-30% and not be above a 50% proportion after establishment. Meadow fescue cultivars 'Pradel' and 'Tetrolina' were within the suggested limits of grass-alfalfa proportion at the 1 pound seeding rate. Other seeding rates resulted in grass proportion higher than the suggested range.

This study investigates the efficacy of handheld Near-Infrared Spectroscopy (NIRS) devices for in-field estimation of forage quality using undried samples. The objective is to assess the precision and accuracy of multiple handheld NIRS instruments—NeoSpectra, TrinamiX, and AgroCares—when evaluating key forage quality metrics such as Crude Protein (CP), Neutral Detergent Fiber (aNDF), Acid Detergent Fiber (ADF), Acid Detergent Lignin (ADL), in vitro Total Digestibility (IVTD)and Neutral Detergent Fiber Digestibility (NDFD). Samples were collected from silage bunkers across 111 farms in New York State and scanned using different methods (static, moving, and turntable). The results demonstrate that dynamic scanning patterns (moving and turntable) enhance the predictive accuracy of the models compared to static scans. Fiber constituents (ADF, aNDF) and Crude Protein (CP) show higher robustness and minimal impact from water interference, maintaining similar R2 values as dried samples. Conversely, IVTD, NDFD, and ADL are adversely affected by water content, resulting in lower R2 values. This study underscores the importance of understanding the water effects on undried forage, as water‘s high absorption bands at 1400 and 1900 nm introduce significant spectral interference. Further investigation into the PLSR loading factors is necessary to mitigate these effects. The findings suggest that, while handheld NIRS devices hold promise for rapid, on-site forage quality assessment, careful consideration of scanning methodology is crucial for accurate prediction models. This research contributes valuable insights for optimizing the use of portable NIRS technology in forage analysis, enhancing feed utilization efficiency, and supporting sustainable dairy farming practices.

Harvesting corn at the proper maturity is important for managing its nutritive value as livestock feed. Standing whole-plant moisture content is commonly utilized as a surrogate for corn maturity. However, sampling whole plants is time consuming and requires equipment not commonly found on farms. This study evaluated three methods of estimating standing moisture content. The most convenient and accurate approach involved predicting ear moisture using handheld near-infrared reflectance spectrometers and applying a previously established relationship to estimate whole-plant moisture from the ear moisture. The ear moisture model was developed using a partial least squares regression model in the 2021 growing season utilizing reference data from 610 corn plants. Ear moisture contents ranged from 26 to 80 %w.b., corresponding to a whole-plant moisture range of 55 to 81 %w.b. The model was evaluated with a validation dataset of 330 plants collected in a subsequent growing year. The model could predict whole-plant moisture in 2022 plants with a standard error of prediction of 2.7 and an R2P of 0.88. Additionally, the transfer of calibrations between three spectrometers was evaluated. This revealed significant spectrometer-to-spectrometer differences that could be mitigated by including more than one spectrometer in the calibration dataset. While this result shows promise for the method, further work should be conducted to establish calibration stability in a larger geographical region.

Prediction models of different types of forage were developed using a dataset of near-infrared reflectance spectra collected by three handheld NeoSpectra-Scanners and laboratory reference values for neutral detergent fiber (NDF), in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), crude protein (CP), Ash, and moisture content (MO) from a total of 555 undried ensiled corn, grass, and alfalfa samples. Data analyses and results of models developed in this study indicated that the scanning method significantly impacted the accuracy of the prediction of forage constituents, and using the NEO instrument with the sliding method improved calibration model performance (p < 0.05) for nearly all constituents. In general, poorer-performing models were more impacted by instrument-to-instrument variability. The exception, however, was moisture content (p = 0.02), where the validation set with an independent instrument resulted in an RMSEP of 2.39 compared to 1.44 where the same instruments were used for both calibration and validation. Validation model performance for NDF, IVTD, NDFD, ADL, ADF, Ash, CP, and moisture content were 4.18, 3.86, 6.14, 1.10, 2.75, 1.42, 2.71, and 1.67 for alfalfa-grass silage samples and 3.22, 2.21, 4.55, 0.38, 2.07, 0.50, 0.51, and 1.62 for corn silage, respectively. Based on the results of this study, the handheld spectrometer would be useful for predicting moisture content in undried and unground alfalfa-grass (R2 = 0.97) and corn (R2 = 0.93) forage samples.

Accurate forage dry matter (DM) concentration estimation is essential for maximizing animal performance and minimizing feed costs. One possible method of estimating DM for rebalancing rations daily involves the use of hand-held near infrared reflectance spectrometer instruments. The SCiO Cup is one of the hand-held instruments that could be used to estimate forage DM, but a thorough evaluation of its effectiveness has not been conducted. Haylage samples (n = 600) from 143 bunker silos were collected across New York State over three years, and vacuum packed for eventual analysis using a SCiO Cup. Samples ranged from pure alfalfa (Medicago L.) to pure grass but were mostly from mixed species. All but one sample received a DM value estimated from several available calibrations pre-loaded in the device. Sixty samples (representing 10% of the sample population) were too wet or dry to generate a result using the mixed silage calibration. For the remaining 90% of samples, SCiO Cup DM estimates were within 3.22%units of oven DM 80% of the time. Precision of the instrument evaluated with multiple scanning of samples using the mixed silage calibration was very good, with the average standard deviation of three values of 0.40 (n = 200). The mixed silage calibration was more effective for predicting DM of this set of haylages than either legume or grass silage calibrations. The SCiO Cup has good precision for estimating haylage dry matter (DM).The mixed silage calibration was not able to estimate DM of very wet or very dry haylage samples.The mixed silage calibration estimated DM value was within 3.2%units of oven DM 80% of the time.

A study was conducted over an eight-month period in 2016 with the objective of implementing and evaluating a semi-stall-feeding system in comparison to the traditional grazing system on goat farms in Kandhamal district, Odisha. Sixteen households across two villages were randomly selected from a sampling frame of farmers that owned at least four adult female goats. Four households from each of the two villages were randomly assigned to the control group (C) that engaged in traditional grazing of goats and four households to the supplemented group (S) which applied semi-stall-feeding (concentrate feeding at 2% of the herd's total body weight of goats). The potential outcomes of a transition to intensive goat production system were assessed by monitoring growth, survival, and milk quality on goat farms. Farmers' input concerning technology adoption was documented. Survivability was 4.26 times greater for kids in the supplemented than control group. Adult and kid weights did not differ among the groups. Sixty-three per cent of participating farmers were interested in supplementing their goats after the project and willing to pay between 1.5 to 15.0 USD/ household per month on goat feed. Results from this study will help policymakers about the potential impact of reducing goat dependence on grazing land and tribal farmer receptivity to goat system intensification.

Advanced manufacturing techniques have enabled low-cost, on-chip spectrometers. Little research exists, however, on their performance relative to the state of technology systems. The present study compares the utility of a benchtop FOSS NIRSystems 6500 (FOSS) to a handheld NeoSpectra-Scanner (NEO) to develop models that predict the composition of dried and ground grass, and alfalfa forages. Mixed-species prediction models were developed for several forage constituents, and performance was assessed using an independent dataset. Prediction models developed with spectra from the FOSS instrument had a standard error of prediction (SEP, % DM) of 1.4, 1.8, 3.3, 1.0, 0.42, and 1.3, for neutral detergent fiber (NDF), true in vitro digestibility (IVTD), neutral detergent fiber digestibility (NDFD), acid detergent fiber (ADF), acid detergent lignin (ADL), and crude protein (CP), respectively. The R2P for these models ranged from 0.90 to 0.97. Models developed with the NEO resulted in an average increase in SEP of 0.14 and an average decrease in R2P of 0.002.

Understanding factors influencing conventional medical knowledge (CMK), general attitudes and risk perceptions of zoonotic diseases among rural residents who face risk of exposure to such diseases is important for human, livestock, and wildlife health. Focusing on Maasai from Makame, Kiteto District (Tanzania) who largely maintained a semi-nomadic lifestyle, we evaluated respondents' CMK of causes, symptoms, treatments, and prevention methods of rabies, brucellosis, and anthrax. In addition, we identified socio-demographic correlates of CMK with respect to the target zoonoses. Finally, we assessed the relative frequency of practices that increase the risk of pathogen transmission, and compared the risk perception of the three diseases. We conducted structured interviews with Maasai respondents (n = 46) in six sub-villages of Makame and considered education, gender, age, and wealth (indicated by standardized number of livestock) as potential correlates of CMK. Respondents had greater CMK of rabies and anthrax, but feared anthrax the most. Receiving formal education increased rabies CMK (p ≤ 0.05). The CMK of anthrax and brucellosis was not associated with any of the tested variables (p > 0.05). Risk perceptions were correlated with knowledge scores for rabies and anthrax (p ≤ 0.05), and multiple interviewees reported engaging in practices that potentially enhance pathogen transmission. Specific socio-demographic attributes (i.e., formal education) may explain the observed variation in CMK of zoonotic diseases. This information can be used to develop and tailor health education programs for specific at-risk groups.

HIGHLIGHTS Quadratic relationships were established to relate ear moisture or stover moisture to whole plant moisture, and they explained 90% and 84% of whole plant moisture, respectively. Based on our observations, the moisture content of a corn field can be estimated within + 1% w.b. in 19 out of 20 fields by sampling 5-10 plants. The calibration offered by SCiO was successful at predicting oven-dried moisture content based on traditional NIRS metrics of R 2 = 0.92, RMSE = 3.6, RPD = 3.2, and RER = 15. However, the 95% prediction bands were + 6.9% w.b., which would indicate little utility in estimating ear moisture content. Based on a prediction model that was developed using the data collected for this study, a significant instrument-to-instrument bias was observed, indicating the necessity of including multiple SCiO devices in calibration spectra collection. ABSTRACT. Determining the appropriate time to harvest whole-plant corn is an essential factor driving the successful preservation via anaerobic fermentation (ensiling). The current options for timely on-farm monitoring of corn moisture in the field include selecting a set of representative plants, chopping and drying a subsample, or harvesting a portion of the field using a harvester equipped with an on-board moisture sensing system. Both methods are time-consuming and expensive, limiting their practicality for harvest decision-making. This work’s objective was to develop a practical solution that utilizes the moisture content of the ear to estimate whole-plant moisture. An improvement of this method was also considered that utilized a hand-held near-infrared reflectance spectroscopy (NIRS) device to predict ear moisture in situ. Based on the data collected during this work, a quadratic relationship was developed where ear moisture explained 90% of the variability in whole-plant corn moisture. However, based on our observations, the hand-held NIRS evaluated would have little utility in predicting whole-plant corn moisture with either the calibration developed here or provided by the manufacturer. The manufacturer’s prediction model yielded the best result with an R 2 of 0.92, and a ratio of performance to deviation of 3.19. However, the 95% prediction band was + 6.85% w.b. Finally, we determined that for a corn field uniform in appearance, sampling five to ten plants is likely to provide a reasonable estimate of field moisture. Keywords: Corn silage, Forage analysis, Harvest timing, Moisture content, NIRS.

Objective: We evaluated variation in sampling and analysis of forages and quantified day-to-day variation in silages and TMR in typical New York dairy farms. Materials and Methods: Alfalfa-grass haylage and corn silage samples were collected daily from 7 dairies, for a total of 24 wk for haylage, 22 wk for corn silage, and 16 wk for TMR samples. Multiple samples also were collected at 4 dairies to evaluate both sampling and subsampling variation. Results and Discussion: Based on SD, sampling for DM varied from actual DM by up to +/- 2 percentage units. Haylage was more variable than corn silage, likely due in part to variability of grass percentage within fields. The most practical parameter to measure for daily rebalancing of rations is DM, and DM had considerable day-to-day variability for haylage, with less variability for corn silage and TMR. Assuming a 7 percentage-unit threshold for a weekly range in DM is great enough to benefit from daily rebalancing, this threshold was exceeded 14% of weeks for corn silage, 25% of weeks for TMR, and 42% of weeks for haylage. A better understanding of day-to-day variability will help determine the accuracy required for on-farm silage moisture determinations. Implications and Applications: Nutrient composition of fed rations differs from formulated rations due to day-to-day variation in DM concentration and nutrient composition of forages. Although providing excess feed likely will mitigate the effects of day-to-day silage variability, it not only increases feed costs but also is less environmentally acceptable.

Predicting forage quality is important when marketing hay, predicting when to harvest a first cutting, moving animals onto pasture or to another pasture, predicting the quality of a silage, predicting how well a forage will perform in a given animal's ration, and increasingly, for use in soil nutrient management. This chapter gives the reader a historic analysis of forage quality analyses and why prediction of forage quality is more important than ever. Crude protein (CP) reflects a forage's potential to provide protein that herbivores require. Forage CP is based on a chemical analysis of the amount of nitrogen, which is the building block for amino acids that make up proteins. Increasingly, predicting total forage value requires that forage testing reports include estimates of nutritive value, including CP and one or more expressions of available energy (e.g. digestible dry matter, total digestible nutrients, digestible energy, and net energy).

Livestock support the livelihoods of one billion people in Africa, Asia and Latin America, but the productivity of animals remains low, reducing the potential of the sector to support higher incomes and better nutrition. Improved livestock feeding has been identified as the most important step towards higher productivity. This scoping review assessed the evidence for the uptake of improved ruminant livestock feed options, the effect of this uptake on livestock productivity and the degree to which this improves smallholder farmer livelihoods. In total, 22,981 papers were identified, of which 73 papers were included in the final analysis after a rigorous double-blind screening review. Only papers that reported farmers’ decision to use a new feed intervention were selected, thereby excluding feeding trials and participatory feed assessments. Of the 73 papers, only 6 reported combined evidence of adoption, effect on productivity and livelihood changes. A total of 58 papers looked at adoption, 19 at productivity change and 22 at livelihood change. This scoping review highlights the gap in evidence for the adoption of new livestock feeding practices and provides recommendations to support farmers’ uptake of feed interventions.

Alfalfa (Medicago sativa L.)-grass mixtures are often used to provide high-quality dairy forage in the northeastern United States. Meadow fescue (Festuca pratensis Huds.) is popular as a companion for alfalfa in the region because of its high nutritive value. We aimed to evaluate alfalfa-meadow fescue mixtures in northern environments that are unsuited to alfalfa production. Our study was conducted in the year after seeding at two northern New York sites. A reduced-lignin [fall dormancy (FD) = 4] and a conventional alfalfa (FD = 3) were seeded with diploid and tetraploid meadow fescue varieties in replicated plots, with five seeding rates for meadow fescue. Dry matter yield increased linearly with increased meadow fescue seeding rate. The meadow fescue percentage of mixtures had a curvilinear response to meadow fescue seeding rate, increasing from 44% at the 0.5 lb acre(-1) seeding rate to 67.4% at the 4 lb acre(-1) rate. Tetraploid meadow fescue had a smaller grass percentage in mixtures and higher nutritive value than a diploid meadow fescue. Reduced-lignin alfalfa was not greatly different in nutritive value from a conventional cultivar under unfavorable environmental conditions for alfalfa production. We conclude that meadow fescue should he seeded at a very low rate (1 lb acre(-1)) with alfalfa in northern environments that are unsuitable for alfalfa. Moreover, reduced-lignin alfalfa (FD = 4) may not result in large increases in nutritive value compared with conventional cultivars selected for high nutritive value on marginal northern sites better suited to FD 3 cultivars.

Improvements in alfalfa (Medicago sativa L.) forage nutritive value through reduction in lignin content have the potential to increase economic returns when it is fed to dairy cattle, either as a pure species or in mixture with perennial grasses. Our objective was to assess yield and nutritive value of reduced-lignin alfalfa in monoculture and in binary mixtures with perennial grass. Studies were seeded in the spring of 2016 in Ithaca, NY, Lexington, KY, and Rosemount, MN, and harvested in 2016, 2017 and spring of 2018. WL 355.RR and Hx14376 reduced-lignin alfalfa were seeded as pure stands and in mixture with festulolium [Festulolium loliaceum (Hudson) P.V. Fournier], meadow fescue [Schedonorus pratensis (Huds.) P. Beauv.; syn. Festuca pratensis Huds.; syn. Lolium pratense (Huds.) Darbysh.], or orchardgrass (Dactlyis glomerata L.). The experimental design was a randomized complete block, with a split plot arrangement of cutting management treatments in Ithaca and Lexington, harvesting at alfalfa first bud and first flower stages, and one harvest treatment in Rosemount at first flower. Samples were hand-harvested for dry matter (DM) and nutritive value determinations. Reduced-lignin alfalfa averaged between 11 and 18.5% less lignin over the 3 yr of the study, and averaged between 5.8 and 10.6% greater fiber digestibility, compared to a normal cultivar. Alfalfa response was relatively consistent across regions, but grass response was variable across regions for both grass proportion in mixtures and nutritive value. Perennial grasses grown with alfalfa must be evaluated on a regional basis to produce meaningful results.

Forage sorghum [Sorghum bicolor (L.) Moench] is a viable alternative to corn silage (Zea mays L.) in double cropping rotations with forage winter cereals in New York due to a later planting date and potentially earlier harvest date of forage sorghum than is typical for corn silage. Our objective was to determine whether harvest of brachytic dwarf brown midrib forage sorghum can take place before the currently recommended soft dough harvest time while maintaining dry matter (DM) yield, forage nutritive value, and total mixed ration performance. Seven trials were conducted on 2 research farms in central New York from 2014 to 2017. Forage sorghum received 1 of 2 fertilizer N rates at planting (112 and 224 kg of N/ha). Stands were harvested at boot, flower, milk, and soft dough stages. Forage samples were analyzed for nutritive value and substituted for corn silage in a typical dairy total mixed ration at varying amounts using the Cornell Net Carbohydrate and Protein System. Timing of harvest affected yield and forage nutritive value for each individual trial and across trials, and the effects were independent of N fertilizer application rate. Averaged across trials, yield ranged from 10.7 Mg of DM/ha for the boot stage to 13.5, 15.2, and 15.8 Mg of DM/ha for the flower, milk, and soft dough stages, respectively. For individual trials, yield either remained constant with harvest beyond the flower stage (4 trials), or beyond the milk stage (1 trial), whereas for 2 trials yield increased up to the soft dough stage. At the later harvest stages, DM, starch, and nonfiber carbohydrates were increased, whereas crude protein, neutral detergent fiber, and 30-h neutral detergent fiber digestibility were decreased. Without adjusting for DM intake, substitution of corn silage by forage sorghum harvested at the soft dough stage resulted in stable predicted metabolizable energy allowable milk, whereas the reduced starch content of earlier harvested sorghum resulted in less metabolizable energy allowable milk with greater substitution of corn silage for sorghum. Forage sorghum can be harvested as early as the flower or milk stage without losing DM yield, allowing for timely planting of forage winter cereal in a double cropping rotation. However, energy supplementation in the diet is needed to make up for reduced starch concentrations with harvest of sorghum at flower and milk growth stages.

A rotation trial with forage triticale (xTriticosecaleWittmack) and forage sorghum (Sorghum bicolor[L.] Moench) was conducted in central New York from 2016 to 2018. Treatments included four timings of sorghum harvest followed by next‐day triticale planting, five triticale spring N rates (0, 34, 67, 101, 135 kg N ha−1), and two N treatments applied at sorghum planting (fertilized, +N, and unfertilized, –N) using a randomized complete block split‐split‐plot design in four replications. The most economic rate of N (MERN) for triticale in spring 2016 was 86 kg N ha−1with a yield at the MERN of 4.0 Mg dry matter (DM) ha−1. In fall 2016 and spring 2017, total forage yield (triticale plus sorghum) did not increase after the mid‐September harvest for the +N and 135 kg spring N ha−1plots (23.8 Mg DM ha−1average). In fall 2017 and spring 2018, there was no difference in total forage yield across harvest timings (13.4 Mg DM ha−1average), likely due to fewer growing degree days (GDD) that year. We recommend harvesting sorghum ∼1150 GDD after planting or at the soft‐dough stage. Earlier harvests resulted in lower yield but greater digestibility and crude protein. Spring‐applied N did not affect forage sorghum yield or nutritive value. Sorghum fertilized with N resulted in MERNs of 0 kg N ha−1for the following triticale crop. Fertilizing sorghum according to N needs and timely harvest can support both sorghum and triticale yields without having to fertilize triticale in the spring.Core IdeasForage sorghum requires N at planting in non‐manured systems.Forage sorghum can be harvested after 1150 growing degree‐days without a yield reduction.Triticale following fertilized sorghum may not need additional N in the spring.Spring N applied to triticale may not affect forage sorghum yield.