Precision livestock farming applied to grazingland monitoring and management—A review

Abstract

To meet the expected demand for food while protecting animal welfare, environmental sustainability, and profitability, animal production efficiency must improve. Improvements in grazinglands management techniques can impact livestock production efficiency. The current stage of artificial intelligence development, mainly machine learning techniques, remote sensing (RS), and precision agriculture technologies, automatizes data collection and raises the monitoring capacity to support on‐farm decision‐making. This literature review presents current developments in precision livestock farming (PLF) applied to grazinglands monitoring and management, demonstrates some knowledge gaps, and discusses potential solutions of grazinglands management issues. Although the implementation of precision technologies in grazing systems is advancing rapidly, challenges, such as lack of reliable reference data and low variability of datasets used to calibrate models, are examples of constraints to be addressed in future studies. More effort in terms of relationship strengthening between farmers and researchers, benefits elucidation, cooperation among professionals with different expertise, and software or app development must be directed to make the knowledge accessible and largely implemented in field conditions.

Non-Destructive Methods Used to Determine Forage Mass and Nutritional Condition in Tropical Pastures

“…Additionally, there is a lack of suitable applications, programs, and proper training for the use of remote tools in predicting forage biomass (Figure 2). Brestas et al [92] highlight that despite rapid advancements in the integration of precision technologies into pasture systems, significant challenges persist and must be addressed in future research. These challenges encompass the lack of reliable reference data [Figure 2] and the limited diversity in the datasets used for model calibration.…”

“…To facilitate the broad and effective dissemination of this knowledge in field environments, there is an imperative need for greater emphasis on strengthening relationships between farmers and researchers, transparently presenting the benefits, promoting collaboration among experts from various domains, and developing software or applications that make the knowledge accessible and easy to apply. Brestas et al [92] highlight that despite rapid advancements in the integration of precision technologies into pasture systems, significant challenges persist and must be addressed in future research. These challenges encompass the lack of reliable reference data [Figure 2] and the limited diversity in the datasets used for model calibration.…”

Farming in the digital age: Unleashing the power of farming as a service (FaaS)

“…FaaS integrates AI algorithms and user-friendly interfaces for proactive pest and nutrient management, offering new opportunities for sustainable farming (Gebresenbet et al, 2023) [8] . Technological integration in modern agriculture, including IoT, smart sensors,image processing, data analytics, artificial intelligence, and machine learning, addresses challenges like water scarcity and pests while improving productivity (Bretas et al, 2023) [6] . These technologies enable intelligent agriculture systems, aiding in crop monitoring, animal production, food safety, and farm management.…”