Grazing of cover crops in integrated crop-livestock systems.

Conventional agriculture is specializing rapidly into the management of few monoculture crops, threatening crop diversity and questioning the sustainability of extensive cropping systems. The grazing of cover crops in integrated crop-livestock systems could be a feasible biologically based technology to restore crop diversity and mitigate ecological issues in cropping systems. However, there is limited evidence on plausible synergies or trade-offs for the practice, and about how grazing plans could affect the herbage production and services from cover crops. This work assessed the effects of cattle grazing on the primary and secondary production of annual ryegrass (Lolium multiflorum) in an integrated ryegrass-soybean rotation system. Specifically, the prediction for synergistic effects of cattle grazing on the ryegrass herbage production, residual crop cover and animal performance were tested in a 2-year (2014 and 2015) study comprising a randomized complete block design of four grazing intensity treatments, replicated three times. A no-cattle grazing treatment (NG), used as control, or continuous grazing with Holstein heifers (~220kg live weight) at targeted sward heights of 5, 10, 15 and 20cm (hereafter referred as G5, G10, G15 and G20, respectively) was applied to ryegrass plots. The herbage production and residual herbage cover of ryegrass, and the average daily gain (ADG, kg/day) and live weight gain per hectare (LWG, kg/ha) of heifers were analyzed by ANOVA (P<0.05) and compared by the TukeyHSD test (P<0.05). Regression models were used to estimate relationships between herbage production, animal performance and sward height. The herbage production was 60% higher (P<0.01) for the grazing treatments compared to NG. The residual herbage for G15 and G20 was not different than that for NG and increased linearly as sward heights increased, reaching highest values for G15 and G20. Maximum ADG was 1.10kg/day for ryegrass grazed at a 20.6cm height, whereas maximum LWG was 427kg/ha for ryegrass grazed to a 16.1cm height. The results support the hypothesis for synergistic effects of using annual ryegrass as a dual forage and service cover crop. Moderate grazing intensity to sward height of 12-18cm with continuous stocking led to optimized forage production and utilization by dairy heifers.

Monitoring and assessment of ingestive chewing sounds for prediction of herbage intake rate in grazing cattle.

Accurate measurement of herbage intake rate is critical to advance knowledge of the ecology of grazing ruminants. This experiment tested the integration of behavioral and acoustic measurements of chewing and biting to estimate herbage dry matter intake (DMI) in dairy cows offered micro-swards of contrasting plant structure. Micro-swards constructed with plastic pots were offered to three lactating Holstein cows (608±24.9 kg of BW) in individual grazing sessions (n=48). Treatments were a factorial combination of two forage species (alfalfa and fescue) and two plant heights (tall=25±3.8 cm and short=12±1.9 cm) and were offered on a gradient of increasing herbage mass (10 to 30 pots) and number of bites (~10 to 40 bites). During each grazing session, sounds of biting and chewing were recorded with a wireless microphone placed on the cows' foreheads and a digital video camera to allow synchronized audio and video recordings. Dry matter intake rate was higher in tall alfalfa than in the other three treatments (32±1.6 v. 19±1.2 g/min). A high proportion of jaw movements in every grazing session (23 to 36%) were compound jaw movements (chew-bites) that appeared to be a key component of chewing and biting efficiency and of the ability of cows to regulate intake rate. Dry matter intake was accurately predicted based on easily observable behavioral and acoustic variables. Chewing sound energy measured as energy flux density (EFD) was linearly related to DMI, with 74% of EFD variation explained by DMI. Total chewing EFD, number of chew-bites and plant height (tall v. short) were the most important predictors of DMI. The best model explained 91% of the variation in DMI with a coefficient of variation of 17%. Ingestive sounds integrate valuable information to remotely monitor feeding behavior and predict DMI in grazing cows.