Satellites predict how much food there will be for animal production

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AllField – Fodder is a fundamental resource for livestock systems, as it is the main food of animals. Knowing how your productivity varies in space and throughout the year is necessary to estimate the number of animals you can have in a field without degrading it and the kilos of meat or liters of milk that will be produced.

A study by the Faculty of Agronomy of the University of Buenos Aires (FAUBA) in the south-western province of Buenos Aires developed a tool that, based on mathematical models and satellite information, allows to predict the productivity of fodder from precipitation.

"When we talk about fodder we mean plant, natural or implanted species that are produced in the fields to feed animals. For example, ruminants such as cows, goats, and sheep consume fodder to produce meat or milk. A large part of Argentine livestock farming is made by directly grazing forage resources," explained Martín Garbulsky, professor of the Chair of Forrajiculture at FAUBA, researcher at the Institute of Physiological and Ecological Research Linked to Agriculture (IFEVA, UBA-Conicet) and director of the Specialization in Pastoral Systems Management of the School for Graduates of FAUBA.

In this regard, he added that the availability of fodder determines the number of animals that can be fed in a field and also how much meat or milk they will produce. Therefore, it is essential to know how the productivity of these resources varies throughout the year and in particular areas of the fields. "This depends on a number of factors, and precipitation is one of the main ones. From FAUBA we wanted to generate a tool to predict how much fodder occurs and how it varies throughout the year in fields of the south-handed Bonaerense, depending on the monthly rainfall".

Pascual Ciccioli, CREA advisor and student of the aforementioned specialization, said that after analyzing satellite information on a number of models, they found that it is possible to predict how much biomass will produce an alfalfa pasture several months in advance. "We generate simple models that have as their main variable the accumulated precipitation because we understood that the productivity of pastures begins to be determined with the rains several months before."

VEGETABLE MEMORY

"To know how much alfalfa pastures produce between March and June, we need to analyze the precipitation that accumulated in the previous three. For the productivity of July and August you have to look at the rainfall of the previous month. And from September to November we must observe the precipitation that fell in the previous 8 months," Ciccioli explained as part of his final work for the Specialization in Pastoral Systems Management.

Garbulsky, for his part, clarified that, on the one hand, the results of the study are closely related to the ability of pastures to respond to soil fertility and rainfall at a particular time; on the other hand, Martin stated that pastures possess what he called 'memory': "These forage species capture resources from the environment and reserve them in different organs, such as roots. Being more vigorous, they are able to make better use of the rains that fall in the following months."

SUSTAINABLE USE OF PASTURES.

"This research is especially relevant for livestock farmers," Ciccioli said, "because having information on future excesses or fodder deficits could anticipate livestock management decisions. 90% of the animal production of the Buenos Aires OS companies in which I work depend on fodder. Therefore, when the availability of this resource varies, the animal load must be adjusted to use it sustainably and avoid over-storeying."

Finally, Garbulsky recounted that from the Chair of Forrajiculture and IFEVA they will continue to investigate alfalfa pastures and also seek to advance on other natural and implanted species. "In addition, we intend to conduct similar studies on agricultural soils. We believe we can look into how these systems work and generate models similar to the one we just described."

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