Big Data in Animal Production Management

Big Data has big potential to transform animal production methods and farm management. Since 2013> the number of articles has been growing and they concern mainly dairy cattle (Lokhorst, Mol and Kamphuis, 2019). The main areas where Big Data can be used are feeding, e.g., forecasting daily intake of cows (Steeneveld and Hogeveen, 2015) and predicting cow’s health and fertility (Harty and Healy, 2016) as well as individual and whole herd performance (Yan, Chen, Akcan, Lim and Yang, 2015). White, Amrine and Larson (2018) presented interesting possibilities for the use of Big Data in cattle breeding (2018), who described the possibility of detecting oestrus and bovine diseases based on movement patterns (distance, pace, and movement method). Tracks the position of each animal at short intervals and compares the results with patterns helping to determine whether or not the movements follow the correct pattern (White, Amrine and Larson, 2018; Fernandez-Carrion et al., 2017). Quality, visualization, and interpretation of data (Hermans et al., 2017, 2018) were also dealing with reproduction of cows. A farmer quickly received information about heat in one of the females in the herd increases the chances of successful insemination. Green et al. (2016) indicate the possibility of using Big Data to control mastitis in cows. Tire authors point out the possibility of forecasting which cows are at risk of disease, which they think is their main advantage. Preventing disease or treating it early reduces the amount of medication used and, importantly, improves animal welfare.

Big Data can also be used to assess the performance of fattening cattle as described by Hewitt, Green and Hudson (2018) to monitor the behavior and growth of pigs (Pineiro et al., 2019) or to achieve rapid genetic progress through genome analysis (Morota, Ventura, Silva, Koyama, and Fernando 2018).

These are just a few examples of the use of Big Data in animal production management, and soon there will be more and more of them because the equipment necessary for precision production is cheaper, and IT technologies and computers are becoming more user-friendly. Table 7-1 shows the main aspects of using Big Data in agriculture.

Advantages of Big Data in Farm Management

A review of the literature indicates many benefits that can be gained by using Big Data in farm management. Table 7-2 presents the most important of them. Tire use of Big Data helps farmers better control the production process (Subramanian, Chen and Redd, 2018). They can identify existing problems faster and receive a warning before they occur (Sonka, 2016).

Using Big Data significantly increases production effects (Australian Farm Institute, 2018; Chandra Sekhar, Udaykumar, Kumar and Sekhar, 2018) and

Table 7.1 Effects of Big Data Use in Crop and Animal Production Management

(Continued)

Management in the Era of Big Data

Table 7.1 (Continued) Effects of Big Data Use in Crop and Animal Production Management

Source: Own elaboration.

Big Data in Modern Farm Management ■ 103

Table 7.2 Advantages of Using Big Data in Farm Management

(Continued)

Table 7.2 (Continued) Advantages of Using Big Data in Farm Management

Source: Own elaboration based on the literature.

economic effects such as improving profitability and efficiency, reducing operating costs (Delgado, Short, Roberts and Vandenberg, 2019).

Hie possibilities offered by Big Data are huge. In addition to forecasting crop yields and animal performance (Lima et al., 2018), they help you decide what and where to grow, when to sow and plant, how to feed individual animals, when and what to do with their breeding, and even when, what, and where to sell.

Tire use of Big Data improves risk management (Allepuz, Martin-Valls, Casal and Mateu, 2018). Farmers can, thanks to Big Data, assess the likelihood of precipitation, frost, crop failure, pest infestation, or pathogens (Bauriegel, Giebel, Geyer, Schmidt and Herppich, 2011; Bendre, Thool and Thool, 2016; Astill, Fraser, Dara and Sharif, 2018; Fernandez, 2018). Tire use of Big Data increases food security (Ahearn, Armbrusterb and Young, 2016).

Wireless data transfer technology permits farmers to access to individual data from anywhere (Wolfert, Verdouw and Bogaard, 2017).

Disadvantages of Big Data in Farm Management

Tire literature also mentions many inconveniences and problems associated with the use of Big Data in agriculture (Table 7-3)-

The huge amount of data, on the one hand, is an advantage, and on the other hand, an inconvenience. Large data sets are necessary to properly predict the

Table 7.3 Disadvantages of Using Big Data in Farm Management

Source: Own elaboration based on the literature.

consequences of events and give correct recommendations, unfortunately, some information is difficult to obtain, especially when it concerns a long production cycle. In the case of cereal production, e.g., for data on sowing or plant growth, you have to wait until the next growing season, i.e. you can only get it once a year. For this reason, the preparation time needed to forecast the data set is long. According to Mark, Griffin and Whitacre (2016), sending large amounts of data requires appropriate infrastructure, the construction of which is extremely expensive and only a few operators can afford such an investment. The asymmetry of information that Ribarics (2016) mentions raises concerns. Farmers, in order to have access to modern technologies, reveal a lot of private information without full knowledge by whom and how it will be used. Meanwhile, software providers can aggregate large amounts of private data and develop new products by combining their privileged position with unique access to highly detailed, private data (Ribarics, 2016). Therefore, issues regarding the method and place of data storage become important. It seems that ethical issues, data ownership, and security will be a problem that needs to be solved for a long time (Carbonell, 2016; Sykuta, 2016; Tantalaki, Souravlas and Roumeliotis, 2019).

Another disadvantage is still the low knowledge of advanced technologies among farmers around the world (Sharma and Kaushik, 2019). And yet using Big Data requires considerable skills (Tantalaki, Souravlas and Roumeliotis, 2019). It is comforting that the research of Sharma and Kaushik (2019) shows that young farmers belonging to the new generation are positive about the use of advanced technologies in farm management and more often use it in practice.

Farmers should also consider the need for investment. Purchase of equipment for data collection, license fees can be a significant barrier to the use of advanced technologies in agriculture, especially on small farms (Long, Blok and Coninx, 2016; Sharma and Kaushik, 2019).

Wolfert, Verdouw and Bogaard (2017) underline that the use of Big Data may threaten the autonomy of farmers and change the way they are operating (Drucker, 2014). What is more, farmers’ expertise and knowledge, is about to be replaced by algorithms (Wolfert, Verdouw and Bogaard, 2017). Following the suggestion of Henry (2015), one day farmers themselves will be replaced by an artificial intelligence and autonomous machines.