In the heart of Brazil, where vast expanses of pasture stretch out under the tropical sun, a new tool is emerging to help farmers and ranchers make more informed decisions about their land and livestock. This tool isn’t a new breed of cattle or a novel type of fertilizer, but a sophisticated computer model called the Agricultural Crop Simulator (AgS). Developed to focus on crops pivotal to the Brazilian economy, AgS has recently been put to the test in a study published in the journal ‘Grasses’, led by Fernando Oliveira Bueno from the Pasture and Animal Feed Research Division at Instituto de Zootecnia.
The study aimed to evaluate the performance of AgS in simulating the biomass production of Marandu palisadegrass (Urochloa brizantha cv. Marandu) under rotational stocking methods, a common practice in pasture-based animal production systems. The initial simulations, however, underestimated leaf and total biomass production, regardless of pre-grazing height. This discrepancy highlighted the differences between cutting and grazing methods, necessitating further model calibration.
“Initially, the model underestimated biomass production, but after addressing differences related to leaf regrowth, we saw significant improvements,” Bueno explained. The team recalibrated the model, focusing on biomass allocation to leaves and stems, which reduced the mean error and the relative root mean square error (rRMSE) in the 25 cm treatment. This recalibration demonstrated the model’s potential for simulating rotational stocking after adjustments were made.
The implications of this research are substantial for the agriculture sector. By accurately simulating biomass production under different management practices, AgS can support planning and management decisions, ultimately enhancing forage production and efficiency. This can lead to improved livestock productivity, better resource utilization, and increased profitability for farmers and ranchers.
The study also opens up avenues for future research. Bueno suggests that future calibrations should consider different management and environmental conditions, which could further enhance the model’s accuracy and applicability. This ongoing refinement process is crucial for developing a tool that can truly support the diverse and dynamic needs of pasture-based systems.
As the agricultural sector continues to evolve, tools like AgS will play an increasingly important role in helping farmers and ranchers navigate the complexities of modern agriculture. By providing a more accurate and comprehensive understanding of biomass production, AgS can help shape the future of pasture-based animal production systems, contributing to a more sustainable and productive agricultural sector.