The Relationship between Satellite-Derived Vegetation Indices and Live Weight Changes of Beef Cattle in Extensive Grazing Conditions

The live weight (LW) and live weight change (LWC) of cattle in extensive beef production is associated with pasture availability and quality. The remote monitoring of pastures and cattle LWC can be achieved with a combination of satellite imagery and walk-over-weighing (WoW) stations. The objective of the present study is to determine the association, if any, between vegetation indices (VIs) (pasture availability) and the LWC of beef cattle in an extensive breeding operation in Northern Australia. The study also tests a suite of VIs along with variables such as rainfall and Julian day to predict the LWC of breeding cows. The VIs were calculated from Sentinel-2 satellite imagery over a 2-year period from a paddock with 378 cattle. Animal LW was measured remotely using a weighing scale at the water point. The relationship between VIs, the LWC, and LW was assessed using linear mixed-effects regression models and random forest modelling. Findings demonstrate that all VIs calculated had a significant positive relationship with the LWC and LW (p < 0.001). Machine learning predictive modelling showed that the LWC of breeding cows could be predicted from VIs, Julian day, and rainfall information, with a Lin’s Concordance Correlation Coefficient of 0.62 when using the leave-one-month-out cross-validation. The LW and LWC were greater during the wet season when VIs were higher compared to the dry season (p < 0.001). Results suggest that the remote monitoring of pasture availability, the LWC and LW is possible under extensive grazing conditions. Further, the use of VIs and other readily available data such as rainfall can be used to predict the LWC of a breeding herd in extensive conditions. Such information could be used to increase the productivity and land management in extensive beef production. The integration of these data streams offers great potential to improve the monitoring, management, and productivity of grazing or cropping enterprises.

Agricultural ecosystem services under climate change – Modelling grassland biodiversity

&lt;p&gt;European permanent grasslands not only represent a backbone for dairy and meet production, but also are hotspots of biodiversity, providing important ecosystem services to society. Understanding how climate variability and change affect the botanical composition of permanent grasslands is therefore essential for informing adaptation and helping farmers targeting sustainable development goals. It is also a key requirement for gauging climate change effects on forage quality, an aspect often overlooked in impact assessments. In this contribution, we present results of a modelling effort to understand short- and long-term changes in grassland biodiversity in response to climatic variations. We use &lt;em&gt;DynaGraM&lt;/em&gt;, a recently developed process-based model for simulating community dynamics in multi-species managed grasslands. Earlier we demonstrated that &lt;em&gt;DynaGraM&lt;/em&gt; is capable of representing the composition of permanent grasslands in the French Jura Mountains inferred from floristic relev&amp;#233;s. In these earlier investigations, we also showed that the model predicts highest, resp. lowest vegetation diversity for extensive grazing, resp. extensive mowing. We further found that the time scales of responses to external perturbations largely dependent on management, with shorter time scales (of the order of 5 to 10 years) under grazing than under mowing (of the order of 50 years).&lt;/p&gt;&lt;p&gt;Here we apply the model to examine how increasing summer aridity affects the species composition of pastures in the same geographic area. To drive the model, we use a set of climate change scenarios obtained from the CMIP5 repository, which we downscaled with the help of the LARS-WG stochastic weather generator. The results underline that management intensity modulates the impact of summer drought on both yield as well as botanical diversity, with largest changes over time in the latter under extensive grazing. Apart from presenting the results in more detail, we also discuss their practical implications and opportunities to extend in future the scope of this work.&lt;/p&gt;

Livestock Mobility Through Integrated Beef Production-Scapes Supports Rangeland Livestock Production and Conservation

Much of the world's rangelands contribute to food production through extensive grazing systems. In these systems, livestock producers, pastoralists, and ranchers move grazing animals to access variable feed and water resources to create value while supporting numerous other ecosystem services. Loss of mobility due to political, social, ecological, and economic factors is documented throughout the world and poses a substantial risk to rangeland livestock production and conservation of rangeland resources. The integration of production-scapes can facilitate livestock mobility through transportation and trade. This paper describes the beef cattle production system in California, where transporting and marketing animals integrate an extensive grazing system with intensive production systems, including feeding operations. Analysis of livestock inspection data quantifies the magnitude of livestock movements in the state and the scope of production-system integration. Over 500,000 head−47 percent of the state's calf crop—leave California rangelands and are moved to new pastures or feedyards seasonally over a 12 week period each year. Most ranchers in California, from small-scale producers (1 to 50 head) to larger producers (more than 5,000), participate in the integrated beef production system. Less than 1% of steers and heifers go from rangeland to meat processing. Like pastoralists, ranchers strategically move cattle around (and off) rangeland to optimize production within a variable climate. Ranchers indicate that their movements result from changes in forage quality and quantity and support their desire to manage for conservation objectives, including reducing fire fuels, controlling weeds, and managing for wildlife habitat. Inspection data, as well as direct observation, interviews, and surveys within the San Francisco Bay area, reveal the extent to which the region's ranchers rely on saleyards to facilitate the movement of cattle and integration of production systems. Saleyards and cattle buyers drive beef production efficiency by sorting, pricing, and moving cattle and matching them to feed resources in more intensive production systems. However, transactions lack traceability to inform policy and consumer choice. New data technologies like blockchain can provide traceability through integrated production-scapes and facilitate market development to support grazing landscapes and consumer choice.

346 Sensor-Determined Behaviors and Pasture Intake Estimation in Extensive Grazing Systems

Wearable sensor devices to monitor livestock behavior and location in extensive grazing systems can overcome limitations to collection and use of behavior data. These data enable generation of new phenotypes for genetic parameter estimation and decision support tools. Technical challenges, including device hardware and location on-animal, sensor types and modalities, data and power management, and sensor networks to enable measurement of livestock phenotypes in extensive environments, are being addressed. Wearable sensors currently used for behavior classifications include accelerometers, magnetometers and/or gyroscope within inertial measurement units, and pressure and acoustic sensors. We primarily use tri-axial accelerometers because of their reliability and richness of data for feature extraction to classify behaviors. Behavior data combined with GPS also allows location, activity and behavior mapping. Development of livestock behavior classifiers using sensors requires annotation of time-synchronized behavior recordings using video and/or a behavior recording app (e.g. CSIRO AnnoLog). Various analytical methods are used to classify behaviors from sensor data, including supervised machine-learning applied to accelerometer data. Our devices generate data for concurrent classification of behaviors including grazing, ruminating, walking, resting and drinking with reliabilities ≥ 90%. Estimates of pasture intake using behavior data across a range of environments also require validation. We have a facility to concurrently generate benchmark estimates of pasture intake using chemical markers and/or biomass disappearance while recording behaviors using sensors. To date, our R&D using pastures ranging from nutritionally optimal to severely drought affected suggest time spent grazing accounts for up to 60% of variation in pasture intake by individual beef cattle. We are assessing other sources of variation including pasture removal events (bites, tears), classes of cattle, and pasture characteristics to determine if more variation in pasture intake can be explained within extensive grazing systems to enhance development of new traits and applications for precision management.

381 Smart Farming for Extensive Grazing Ruminant Production Systems

Smart farming for extensive grazing systems includes applications linking environment and supply-chain, including metrics for climate, soils, pastures, animals and animal products to enhance management, optimization and predictions. Technological developments for remote monitoring in extensive systems have varied in their success and remain limited in uptake, and include: In-field, fixed-device monitoring of livestock numbers, water, photosynthesis and greenhouse gas emissions; body composition and physiology assessments using devices fixed to handling facilities; ground- or aerial-based livestock, water, pasture, invasive weeds and/or soil monitoring using photogrammetry or technologies including LiDAR; multi-channel, satellite-based spectrometry coupled with weather and soil grids to model and predict pasture biomass components; automated in-field liveweight measurement and drafting; virtual fencing; on- and in-animal devices to monitor location, activity, behaviors and physiology; GPS to monitor asset and personnel location. These technologies target productivity, efficiency, health and welfare of ruminants, including genetic improvement, and more efficient, sustainable resource use, including soils, pastures and water, to improve individual ruminants and grazing systems. We have developed and validated technologies for remote, in-field determination of animal behaviors, pasture characteristics including availability and disappearance mapping for calibration and validation of satellite images, and pasture intake under varying grazing conditions. Examples of our R&D include experimental on-animal sensor devices to classify and monitor behaviors in extensive systems, and development of a GrazingApp linking satellite imagery, weather and soil information to model and predict animal production. Development of these technologies has required analytical methods for big data, including machine learning and artificial intelligence. These and other applications that function in near to real-time are enhanced by management and aggregation of enormous amounts of data generated by sensors and other devices into useful metrics before transmission using wireless networks. These metrics are the basis for data-driven management decisions that reduce risk and enhance profit for grazing enterprises.

Improve Pasture or Feed Grain? Greenhouse Gas Emissions, Profitability, and Resource Use for Nelore Beef Cattle in Brazil’s Cerrado and Amazon Biomes

Economic development, international food and feed demand, and government policies have converted Brazil’s natural ecosystems into agricultural land. The Integrated Farm System Model (IFSM) was evaluated using production, economic, and weather data collected on two cooperating farms in the Legal Amazon and Cerrado biomes in the Midwest state of Mato Grosso, Brazil. Three sustainable agricultural intensification strategies, namely grain supplementation, pasture re-seeding, and pasture fertilization were simulated in IFSM with double the beef cattle stocking density compared to extensive grazing. Livestock dry matter consumption simulated in IFSM was similar for pasture grazing estimates and actual feed consumed by beef cattle on the two collaborating farms. Grain supplementation best balanced beef production and profitability with lower carbon footprint compared to extensive grazing, followed by pasture fertilization and pasture re-seeding. However, pasture re-seeding and fertilization had greater use of water and energy and more nitrogen losses. Human edible livestock feed use was greatest for grain supplementation compared to other modeled systems. While grain supplementation appears more favorable economically and environmentally, greater use of human edible livestock feed may compete with future human food needs. Pasture intensification had greater human edible feed conversion efficiency, but its greater natural resource use may be challenging.