Future Implications for Animal Production: A ...

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growing world population (estimated by the United Nations to be 9.55 billion by .... security for all” must be achieved in a sustainable way to be achieved long term. .... Adapted from Council for Agricultural Science and Technology (1999). ..... resolution (e.g., high-definition satellite imagery) as well as fast and reliable data ...
Future Implications for Animal Production: A Perspective on Sustainable Livestock Intensification1 Luis Orlindo Tedeschi1, James Pierre Muir2, David Greg Riley1, Danny Gene Fox3

Department of Animal Science, Texas A&M University, College Station, TX, USA Texas A&M AgriLife Research, Stephenville, TX, USA 3 Department of Animal Science, Cornell University, Ithaca, NY, USA 1 2

Abstract. Food supply has improved considerably since the 18th Century industrial revolution, but inadequate attention has been given to protecting the environment in the process. Feeding a growing world population (estimated by the United Nations to be 9.55 billion by 2050) while maintaining or reducing the impact on the environment requires immediate and effective solutions. Sustainability is difficult to define because it embodies multifaceted concepts and the combination of variables that make a production system sustainable can be unique to each production situation. The concepts of sustainability, resilience, and resistance are dependent upon context and time horizon. Sustainable is the characteristic of a system that continues to co-exist with other systems at a different output level after a period of perturbation(s). Resilience is the ability of a system to fully (or partially) recover and re-establish a steady output (i.e., dynamic equilibrium) after it has been perturbed either by a natural (i.e., endogenous) or an artificial (i.e., exogenous) stressor. Sustainable intensification (SI) produces greater output(s) through the more efficient use of resources within a period of time while reducing (or at least not increasing) negative impact on the environment; hence, SI provides opportunities for increasing animal and crop production per unit of area via sustainable alternatives that fully consider the three pillars of sustainability: planet, people, and profit. The question that remains unanswered is whether global agriculture in 35 years will feed 10 billion of people, produce biofuel and clean electricity, supply fiber, and at the same time reduce its environmental footprint to avert or even reverse climate change. Key words. Cattle, Efficiency, Environment, Production, Sustainability, Systems

The Need for More Productive and Sustainable Food Production Systems

Discussions about sustainability come at a time when the awareness of global warming and food shortage risks has never been greater; these discussions were brought about by recent extreme weather events and hunger around the world. Frustration with the lack of achievements in the prevention of global warming was augmented by the perception that the 2009 international climate negotiations in Copenhagen failed miserably; it was a “festival of conspiracy and betrayals” (Goodell, 2010). The sustainability issue could go in the same direction and become entangled in the same web of politics as the Copenhagen negotiations if science is not taken seriously by conquering environmental pollution and hunger simultaneously. China blames the United States of America and Europe for global warming, saying that it has been largely caused by 200 years of fossil-fuel burning, after the industrial revolution in the 1700s (Goodell, 2010). Clearly, this is not a one-player game; it requires multilateral decisions and measurement on a global scale.

In an unprecedented move by the Catholic Church, the concern of global warming threatening life on Earth was the central point of the encyclical “Laudato Sí” (i.e., praise be to you) by the Pope Francis: “a very solid scientific consensus indicates that we are presently witnessing Paper presented at the 52th Annual Meeting of the Brazilian Society of Animal Science, Belo Horizonte, MG on July 19-23, 2015 (http://www.sbz2015.com.br/). Citation: Tedeschi, L. O., J. P. Muir, D. G. Riley, and D. G. Fox. 2015. Future implications for animal production: A perspective on sustainable livestock intensification. Pages 1-23 (CD format) in Proceedings of the 52th Annual Meeting of the Brazilian Society of Animal Science, Belo Horizonte, Minas Gerais, Brazil. Sociedade Brasileira de Zootecnia (SBZ) 1

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a disturbing warming of the climatic system” and emphasized that “nobody is suggesting a return to the Stone Age, but we do need to slow down and look at reality in a different way, to appropriate the positive and sustainable progress which has been made, but also to recover the values and the great goals swept away by our unrestrained delusions of grandeur” (Francis, 2015). Global warming has become a political and religious matter, but what does science tell us?

Environmental protection is paramount for providing minimum livelihood standards for humans and ensuring the survival of our species (and many others) in centuries to come. This spans the preservation of biodiversity and responsible stewardship of soil and water resources as we seek to feed and clothe ourselves utilizing these natural resources. Widespread alterations of the environment by humankind in support of our own existence have accelerated since the beginning of the industrial revolution in the 18th Century (Sabine et al., 2004). Food production has been based on maximizing productivity and profitability with inadequate concern for protection of soil, water, and water quality in the process. As a result, these alterations have disrupted natural cycles by adding sequestrated carbon back into the atmosphere at a much faster pace than it can be immobilized.

Terrestrial ecosystems react to atmospheric CO2 concentration such that their short- and long-term feedbacks likely play a role in climate change. Their precise contribution is largely uncertain because they impact diverse ecozones and the time of impact cannot be predicted. Although uncertainty remains, Schimel et al. (2015) reported a significant uptake by tropical forests and suggested that up to 60% of their present-day terrestrial sink is caused by increasing atmospheric CO2. On a global scale, the FAO (2006) reported that the livestock sector was responsible for 18% of total greenhouse gas (GHG), when expressed as CO2 equivalent, and 9% of anthropogenic CO2 emissions. These estimates, however, have been challenged and thought by some to be significantly less (Hristov et al., 2013; Place, 2015). More than 215 years ago, Thomas Robert Malthus prophesied that human population growth would outrun our ability to produce food (Wrigley, 1988). Today this remains a real possibility given the need to feed a staggering growth in world population, projected to be 9.55 billion by 2050 (United Nations, 2013), while maintaining or reducing the impact on the environment. This task poses a progressively more challenging and constant pressure on crop, soil, and animal scientists around the world to come up with immediate and effective solutions. Our modern food production involves intricate and complex systems with many feedback signals (Tedeschi et al., 2011b). For instance, an increase in food production affects the environment by changing the climate directly through emissions that contribute to global warming and from excess nutrients that reduce water quality or by reducing nonrenewable resources (e.g., fossil fuel). In turn, the changes in environment offset improved productivity through harsher conditions such as degradation of soil quality, increase in warming, resurgence of new diseases, depletion of biodiversity, and loss of adapted animals and crops, among many other outcomes. Animal scientists must develop strategies that forecast the rate and magnitude of global changes as well as their possible influences on the food production chain. They cannot, however, afford to stop there: they must then develop tactics to adapt to and mitigate the causes of global climate change due to food animal production.

The Feed the Future2 concept likely had its inception in the 1960s, but not until recently has it been given much attention. The success of the Feed the Future initiative depends on innovative solutions to foster greater agriculture production while maintaining profitability and minimizing its environmental impact. The current stagnant growth rate of food animal agriculture will likely fail to meet the expected growth in global demand for animal protein, especially if it is 2

http://www.feedthefuture.gov

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to be profitable when produced in an environmentally and socially responsible way. Innovations and investments are needed in animal science research and development in the 21st century as dramatic increases in global demand for food protein (e.g., meat, fish, eggs, and dairy products) are forecasted by 2050 due to increase in world population (National Research Council; NRC, 2015). Additionally, the possible use of biomass for biofuel production through thermochemical conversion technologies (Verma et al., 2012) creates a new, imminent threat to livestock systems because they often compete for the same land area and resources. The future of this competition is uncertain as it depends on regulations, subsidies, and petroleum prices, and these policies vary across countries (Hertel, 2011).

Debates on global warming and Feed the Future concepts have initiated unprecedented discussions about alternative production systems needed to meet growing human demand for food. Unprecedented extreme high temperatures (> 40oC) and the number of days with temperature humidity index above comfort threshold (i.e., 68) have noticeably increased in recent years in countries located within the temperate zone, thereby negatively affecting agriculture and livestock productions (Silanikove and Koluman, 2015). Regardless of the disagreements about the veracity and acceptability of global warming data and the methods used to collect it, the weight of the evidence favors acceptance rather than rejection and we cannot afford to take the risk of being wrong. Thus, to avoid mass starvation in the future, we must assume there will be continued exponential growth of the human population (United Nations, 2013) and current methods to produce food must improve to meet this challenge, using environmentally-friendly technology. Although many of these technologies exist, they may only partially address the Feed the Future promise without reducing environmental impact, even if fully implemented. For instance, animal agriculture expansion into new lands would likely not be a feasible solution because of resulting increased competition for land from other human activities (e.g., row cropping, urbanization or water capture; Pretty et al., 2011) among many other issues. The NRC (2012) indicated that “food security for all” must be achieved in a sustainable way to be achieved long term. Sustainability Concepts and Definitions

The increase in concerns about sustainable food production systems has resulted in rhetorical discussions and diverse opinions on what sustainability is. Opinionated papers have presented contrasting schools of thought (e.g., business-as-usual optimists, environmental pessimists, new modernists to name a few; Pretty, 1997). Open discussions based on sound science that lead to clearly defining and describing sustainable food production systems under different plant and animal production conditions are needed. As humans have imposed greater demands upon natural systems (e.g., agriculture), some have raised alarming concerns about the sustainability of these systems because of the finite nature of soil and water resources (Arrow et al., 1995; Meadows and Meadows, 2007; Meadows et al., 1972) and the heavy dependency of agriculture on non-renewable resources such as fossil fuel (NRC, 2010). In fact, the 2007-2008 commodity crisis highlighted food production vulnerability to weather-related events, financial markets, and poor governmental interventions in protecting food exportation to avert chaos in the domestic food supply (Hertel, 2011).

A recent concept, Sustainable intensification (SI), accounts for the need to increase crop and food animal production per unit of area while taking into consideration sustainable production alternatives that fully address the three pillars of sustainability (planet, people, and profit; Makkar (2013)). Thus, SI requires that food production systems be profitable, socio-culturally acceptable, beneficial to the people, as well as environmentally friendly to natural resources (Figure 1). Sustainable intensification supports climate change mitigation and the Feed the Future initiative. Improving food yields in areas that are already highly productive is not necessarily the solution because it could end up increasing the environmental burden. For about 80% of the 3

chronically hungry in Africa, most of whom are smallholder farmers, an improvement in yield per area would increase their access to food and generate more income (The Montpellier Panel, 2013). In that context, SI is used to increase productivity in areas that were previously underutilized. A 2010 report analyzing 40 SI projects from 20 countries during the 1990s to 2000s indicated that about 10 million farmers had benefitted from SI projects with improvements on approximately 12.75 million Hectares (Pretty et al., 2011). Animal products and crop yields per hectare were increased by combining the use of new and improved crop varieties. The main challenge with the adoption of SI is the spread of effective processes and lessons to many more people, generally smallholder farmers and pastoralists across the world (Pretty et al., 2011).

Figure 1. The three pillars of sustainability. Based on “sustainable development” from Wikimedia.org under Creative Commons licensing, and further adapted from United Nations (1987), International Union for Conservation of Nature (IUCN) (2005), Makkar (2013), and Makkar and Ankers (2014). Unfortunately, the deployment of SI technologies does not occur without controversy, which is often based on incomplete information or lack of sound science. An example of SI technology conflicting with social issues is the misperception of feeding genetically modified (GM) plants or their byproducts to animals. It is generally accepted by the scientific community that GM plants (e.g., corn) used as feed material for food-producing farm animals are safe because they do not alter animal metabolism or the organoleptic and bromatological characteristics of meat, milk, and eggs (Swiatkiewicz et al., 2014). The negative perception of GM food by some, however, hinders its scope and application where it is needed the most, and limits its contribution to SI. The GM organism can be a powerful tool in addressing climate change issues as discussed by Zilberman (2015): the first generation GM plants are insect and disease resistant or herbicide tolerant; the second generation GM plants have enhanced organoleptic and bromatological characteristics, photosynthetic efficiency, and abiotic stress tolerance (e.g., drought); and the third generation GM plants produce specific chemicals and pharmaceuticals. 4

Another social conflict with SI stemming from misconception is that some believe livestock always competes with human food supplies because grain (e.g., corn) or cultivated pasture used to feed livestock could be used directly as human food and, thus, they assume livestock activity is an inefficient or wasteful use of resources (CAST, 2013). In actual fact, as shown in Table 1, for some species such as beef and dairy cattle, the protein conversion efficiency on a human-edible basis is often greater than 1:1, indicating that the amount of human-edible animal protein product is greater than the human-edible feed consumed by the animal (CAST, 1999), confirming the undeniable benefit of animals as an efficient source of high-quality food for humans. Thus, the dilemma in hand is how to meet the challenge of providing high-quality food to a growing human population while reducing the environmental footprint in an effective, economical, and timely fashion. Table 1. Comparative gross efficiencies of conversion of dietary energy and protein to product and returns on human-edible inputs in products for swine, poultry, milk, and beef in different countries 1 Country

Species

Gross efficiency Human-edible efficiency Energy Protein Energy Protein Swine 0.15 0.07 0.24 0.11 Argentina Poultry 0.18 0.30 0.28 0.69 Milk 0.19 0.16 4.61 1.64 Beef 0.02 0.02 3.19 6.12 Swine 0.13 0.08 0.25 0.21 Mexico Poultry 0.2 0.33 0.34 0.83 Milk 0.12 0.11 0.79 1.06 Beef 0.06 0.02 16.36 4.39 0.20 0.16 0.35 0.51 South Korea Swine Poultry 0.21 0.34 0.30 1.04 Milk 0.26 0.19 3.74 14.3 Beef 0.06 0.06 3.34 6.57 0.21 0.19 0.31 0.29 United States Swine Poultry 0.19 0.31 0.28 0.62 Milk 0.25 0.21 1.07 2.08 Beef 0.07 0.08 0.65 1.19 1 Gross efficiency was calculated as outputs of human-edible energy and protein divided by total energy and protein consumed by the animals. Human-edible efficiency was computed as outputs of human-edible energy and protein divided by human-edible energy and protein consumed by the animals. Adapted from Council for Agricultural Science and Technology (1999).

Since the introduction of the sustainable development concept, which was defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” by the World Commission on Environment and Development (United Nations, 1987 -- Brundtland Commission's Report), sustainability has been defined in more than 100 different ways (Pretty, 1997) probably because one definition does not fit all possible scenarios. Sustainability fits the criteria for a “wicked problem” (Peterson, 2011): (1.) the ideal definition lacks specificity and it can be reduced to a slogan, (2.) one can never know if sustainability has been achieved because the outcome is usually better or worse rather than true 5

Relative Output Level or Resource Outflow, %

or false, (3.) stakeholders have different points of references for the problem, and (4.) system components and cause/effect relationships are uncertain or changing. Thus, there is not one best definition of sustainability. Pretty (1997) believes that sustainable agriculture is more a learning process than prescribed specific and rigorous application of technologies, practices, or policies. Regardless of the definition(s) adopted for sustainability, one needs to clarify what is being sustained, boundaries of the system, intensity of disturbances, and time scale. Figure 2 graphically illustrates some concepts to establish the basics for effective communication and understanding among parties. It shows different response behaviors when a system (or organism) is challenged with a temporary (or permanent) perturbation or distress with an onset at time 10. Except for the unviable scenario portrayed in Figure 2, the other scenarios in general represent different degrees of sustainable behaviors from an agro-ecological sciences perspective, as defined below.

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Figure 2. Schematic illustration of different levels of hypothetical sustainable responses by a system after a period of perturbation(s) or stress(es). Definitions for sustainability abound in the agro-ecological sciences. The Oxford English Dictionary defines sustainable as "capable of being maintained or continued at a certain rate or level," but it does not make any attempt to include in this context the system reaction to a perturbation. A broader concept of sustainability needs to take into account the context (e.g., variables involved and their relationships) and the time horizon. Sustainable is the characteristic of a system (or an organism) to continue to co-exist with other systems (or organisms) at a different output level after a period of perturbations. Furthermore, a sustainable system has to maintain itself with minimal or no impact outside its boundaries. Therefore, one has to establish the chronological and/or spatial boundaries of the system. Sustainability is the ability of a system to be sustainable. As illustrated in Figure 2, after a period of perturbations (or distress), the system (or organism) reduces its output to a lower level and stabilizes at this new level. Sustainability could be difficult to achieve when exogenous stresses are constantly imposed onto a system, possibly leading to an eventual collapse.

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Resilience is defined by the Oxford English Dictionary as "the quality or fact of being able to recover quickly or easily from, or resist being affected by, a misfortune, shock, illness, etc.; robustness; adaptability" or "the action or an act of rebounding or springing back; rebound". Resilience, thus, is the ability of a system (or organism) to fully (or partially) recover and reestablish a steady output (i.e., dynamic equilibrium) after it has been perturbed either by a natural (i.e., endogenous to the system) or an artificial (i.e., exogenous to the system) stressor(s). In other words, resilience is achieved through mutual reinforcing feedback loops that permit the system to persist overtime despite temporary disruptions, by either intrinsic (e.g., endogenous) or surrounding environmental factors (e.g., exogenous) to the system, by regenerating itself to its original state. Resilience can have a positive or negative impact on the system (or organism) output. A system is resilient when it recovers to the same level at which it functioned prior to the perturbation. Under initially unfavorable grazing conditions, a growing animal’s subsequent compensatory growth is an example of resilience. Animals that have undergone an undernourished period usually lose body weight (BW) (in the form of body reserves) in support of important bodily functions to ensure survivability, and then, after the nutritional restriction has ceased, they partially or fully restore their BW to the normal growth curve by compensating the BW loss via increased growth rate (Ryan, 1990). Resiliency is the ability of a system (or organism) to be resilient or to absorb stress without suffering modifications. As illustrated in Figure 2, after a period of perturbations (or distress), the system (or organism) reduces its output, but immediately initiates the recovery to its original state. A sustainable system is resilient within a given context but not all resilient systems are sustainable because in the process of rebounding, the system may overshoot and collapse, find another point of temporary stability, or collapse entirely. Therefore, sustainability is not achieved if the system cannot return to its original stable level. The point of no return occurs when a system (or organism) can no longer be resilient nor sustainable, and the fate of the system (or organism) is a general failure as illustrated by the unviable scenario in Figure 2. For example, the mechanism of compensatory growth has been well documented in grazing animals. It depends on the degree and duration of the nutritional restriction and the quality of feed provided after the cessation of the undernourishment period (Ryan, 1990). Some animals can partially or fully restore their embedded, genetic potential for growth while others may stay at a lower growth or yield level (i.e., stunted animals) and never reach their normal growth (Hogg, 1991) even if previous ADG is reestablished.

Stable is defined by the Oxford English Dictionary as “not likely to change or fail; firmly established” while resistant is “offering resistance to something or someone.” A stable (or resistant) system (or organism) is able to produce and reproduce under periodic or continuous stress conditions for a given time compatible with its growth cycle (e.g., fertilization, development, yield, offspring, or seeds). The system (or organism) is insensible to perturbations in the surrounding environment and it maintains an output in dynamic equilibrium over time and across several generations. In short, it has the ability to resist disorder. As with the previous discussion, we assumed that the perturbation or distress is within reasonable physiological limits, most notably non-fatal. As illustrated in Figure 2, after a period of perturbations (or distress), the system (or organism) does not change its output level. In biological sciences, this outcome is rarely observed, as most living organisms will respond to an exogenous stressor in either a negative or positive way. Tolerance or survivability, i.e., the ability of an organism to stay alive during stress conditions with minimum or absent growth and proliferation, waiting for the right time to express full genetic potential, is more related to resilience than resistance. For instance, Volaire et al. (2014) defined drought survival as the ability of plants to cease growth during moisture shortages but to regrow (continue their life cycle) when drought ceases. Plants, therefore, decrease output level for a period of time and then recover growth rates (but less likely total yield vis-à-vis their genetic potential) to pre-stress levels. 7

Others have defined natural systems as resilient when they tend to maintain their integrity under disturbances and defined stability as the ability of a system to return to an equilibrium state after a temporary disturbance (Holling, 1973). These definitions are somewhat opposite to our definitions as discussed above and shown in Figure 2. Ludwig et al. (1997) used Holling’s (1973) definitions of resilience and stability and compared them from a mathematical perspective. In the long run, resilient organisms or systems tend to resist exogenous perturbations by always reverting to the original level, thereby behaving more like a stable/resistant system (Figure 2). Our definitions of resilient, sustainable, and resistant are more in line with those adopted by Fiksel (2003). Our observations support the view that true stability is nearly impossible in natural ecosystems (or organisms) because disturbance (stress) is almost always an integral part of those. An unviable system (or organism) is neither sustainable nor resilient to perturbations and complete failure is the result after a perturbation unless an exogenous interference rescues the system (or organism) in time. Row cropping annual grains with tilling, irrigation, and other heavy inputs is an example of such system dependence on exogenous interference. Even at that time, however, the system (or organism) may or may not find a new level of stability.

We conclude SI is the best overall concept that describes the approach that will be needed to increase food production while accounting for environmental effects. SI is about producing more output(s) through the more efficient use of the resources (i.e., inputs) within a period of time while reducing the negative interference with the environment. The origin of SI dates back to 1990s when the term was developed for African production systems in which low yields and high environmental degradation were predominant, prompting an immediate pejorative use of SI to be synonymous with smallholder-oriented or organic production technology (Garnett and Godfray, 2012). SI has often been confused with other terms and concepts such as conventional intensification, ecological intensification, agroecology, organic agriculture, climate smart agriculture, and eco-efficiency (Garnett and Godfray, 2012; Kuyper and Struik, 2014) that have triggered negative perceptions. Tittonell (2014) concluded that SI is more loosely defined than ecological intensification. Struik et al. (2014) indicated that SI is by definition an oxymoron problem as a win-win situation is rare, and ambiguities exist in both terms of SI (Uphoff, 2014). We agree with The Royal Society (2009) definition of SI, disconnected from any particular agricultural production system, as a form of production wherein “yields are increased without adverse environmental impact and without the cultivation of more land.” Campbell et al. (2014) concluded that SI and climate-smart agriculture are complementary processes but different multi-managerial approaches have to be adopted at different levels to achieve successful implementation. These include diversified farming systems, local adaptation planning, building responsive governance systems, enhancing leadership skills, building asset diversity, reducing consumption and waste, building social safety nets, facilitating trade, and enhancing diets. It is, however, important to stress that SI entails the increase of food production from existing farmland without additional environmental impact. The responsive behavior shown in Figure 2 illustrates SI: output level increases after the onset of perturbations in the system until it reaches a dynamic equilibrium at a greater level than before the perturbations. Of course, in this case, perturbations had a positive impact on the system (or organism), assuming that the level of input(s) were constant. Sustainable production is often misunderstood as a goal for farmers and other land managers. Sustainability represents the state of a complex dynamic system that is always evolving and transforming itself; thus, it cannot entail a production goal. It is an intrinsic characteristic of the system that needs to be maintained. The system has to be shaped and managed until it is sustainable. For instance, sustainable profitability and SI are distinct means to be productive in distinctive ways: the former contributes to sustainability by decreasing cost-to-benefit ratios while the latter one does so by increasing the output per unit of input. A systems approach incorporates 8

multiple means to attain system sustainability. For example, input resource use and energy intensity reduction, waste conversion into valuable products, identification of boundaries, establishing requirements, and employing feasible technologies are among key strategies for system sustainability (Fiksel, 2003). In addition, from a systems perspective, we must identify key processes within the system of interest and define their dimensions (Uphoff, 2014). Furthermore, while SI may improve food supply in the foreseeable future, it does not imply food security for all (Garnett et al., 2013) because food availability and supply, distribution and allocation, storage, and utilization may prevent food from reaching those that need it the most (NRC, 2012). In fact, nutrient security might be a better term for addressing hunger. Fath (2014) believed that we can hardly address sustainability of ecological systems without discussing ideas about systems thinking put forward by three personalities: the American ecologist Bernard Patten, German sociologist Niklas Luhmann, and Austrian-born architect Christopher Alexander. Patten’s (1978) contributions to the importance of defining environment in ecological systems and Luhmann’s (1996) for social systems indicate that boundaries are necessary to demarcate the limits of the system and the environment. Boundaries are also key to understanding how systems interact with environment in exchanging energy, matter, and information that keeps the system active and sustainable. The parallel insights of these individuals remarkably emphasizes the ideas discussed by Forrester (1961, 1971, 1973), Sterman (2000), Maani and Cavana (2007), Morecroft , and Warren (2008) for business dynamics, and by Tedeschi et al. (2011b) for animal agriculture on using a systems approach when modeling complex relationships, including education (Lander, 2015).

The concepts and definitions discussed above lay the foundation for studying SI systems and proposed ways to describe, measure, and evaluate their achievements. Methods to effectively promote SI as well as the deployment and stability of SI systems need to be addressed. The NRC’s (2010) “Toward Sustainable Agricultural Systems in the 21st Century” assessed the scientific evidence of strengths and weaknesses inherent in deploying sustainable agriculture under various production, marketing, and policy approaches. It also examined its unintended consequences. The NRC (2010) committee identified incremental and transformative changes. The latter are critical and include “the development of new farming systems that represent a dramatic departure from the dominant systems of present-day American agriculture and capitalize on synergies and efficiencies associated with complex natural systems and broader social and economic forces using integrative approaches to research and extension at both the farm and landscape levels.” An important practice for livestock suggested by the committee was the genetic improvement of livestock to increase feed efficiency as well as animal health and welfare. Sustainable Livestock Intensification (SLI)

Livestock production impacts the environment in many ways. Janzen (2011) indicated that about 20 to 30% of land utilized by humans is used for grazing while another 7 to 10% is dedicated to producing animal feed and forage. A large portion of grazed areas is not suitable for alternative production of human food. In the United States, about 25.9% of the land is classified as grassland, pasture, or rangeland (CAST, 2012). Oltjen and Beckett (1996) indicated that humanly edible returns for digestible energy ranged from 37 to 59% and returns to digestible protein ranged from 52 to 104%, depending on the time spent in the feedlot and the amount of corn fed. Similarly, Wilkinson (2011) indicated that feed conversion ratio for edible protein into edible animal protein was greater than 1. Although certain ruminant production systems have a human-edible protein efficiency greater than 1 (Table 1) (CAST, 1999), about 16,000 L of water is required for each kilogram of beef produced, though there is large variation in that estimate (Janzen, 2011). The main source of agricultural N loss to air and considerable N and P emissions to aquifers and surface water originates from livestock (Janzen, 2011); thus, sustainably managing livestock N and P 9

excretion (Eghball, 2002; Klausner et al., 1998), especially in concentrated animal feeding operations, is necessary to contain excessive environmental pollution. Livestock provide several benefits, including the conversion of human-inedible feed into high-quality food (Gill, 2013), preservation of ecosystems (i.e., grasslands), recycling of organic matter and nutrients, and many social aspects associated with livestock (Cheeke, 1999; Janzen, 2011). We therefore need to make livestock production more sustainable using novel technology and rational management strategies while simultaneously providing high-quality food, preserving biodiversity, and adopting animal welfare practices. Sustainable livestock intensification is needed in animal production through the use of technologies that can be used to increase food from animals. Dumont et al. (2013) proposed five ecological principles for the redesign of animal production systems (management to improve animal health, decreased inputs, optimize metabolic functioning of farming systems, enhance diversity, and preserve biological diversity) and Dumont et al. (2014) discussed 40 issues that were collapsed into four major themes: animal adaptive capacities, feed resources and forage systems, design and evaluation of new production system, and rules for scaling-up agroecological animal production systems. We believe that focus areas include management of grassland/row crop fields, precision feeding, adaptation of livestock to climate changes, water usage and recycling, and the preservation of biodiversity through genetic manipulation and preservation. Smallholder animal farmers and SLI

Strategies for intensifying ruminant production around the world cannot ignore smallholder or subsistence farms and herders. For decades, livestock in the tropics have held the greatest promise but also the strongest challenges (Preston, 1990), a situation that has only intensified with the realization that the vast majority of these smallholders exist in developing countries (Kruska et al., 2003) and are most vulnerable to climate change (Musemwa et al., 2012). Animal health (Suriyasathaporn, 2011; Young et al., 2014), reproduction (Bahmani et al., 2011), nutrition (Atuhaire et al., 2014; Stür et al., 2002), household food security (Nampanya et al., 2014), and profitability (Widiati et al., 2012), especially as it relates to market access (Zvinorova et al., 2013), are hurdles to overcome if SI is to reach the majority of the world’s ruminant producers. Ineffective extension, inadequate education, inaccessible credit and gender inequality for women participants keep progress to a minimum (Esilaba et al., 2005; Fon Tebug et al., 2012; Mekonnen et al., 2010; Zvinorova et al., 2013). The picture is not all negative, however. Understanding the whole farming system from a human as well as a biological perspective can make a difference, as does the involvement of smallholders in identifying bottlenecks and solutions (Bayemi et al., 2009; Esilaba et al., 2005; Le Gal et al., 2013; Mekonnen et al., 2010; Stür et al., 2002). The role of decision support systems to assist SLI

The lack of awareness and limited knowledge of mathematical models are the main factors that foster a negative perception of modeling and simulation, thereby hindering their development and broader application. Mathematical models have a crucial role in shedding light on unforeseen variable relationships and quantifying expected outcomes resulting from alternative decisions in the production scenarios given the context for which the model was intended to be used. Our reasoning is endorsed by the recommendations of Garnett and Godfray (2012), who recommended that a more system-oriented approach to decision making is needed to develop substantial programs of future activity related to SI.

Recently, the outbreak of Ebola in West Africa required real-time modeling and simulation to identify the spread of the disease and to provide timely guidance for policymakers (Lofgren et al., 2014). Mathematical models might also be an effective tool to circumvent our imperfect ability to detect disease outbreak in livestock (Perry et al., 2013) within the SI context. In the United States of America, the number of animals per livestock operation has increased significantly 10

because large-scale facilities are more profitable, despite the fact that crowded animals are more susceptible to disease, thereby increasing health costs (Tilman et al., 2002).

Livestock diet balancing and formulation is crucial to make best and most profitable use of the feeds available in each unique production situation and deliver appropriate energy and nutrients that allow animals to express their genetic potential for growth, development, and production. It can also be important to minimize the excess of nutrients (those that will not be absorbed and utilized by the animal) that would otherwise be excreted into the environment. This practice is commonly known as precision feeding and has been defined as “feeding livestock so that animal performance is not adversely affected but so that nutrient excretion to the environment is the smallest quantity possible” (Cole, 2003). Other definitions including economic and social aspects have also been suggested. Opportunities have been documented for N and P nutrition (Cerosaletti et al., 2004; Vasconcelos et al., 2007) and feeding management (Vasconcelos et al., 2006). Similarly, mitigation strategies for methane emission by livestock, especially ruminants, have also been proposed (Eckard et al., 2010; Gerber et al., 2013; Knapp et al., 2014; Tedeschi et al., 2011a; Tedeschi et al., 2003). Diet and feeding practices that have been reported (based on survey analysis) to improve sustainability include: (1) minimize water pollution, deforestation, and air pollution from an environmental perspective; (2) produce animal protein affordably without competing with crop cultivation for human food or compromising ethical aspects of livestock wellbeing; (3) reuse feed waste after ensuring its safety, and (4) provide incentives to those adopting sustainable diet and ethical feeding practices (Makkar and Ankers, 2014). Many of these goals, however, can only be achieved with the assistance of decision support systems through computer modeling and simulation that accurately and precisely formulate diets that meet animal demand for energy and nutrients for an optimized performance under various production scenarios. Although mathematical models have been used to predict environmental impacts of ruminant production (Tedeschi et al., 2014a) with varying results, under certain conditions they can be used to explore areas of uncertainty of ruminant production on environmental impact and Feed the Future issues. Furthermore, uncertainty caused by statistical variation can be incorporated into stochastic models to forecast confidence regions. Forecasts are valuable to quantify technology impact or alternative production options on environment and food production as well as understand how these options will behave over time.

Mathematical models have expanded as our awareness of their potential for data mining and processing becomes more apparent. Mathematical and computational models can assist in addressing this deficiency through whole-farm modeling (WFM) simulation (Parsons et al., 2010; Snow et al., 2014). Though animal WFM submodels can vary considerably among species or systems (Tedeschi et al., 2014b), sustainability of beef (Rotz et al., 2013) and dairy (Rotz et al., 2010) life cycles can be assessed through production simulations scenarios using WFM; understanding how and when animals and crops affect the environment can provide timely guidance for policymakers. Data measurement and collection needed to understand livestock-environment feedback is often very time consuming. Therefore, the development and deployment of technology to support data collection for accurate and precise predictions will likely require a high degree of spatial resolution (e.g., high-definition satellite imagery) as well as fast and reliable data acquisition tools (e.g., unmanned aerial vehicles). Sustainable production systems: Livestock and grazing systems

Invasive species are any alien animal(s) or plant(s) that disturb an ecosystem by displacing more productive, nutritionally valuable native species and altering the normal behavior of natural cycles (Peterson, 2003). Invasive species (e.g., feral species) resulting from climate change or ecosystem disturbance can be a major hindrance to agriculture (Seward et al., 2004). They can be 11

useful, however, in developing resistant or resilient species or ecosystems. The genetic attributes of invasive species that allow them to thrive in harsh environments can likewise ensure the success of crops/grasses of interest in the same environmental conditions. Droughts in the United States of America in 2011, northeastern Brazil in 2013, as well as many other regions of the world, including southern Europe and most Mediterranean areas, raise a concern over sustainability and resilience of both native and sown grasslands (Craine et al., 2013). How can we identify herbaceous species that thrive under these stressful conditions? Volaire et al. (2014) proposed a conceptual prototype to analyze adaptive responses of perennial herbaceous species. They pointed out two major challenges for plant breeding and agro-ecology research to cope with recurrent drought problems: 1) select new plant material that incorporates long-term survival and persistence to droughts (i.e., drought resilience or drought resistant), and 2) use forage mixtures to ensure species diversity that would maintain a critical level of herbage mass production through environmental fluctuations. Kahmen et al. (2005) pointed out the importance of species diversity in grasslands not only for persistence but also for productivity, especially below ground.

The use of agroforestry (e.g., agro-silvo-pastoral systems) is a potential SI approach for grazing ruminants in several regions around the world. It is perceived that increased livestock production could be achieved if smallholder mixed-crop-livestock systems, which are mostly located in tropical regions, increase their productivity (Oosting et al., 2014). Campbell et al. (2014) illustrated this approach through feeding cattle Leucaena leucocephala, a legume tree that is widely available in tropical and subtropical ecozones. They reported that adding small amounts of Leucaena leaves to dairy cattle diets can increase daily milk yield and weight gain by at least threefold, besides decreasing methane produced per kg of meat and milk (Thornton and Herrero, 2010). In contrast, Rueda et al. (2003) indicated that even though supplementation with sorghum grain increased milk production and growth by 25 to 50% per animal on cattle farms in Brazil’s western Amazon, it was less profitable than current forage-only diets. In their case, SI could be better achieved by judicious fertilization of grass-legume pastures and greater stocking density rather than feed supplementation, highlighting the importance of mathematical model simulations for making management decisions. Veysset et al. (2014) classified Charolais cattle farms into four groups: specialized conventional livestock farms (100% grassland-based farms (GF), n = 7), integrated conventional crop–livestock farms (specialized farms that only market animal products but that grow cereal crops on-farm for animal feed, n = 31), mixed conventional crop–livestock farms (farms that sell beef and cereal crops to market, n = 21), and organic farms (n = 7), and analyzed their technical, economic, and environmental performances. The authors concluded that although all types of farms had comparable economic performances, the mixed conventional crop– livestock farms made heavier and consequently less efficient use of inputs, and had environmental performances less than the specialized farms. The route to success of agroforestry (i.e., agroecology, mixed conventional crop-livestock) farms is not easy and it may have many limitations for their long-term economic and environmental sustainability. The next big problem: water scarcity

Available water for food production refers to the sum of green water (naturally infiltrated into the soil) and blue water (water in rivers and aquifers). By 2050, Rockström et al. (2009) estimate there will be a shortage of blue water for 59% of the world’s population while 36% of the world population will face shortage of blue and green waters. These are seriously alarming estimates. Without taking into account the impact of climate change on agriculture, FAO (2011) estimated that agricultural irrigation will have to increase 11% to meet consumer demand for high quality food as the human population approaches 9 billion by 2050. This estimate could be 12

exacerbated by climate change (Schmidhuber and Tubiello, 2007). The forecast for agriculture is grim: “climate change will significantly impact agriculture by increasing water demand, limiting crop productivity and by reducing water availability in areas where irrigation is most needed or has comparative advantage” (FAO, 2011). To make things worse, Hilker et al. (2014) indicated that since 2000, precipitation has declined across 69% of the world’s tropical evergreen forest (5.4 million km2) and across 80% of all subtropical grasslands (3.3 million km2). The increase in irrigation needs, the reduction in rainfall, and the decline in terrestrial water storage in some parts of the world may indicate the beginning of tropical forest desiccation in the 21st century, leading to negative cascading effects on global carbon and climate dynamics (Hilker et al., 2014). Future Directions and Needs

Practical examples that can be used for SI in sub-Saharan Africa are available for livestock (FAO, 2002) and crops (Juma et al., 2013). Nonetheless, some questions remain unanswered for the sustainable livestock intensification concept. Will it adequately support the Feed the Future initiative? What are the differences between regional and global application? Are rational management and human intervention the keys to fostering resilient systems? The following are a few examples among many. Fetal programming

Maternal under- or over-nutrition during pregnancy are detrimental to growth, development, and the function of some biological systems in offspring, including reproductive development and efficiency (Mossa et al., 2015). Ferrell (1991) analyzed non-nutritional factors that can alter fetal development through embryo transfer between large (Charolais) and small (Brahman) genotype into recipient cows of similar and dissimilar genotype. Their data indicated that at about 230 days of gestation, fetal genotype accounted for most of the fetal weight variation among treatments, confirming that fetal genetics determine most of the fetal growth (Bell, 2005). After 230 days of gestation, however, dam genetics became increasingly influential on fetal weight, especially of Charolais fetuses transferred to Brahman dams. Fetal programming is based on the developmental origins of health and disease (Gotoh, 2015) in which alteration in the nutrition and endocrine status of the fetus or during the early postnatal phase can cause developmental alterations that permanently modify structure, physiology, and metabolism of the animal into adulthood.

Genetics and system sustainability One of the basic principles of quantitative genetics is that, by changing allele frequencies, the population mean for a given character can be changed. In the 20th century, livestock breeding was very effective at implementation of selection improvement programs. The important traits of the time were quantitative in nature (milk yield and growth rate) with moderate to large additive genetic control in many populations supporting that selection. Extension programs in the United States were important components of the overall improved agricultural environment of that century, and technological advances, especially in the last half of the century, contributed strongly. Although improvements in milk yield and beef quantity were not exclusively the result of selection, no one will contest that the genetic component of this improvement was substantial. For comparable amounts of milk yield, modern dairy production systems require only a fraction (one third or less) of the animals, feedstuffs, water, and land as compared with systems in use in 1944 (Capper et al., 2009). For comparable beef production, modern systems require approximately 70, 81, 88, and 67% of the animals, feedstuffs, water, and land, respectively, that were required in 1977, while producing 82, 82, 88, and 83% of the manure, methane, carbon dioxide, and total carbon footprint, respectively (Capper, 2011a). However, as growth increased for cattle in beef production, mature BW also increased, accompanied by increases in daily resource use and GHG emissions (Capper, 2013). 13

As described by Capper and Bauman (2013), the “dilution of maintenance” concept is the improvement of animal productivity such that a greater quantity (e.g., meat or milk) is produced in a given time period. Systems with such economies of scale excel with respect to absolute resource efficiency and utilization. Additionally there is an upper biological limit to traits like growth and milk production (Capper and Bauman, 2013). The beef production system in the United States of America is segmented; efficiency in one segment is often antagonistic to efficiency in another segment. For example, although increased BW/growth rate of cattle facilitates economies of scale from the point where cattle are fed high concentrates in feedlots through slaughter and beef packaging, the heifer half-siblings of those steers are bred to join the nation’s cow-calf herd. Body weight and other mature size measures of those females increase but many aspects of reproduction are diminished with increased size (Vargas et al., 1999). Other important components of the production system may be influenced negatively; for example, greenhouse gases emissions such as methane would positively associate with mature cow size (Capper, 2013). Smaller cows would require fewer resources for comparable production vis-á-vis larger cows, even if they were equally productive from a fertility/maternal perspective. Although more “traditional” extensive production systems may be perceived to be more sustainable from many perspectives, the reality is often demonstrated to be otherwise (Capper, 2011b, 2012; Stackhouse et al., 2011). System sustainability should consider more than those genetic characters traditionally representative of animal productivity (Hermansen and Kristensen, 2011; Van Eenennaam, 2013). What appears to be missing in the evaluation of system sustainability is the inclusion of traits such as cow reproduction that are considered less from an economy-of-scale perspective. The additive genetic component of variation in such traits is low, making selection a more long-term process with less visible results in the short term. This is the opposite of the 20th century success traits, namely milk yield and growth or weight, which are certainly more responsive in the short term. There are, however, genetic improvement approaches for such traits that are successful in the short term, such as crossbreeding. A second concern with current assessment of system sustainability could be the global approach. Cow adaptation to local environments is a primary animal breeding strategy for sustainable beef production (Olesen et al., 2000). For example, consider a resource-limited environment, perhaps drought conditions. Cows can contribute to their population by 1) diverting all their energy into procreation, often at the expense of their long-term reproductive efforts, or by 2) living another year but withholding nutrients and energy from the reproductive process. Our own anecdotal related observations are that highly productive European breeds of cattle respond with the former strategy and adapted, but otherwise less productive “indigenous” cattle breeds often employ the latter. Population growth and demand for protein will grow most this century in tropical developing nations (Laurance et al., 2014). Some aspects of adaptation to such environments have been quantified and evaluated genetically (Riley et al., 2011; Riley et al., 2012), but much potential characterization remains. This may not be as severe a concern if reproduction traits are considered as an outward manifestation of adaptation. Flexibility of response to environmental stress could strongly influence system sustainability through environmental perturbations. Genetic flexibility appears to be an essential part of livestock production system sustainability. Conservation genetics and diversity studies illustrate one aspect of flexibility: diversity statistics indicating heterozygosity at markers and genes throughout the genome (Van Eenennaam, 2013). Markers are visible but without function (some researchers categorize markers into those without and with function), but believed to be linked closely to unknown, unseen genes. Genomic markers may not be in coding regions (genomic regions with some function, for example those coding for RNA that may or may not be ultimately translated into proteins); coding regions may be more conserved, that is, they maintain their identity without permitting alternative versions, because those may prevent protein 14

production. It would therefore be essential that diversity should not be considered exclusively from a heterozygosity-of-marker perspective. Theoretically, continued selection for a single character will reduce additive genetic variation; that is, it will tend to make animals homozygous for many loci, reducing heterozygosity, and flexibility associated with having multiple alleles (alternative versions of a gene). Meiosis as a diversity-producing mechanism would be offset by increased homozygosity of individuals at many loci. As genes of importance continue to be characterized, allelic diversity for those genes may be a worthy consideration in livestock planning and breeding programs.

Preservation and conservation of breeds or races as they exist could maximize future genetic flexibility as environmental perturbations are encountered. Breeds are populations with allele frequencies that have been influenced by common (to the breed) forces across time. These would be particularly important with respect to traits of adaptation to local conditions, as redevelopment of these adaptations may be costly in terms of loss of individuals and of long-term persistence of the population. This conservation may be a more prominent issue in developing countries, as historically one pattern of improving productivity is to import productive breeds or races and then attempt to alter the local environment to accommodate them. Capper and Cady (2012) demonstrated differential breed contribution to a component of system sustainability (environmental impact) in the cheese production. It may be attractive to consider that genetic improvement could eventually result in highly selected sets of animals that would excel in all situations. Of course, no animal is best at everything; limited nutrient partitioning to growth, for example, depletes those nutrients available for other life processes. Selection indices can be developed to assign weights to important characters for selection. Although a global emphasis and perspective is beneficial overall (Herrero and Thornton, 2013), improvement planning must be implemented at not too broad a geographic scale, as different genotypes (aggregate) will perform unequally in local environments. An objective would be to develop and promulgate systems of parental ranking (expected progeny differences, predicted breeding values, or some other metric representing genetic merit) at the smallest reasonable local level possible, at least at the country level. It would also seem to be important to extend the scope of genetic investigation and implementation of advanced methodology to smallholder livestock systems that are often characteristic of tropical protein production. Those efforts at present are limited (Ejlertsen et al., 2012; Marshall et al., 2011). Genomic improvement programs (Capper et al., 2008; Capper and Hayes, 2012; Stackhouse et al., 2011) could have increasing impact as they develop as part of sustainability of food production systems. Although this has not proceeded at a desired rate for selective improvement of livestock, the expectation is that it will become reality at some point. As discussed above, consumer perceptions and social acceptability of the technology will have an important effect on designing sustainability systems (Croney et al., 2012; White and Capper, 2013); sometimes the general public might be antagonistic to true sustainable systems and SI cannot be achieved. Conclusions

Sustainable intensification will be necessary to increase food production that meets the needs of a growing world population. We will need to address multiple facets within animal agriculture systems, especially in economically and environmentally challenged environments. Feed efficiency is likely the single most important variable that can drive up output per input ratio while concurrently reducing the carbon footprint of ruminant production by reducing greenhouse gas emissions (i.e., methane) under climate-change conditions. Thus, the identification and selection of feed efficient animals, feeding systems, and technologies that will improve efficiency of nutrient use are priorities for sustainable livestock intensification programs. Taking full 15

advantage of genetic potential within animal genomes, whether from a breed or selection perspective, will likewise contribute. Certainly investing these options within the world’s socially and economically marginal populations that constitute today’s predominant animal agriculturalists seems likely to have positive returns. Computerized mathematical models based on sound science will be needed to integrate accumulated biological and management knowledge to identify and implement the most productive, economically and environmentally sustainable system in each unique production situation. Pretty et al. (2011) drew several lessons from their experiences in spreading the use of SI, including (1) synergize scientific knowledge and farmer experience into technologies and practices that integrate crops and animal production with agroecological and agronomic management; (2) develop social infrastructure that builds trust among individuals and agencies; (3) improve farmer awareness, knowledge, and capacity through extension and other education programs; (4) engage with industry and private partners to supply goods and consulting services; (5) focus on genderneutral educational, microfinance, rural banking, and agricultural technology needs; and (6) foster public support for agriculture. Sustainable intensification is not the result of improved biological and physical processes alone; it requires the intervention of human ingenuity to manage the system appropriately and intelligently in an integrated way. The increase in food production has to be achieved through enhanced yield (output/input) rather than expanding the area of land as the latter may further increase environmental burden. Smallholder farmers may benefit the most from SI that leads to greater integration into the market economy.

In deploying the technology, we concede that a catchy phrase or slogan such as sustainable livestock intensification will not miraculously solve the problem at hand and it may inexorably backfire if interest groups apply it in ways for which it was not intended, thereby corroding the technology. Finally, the question that remains still unanswered is: will SLI effectively increase the supply of animal products for those who would benefit the most? From a broader perspective, a more daunting question is whether global agriculture in 35 years will be able to feed 10 billion people, produce biofuel and clean electricity, supply fiber, and at the same time reduce its environmental footprint to avert or even reverse climate change? Literature Cited

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