New Technology Tools and Life Cycle Analysis (LCA) Applied to a Sustainable Livestock Production

Numerous factors, such as the rising population, urbanization and incomes, have contributed to the global increase in meat consumption in 2018 by 59% (43 kg per capita) compared to the 1990s values (1). In the same period, global fish consumption per capita increased from 13.5 kg per person/year to more than 20 kg. In 2018, meat production accounted for 330 million tons (MT) worldwide, of which 120.5 MT of poultry, 118.7 MT of pork, 70.8 MT of beef and other bovine species and 14.8 MT of sheep (1). According to the indications of the World Health Organization, FAO and the United Nations Organization, the amount of animal products in human diets should be equal to one-third of the daily protein requirement (7). The suggested daily human consumption of protein per capita is 0.66–1 g/kg body weight. Data published by the FAO (2) on global animal protein production indicate a current availability of 24 g/day per capita. The FAO (8) predicts an increase in animal production products of 1–3% per year for the next 30 years. Parallel to the demographic increase, the demand for food is growing, and FAO’s future projections outline a scenario by 2050, in which more than 500 MT of meat per year will be needed for human consumption. Approximately 800 billion t of cereals are currently used to produce animal feed; they will exceed 1.1 billion t by 2050. Most of this product will compete with human food, and the proportion of arable land used to produce new feeds will further increase (8). The possible conflict in the use of agricultural products, in particular cereals, for human or animal diets is one of the various challenges in the realization of sustainable production. Cattle use 60% of the total vegetal biomass that is produced, converting grass, pasture fodder and other inedible products for humans to edible human protein and improving the overall efficiency of the system. On the other hand, the limitation of arable lands due to urbanization, salinization and desertification requires great attention to the sustainability of production systems. Furthermore, the excessive exploitation of natural resources and global warming, with uncontrollable meteorological phenomena and drought, could negatively impact agricultural and aquatic production systems. Approximately 8% of the total water consumption is used to irrigate crops for animal feed (9, 10). Future sustainability of production systems will depend on the ability to create and introduce new production methods characterized by reduced energy demand and limited stress on terrestrial and aquatic ecosystems. Changes in eating habits and diet will also be indispensable. In the future, livestock production methods will probably change and show substantial differences between developed countries and developing countries, and consequently, between high-intensity production and small-scale agro-pastoral systems. The future demand for products of animal origin can only be met through a sustainable intensification of a low-carbon economy. On the other hand, the need to adapt to climate change and reduce GHG emissions will undoubtedly increase the costs of production and processing of raw materials, and therefore, of the finished product to the final consumer.

Different international institutions of the agricultural and food sector have estimated (for the short term, the year 2030 and for the long term, 2050) the ability of natural resource systems to absorb external climate shocks (6). Crop production will increase by 80% compared to current levels due to the adoption of different innovative cultivation techniques. Unfortunately, the growth rate of non-GM cereal cultivation has declined from 3.2% per year in the 1960s to 1% in recent years (7). Meat production will continue to expand globally. The livestock feed alternatives, which are discussed in depth in this document, must be evaluated in relation to their social acceptance (consumer approval), environmental sustainability, economic viability (costs and benefits) and technological feasibility (1).

Population dynamics are one of the most crucial drivers of future scenarios. According to the FAO outlook, the human population will increase globally from the present seven billion people to approximately 9–10 billion people in 2050, despite a decline in the birth rate (6). Three changes are expected in the future: the first change is the concentration of population growth, mainly in Asia and Africa. The second change is the advance in urbanization that will continue at an accelerated rate: approximately 70% of the world’s population will be urbanized in 2050 compared to the actual 49%. The third change is the change in the consumption patterns due to the income growth of the middle class in the least developed countries (LDCs). Globally, the average world citizen income is currently approximately $11,000 US/year, which is twice the income level in 1970. Per capita food consumption is expected to rise from 2,860 kcal in 2015 to 3,070 kcal in 2050 (4). Most developed countries have substantially completed the transition to livestock-based diets, while not all developing countries will likely shift, in the foreseeable future, to meat consumption levels, which are typical of Western diets (4). This shift requires a 70% increase in food production with a growing demand for red meat and dairy products to 80% (12). The expected global population and meat consumption increase are reported in Table 1.

The major meat increase is expected for poultry: a higher conversion rate will maintain a convenient price and will partially replace beef and pork meat. In the US, pork consumption has maintained a range between 20 kg/person and 25 kg/ person per year; beef consumption has fallen from 43 kg per year in 1975 to 24 kg per year in 2015; poultry meat has displayed a mirror-image response over the same time period, soaring from 21 kg/year to 48 kg/year, an increase of nearly 130% (13). The livestock production system in the European Union represents 40% of the total agricultural activities, with the employment of approximately four million people. Animal proteins comprise 50% of the total proteins of milk-based diets. Per capita consumption in the context of the European Union is 65.5 kg of meat and 236 litres of water per person/year. Over 40% of British people are reducing their meat intake, and 35% of Americans obtain most of their protein from plant sources. These changes are driven by consumer economics; for example, the relative cost of poultry meat during the last 6 months of 2020 was 4.2 US $/kg, whereas the relative cost of beef averaged 12.5 USD/kg. The increase in the world population and requirements of dietary changes for healthier nutrition are placing increasing pressure on changing the agricultural production system by adopting lower impact technologies.

A modern sustainable animal production farm should fulfil the following economic, environmental and social requirements: i) economic sustainability, i1) continuous process control in real time (improves efficiency of the use of production factors), i2 continuous real-time connection to external data sources (more convenient decisions based on market information), ii) environmental sustainability, ii1) better management of crops and harvest times (improves the use of nutrients in the field and quality of food for livestock, ii2) better coverage of the needs of animals (improvement in the production performance of animals with a lower impact per unit of product, ii3) better management of manure with reduced emissions, and iii) social sustainability (ethics and animal welfare), improved real-time monitoring of animals. The directions towards new sustainable strategies of carbon reduction are based on three actions: 1) Tebe, technology innovation and business models. Most existing technology innovation will take a long time to become economically feasible and will require substantial investments. Another problem is the threshold for GHGs, such as ammonia: 10,000 kg of ammonia per year (The amount that would be generated by 40,000 chickens, 2,000 pigs or 750 piglet-bearing sows). This high limit has allowed large factories, collecting common agricultural policy (CAP) subsidies, to simply game the system by legally dividing their operations up to the maximum allowed by the regulations before requiring authorizations. 2) Sece, socio-economic and consumer changes towards new food habits. Assess the relative resource intensity of different diets and food groups, and obtain a common definition of a ‘sustainable diet’, both across different cultures and in view of future resource constraints and the growing global demand for calorie- and protein-rich foods; 3) Ecsa, external change and socioeconomic adaptation. The primary drivers of consumer food choice are price, taste, and convenience production method. Several studies concluded that a 50% reduction in the current consumption of livestock products in the EU would make a significant contribution to climate change mitigation and align the current intake of animal protein and fats with WHO recommended dietary guidelines (12). This contribution could lead to 40% less reactive nitrogen emissions from agriculture and a 23% reduction in cropland area.

Agricultural activities use 80–90% of freshwater and cause 24% of global GHG emissions (8). A quarter of all food, measured by MJ content, is wasted from “farm to fork”, and 8% of the losses occur upstream of the value chain. Intensive agri-food chains are responsible for soil erosion and pollution due to fertilizers, pesticides, deforestation and irrigation. However, to meet the Sustainable Development Goals (SDGs) targets envisaged by the 2030 Agenda for Sustainable Development, additional efforts should be made to reduce GHG emissions, compel the demand for resource-intensive animal food crops and reduce food loss and waste along the food supply chain. Another target for 2030 is to reduce the “triple burden” of malnutrition: undernourishment, micronutrient deficiencies, and obesity.

Novel tech–biotech techniques pursue three goals: intelligent growth of agri-food production, inclusive growth of agri-food production, and sustainable growth of agri-food production (15, 16). Agriculture 4.0 is a combination of mechanical innovations and information and communication technologies (ICTs) for automatic machinery devices, robots, AI, 3D vision, irrigation, fertilization, precision farming, waste treatment, and advanced biopharming methods (17, 18, 19, 20, 21, 22, 23, 24, 25). All these technologies are targeted to improve farmers’ decision performance. The labour utilized in routine operations could be substituted with intelligent labour techniques. Precision animal husbandry represents a new opportunity for the animal production sector, improving the efficiency of the overall systems and enhancing both animal welfare (due to the possibility of monitoring and managing individual animals and not only the group) and production sustainability. The agricultural sector is considering with growing interest the adoption of ICT methods to improve the management process (with reduction of costs) and reduction of the environmental impact (mitigation of emissions). Digital and technical information is produced by sensors or other devices capable of measuring variables of interest. PLF techniques, as outlined in Table 2, are based on the integrated use of all the information available on the farm.

The most common PLF technologies are milking technologies: automated milking (robot); localization and attack of milking systems; colour sensors; electrical conductivity sensors; flow metres; near infrared reflectance (NIR) analyser; flow analysers (urea and beta-hydroxybutyrate (BHB), L-lactate dehydrogenase (LDH), progesterone); Body Condition Score (BCS) automated assessment systems; housing environment: microclimate (temperature and relative humidity); lighting (time and lux intensity); gas (N₂O, CH ₄, CO₂, and NH₃; powders (PM_2.5 and PM₁₀); animal feeding: field sensors (satellite) for harvesting and conservation (ensiling and hay making) decisions; conservation sensors (thermography); in line sensors (NIR); and animal welfare and health: location, movement, breathing, body temperature, ingestion and rumination (26, 27, 28, 29, 30, 31, 32, 33, 34). The application of sensors (Fig. 1) in the animal production sector is still in its infancy and has yet to be developed in the future.

Figure 1

An improvement in economic farm efficiency is the improved use of production resources. In the livestock sector, feed represents more than 30% of the production costs (35). Today, methods for a rapid near infrared reflectance (NIR) spectroscopy analysis of feeds are available. These methods can be used to quickly analyse the mixed feeds that are loaded into the mixer wagon for the preparation of cattle diets. These systems are very important, especially in agricultural areas where large quantities of wet silage, maize and grass silage are employed. These feeds are characterized by high and variable humidity. The importance of this method relies on the advantage of determining, in real time, the actual amount of dry matter and the risk of potential quality deterioration. Currently, the method for the detection of oestrus is widely utilized at the market level via algorithms that interpret variations in animal activity (36). Individual milk production (milking robot) allows correct dietary animal management (e.g., placing a cow in the most suitable production group or providing a food supplement using individualized automatic systems) (35). Less common are the macro-components of milk analysis (e.g., urea and beta-hydroxy-butyrate), which can provide additional information about the nutritional status of an animal, allowing us to correct the composition of the diet. PLF may allow high and efficient livestock production. The possibility of automatically recording and managing a large amount of data is related not only to milk production (even of each quarter of the animal udder) but also to analysing the flow (peak and average), total milking time and other variables (37). On the other hand, the correct and updated knowledge, day by day, of the individual production level allows an easier and faster adjustment of the diet to support the best production performance. For ruminates, the main objective is to optimize the protein synthesis of rumen microorganisms. This result is obtained by feeding animals the required amount of amino acids and fats, and by this way, reducing the polluting emissions (N and methane) (39). The improvement of livestock feed efficiency can also cause additional positive economic and social effects (40). Managing and analysing “big data” obtained on farms involves several activities. First, it is necessary to standardize the data obtained from multiple sources (internal and external to the farm) in real time, which can provide better opportunities for diagnosing risks and identifying alternatives (39). Large amounts of data (often from heterogeneous sources) require 1) large-scale collections, 2) storage, 3) pre-treatment, 4) modelling, and 5) analysis. Recently, artificial intelligence methods have been proposed for PLF data analysis. Several approaches are available, such as supervised learning (with data labelling and training datasets) and unsupervised learning methods that independently utilize data (41).

With the advent of omics technologies, it is possible to identify genetic variants (polymorphisms) at the DNA level and single genes (single nucleotide polymorphisms and SNPs) with significant effects on quantitative traits (QTLs). The identification of these polymorphisms in domestic species has enabled the genotype of a large number of animals and the use of several thousands of molecular markers via the use of SNP chip arrays (42). By means of these latest generation genomic tools, we are therefore able to identify new associations between the genetic variants (SNPs) and traits of interest. In particular, we can select animals with a “lower environmental impact” in terms of reduced feed consumption and methane emissions and increased metabolic efficiency (43). Among the various climatic variables, thermal stress has been reported as the most damaging factor for the economy of the livestock sector. There are numerous candidate genes associated with the adaptation of ruminants, monogastrics and poultry to heat stress. For example, genes that encode leptin (LEP), thyroid hormone receptor (THR), insulin growth factor-1 (IGF-1) and growth hormone receptors (EGF family) are associated with the effects of heat stress on several physiological animal processes (milk, meat and egg production; thermoregulation; and oestrus cycles) (4). Genetic selection is an important method for improving the feeding efficiency of beef and dairy cattle. Food efficiency is traditionally measured as the ratio between distributed food and weight gain. Genetic analysis of traits such as the food conversion ratio (FCR) or g CO₂/dry matter intake is difficult to measure, as more emphasis is placed on the trait with greater genetic variability (45). For this reason, the residual feed intake (RFI) trait is preferred to select animals with lower methane emissions (46). This trait is moderately heritable (0.26–0.43) and is obtained by a precise individual measure of food intake. The value of RFI can also be corrected for the back fat value (RFIfat). Animals selected for a low RFI value will show a better conversion rate and lower dry matter ingestion. The selection of these animals will allow the reduction of enteric CH₄ emissions (15–25%). de Haas et al. (47) predicted the RFI and CH ₄ emissions (based on DMI and diet composition) and suggested that 10 years of selection for the nutrition efficiency trait could reduce the CH ₄ emissions by 1–26%. Yan et al. (48) analysed the data of experiments using a calorimetric chamber and suggested the selection of cows with greater energy efficiency. In 2018, the Holstein Friesian Genetic Center (Cremona, Italy) started a new project, named “Latteco”, with the aim of measuring two traits for each individual bull: dry matter ingestion and greenhouse gas emissions (enteric methane and carbon dioxide). The experimental test starts after the quarantine period, and the bulls are divided into homogeneous groups according to age and weight. The young bulls were fed ad libitum during the whole test. The individual ingestion of dry matter (kg/d) and eating behaviours were evaluated using the Roughage Intake Control system (RIC, Hokofarm Group). A manager allows the calculation and monitoring of daily ingestions. Emissions of CO₂ and CH ₄ (g/d) were measured using the “GreenFeed” system (C - Lock Inc., Rapid City, SD, USA). The emissions are recorded through a feeder at each individual access of the animal to the feeding box. The initial results showed that more efficient animals emit less GHG. In addition, as the weight, chest circumference, height at the withers and ingestion capacity of individual bulls increase, methane and carbon dioxide emissions increase. One of the first objectives proposed is therefore to select more efficient animals to reduce emissions.

New biotechnology technologies and genome editing, such as the CRISPR/Cas9-12 method, can be employed to improve the productivity of animal and vegetable crops. Institutions and policy makers should promote regulatory environmental legislation to encourage the diffusion of these new technologies and investment through the entire livestock production chain.