The role of ruminant animals in sustainable livestock ...

International Journal of Sustainable Development & World Ecology

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The role of ruminant animals in sustainable livestock intensification programs Luis Orlindo Tedeschi, James Pierre Muir, David Greg Riley & Danny Gene Fox To cite this article: Luis Orlindo Tedeschi, James Pierre Muir, David Greg Riley & Danny Gene Fox (2015) The role of ruminant animals in sustainable livestock intensification programs, International Journal of Sustainable Development & World Ecology, 22:5, 452-465, DOI: 10.1080/13504509.2015.1075441 To link to this article: http://dx.doi.org/10.1080/13504509.2015.1075441

Published online: 25 Aug 2015.

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Date: 16 October 2015, At: 03:20

International Journal of Sustainable Development & World Ecology, 2015 Vol. 22, No. 5, 452–465, http://dx.doi.org/10.1080/13504509.2015.1075441

The role of ruminant animals in sustainable livestock intensification programs Luis Orlindo Tedeschia*, James Pierre Muirb, David Greg Rileya and Danny Gene Foxc a

Department of Animal Science, Texas A&M University, College Station, TX, USA; bTexas A&M AgriLife Research, Stephenville, TX, USA; cDepartment of Animal Science, Cornell University, Ithaca, NY, USA

Downloaded by [University of Otago] at 03:20 16 October 2015

(Received 3 June 2015; final version received 18 July 2015) Food supply has improved considerably since the eighteenth century industrial revolution, but inadequate attention has been given to protecting the environment in the process. Feeding a growing world population while 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. Sustainability represents the state of a complex system that is always evolving. It is an intrinsic characteristic of the system that needs to be shaped and managed. A sustainable system has the ability to coexist with other systems at a different output level after a period of perturbation. Resilience is the ability of a system to recover and reestablish a dynamic equilibrium after it has been perturbed. Sustainable intensification (SI) produces more output(s) through the more efficient use of resources while reducing negative impact on the environment; it provides opportunities for increasing animal and crop production per area while employing sustainable production alternatives that fully consider the three pillars of sustainability (planet, people, and profit). Identifying the most efficient animals and feeding systems is the prerequisite to successful applications of sustainable livestock intensification programs. 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. System modeling is a powerful tool because it accounts for many variables and their interactions involved in identifying sustainable systems in each situation. Keywords: cattle; efficiency; environment; production; sustainability; systems

Introduction Discussions about sustainability come at a time when the awareness of climate change (i.e., global warming) and food shortage risks has never been greater. Frustration by the scientific community regarding mitigation of climate change is augmented by the perception that the 2009 international climate negotiations in Copenhagen failed miserably; it was a ‘festival of conspiracy and betrayals’ (Goodell 2010). Sustainability concerns could become entangled in the same web of politics as the Copenhagen negotiations if science is not taken seriously by conquering battles against environmental pollution and hunger. We believe that 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 from 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 eighteenth century (Sabine et al. 2004). Food production to date 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 may have likely contributed to disrupting natural cycles from a biodiversity point of *Corresponding author. Email: [email protected] © 2015 Taylor & Francis

view (Ripple et al. 2015) to an environmental perspective by adding sequestrated carbon back into the atmosphere at a much faster pace than it can be immobilized (Sterman 2008). Terrestrial ecosystems react to atmospheric CO2 concentration such that their short- and long-term feedback likely plays a role in climate change. Their precise contribution is largely uncertain because they impact diverse ecozones and the time and extent of impact cannot be accurately 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 Food and Agriculture Organization (FAO) (Food and Agriculture Organization 2006) reports that the livestock sector was responsible for 18% of the 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). 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 the staggering growth in world population, projected to be 9.55 billion by 2050 (United Nations 2013), while


International Journal of Sustainable Development & World Ecology reducing the impact on the environment. This task poses a progressively more challenging and constant pressure on crop and soil resources as well as 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, Nicholson, et al. 2011). 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, and by consuming non-renewable resources (e.g., fossil fuel). In turn, the changes in the 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 Future1 concept likely had its inception in the 1960s, but not until recently it has been given much attention. The success of Feed the Future depends on innovative solutions that foster greater agriculture production while maintaining profitability and minimizing environmental impact. We believe that the current rate of food animal agriculture will likely fail to meet the expected growth in global demand for animal protein, especially if it is 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 twenty-first century as dramatic increases in global demand for food protein (e.g., meat, fish, eggs, and dairy products) are forecasted by 2050 due to an 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 and resources. The future of this competition is uncertain as it depends on regulations, subsidies, and petroleum prices, and these policies vary across countries and time (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 (>40°C) and the number of days with the temperature humidity index above comfort threshold (i.e., 68) have increased in recent years in countries located within the temperate zone, thereby negatively affecting agriculture and livestock production (Silanikove & Koluman 2015). A warming environment clearly affects crop yields. Since 1989, long-term temperature trends and precipitation

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totals in Europe have been adverse to wheat and barley yields, which have dropped 2.5% and 3.8%, respectively, despite large heterogeneity across European regions (Moore & Lobell 2015). On the other hand, corn and sugar beet yields have increased slightly. These authors suggest that climate trends account for approximately 10% of the consistent lack of change in European crop yields. 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 that 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 such as 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 successful long term. The need for more productive and sustainable food production systems Increased concern about sustainable food production systems has resulted in rhetorical discussions and diverse opinions on the definition of sustainability. Opinion papers have presented contrasting schools of thought (e.g., business-as-usual optimists, environmental pessimists, and 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 (Meadows et al. 1972; Arrow et al. 1995; Meadows & Meadows 2007) 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 weatherrelated 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)


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Figure 1.

L.O. Tedeschi et al.

The three pillars of sustainability.

Source: Based on ‘sustainable development’ from Wikimedia.org under Creative Commons licensing, and further adapted from United Nations (1987), IUCN (2005), Makkar (2013), and Makkar and Ankers (2014).

(Makkar 2013). Thus, SI requires that food production systems be profitable, socio-culturally acceptable, beneficial to the people, as well as protective of natural resources (Figure 1). SI supports climate change mitigation and the Feed the Future initiative. Food yield must be improved in both areas that are already highly productive and those that are currently underutilized. For about 80% of the chronically hungry people 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 of areas that were previously underutilized. A 2010 report analyzing 40 SI projects from 20 countries during the 1990s–2000s indicated that about 10 million farmers had benefitted from SI projects with improvements on approximately 12.75 million ha (Pretty et al. 2011). Animal products and crop yields per hectare increased by combining the use of new and improved crop varieties. The main challenge with SI adoption is the spread of effective processes and education to many more people, generally smallholder farmers and pastoralists across the world (Pretty et al. 2011). 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 materials for foodproducing 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 (Zilberman 2015) as follows: 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. Another social conflict with SI stemming from misconception is that some believe that livestock always competes with human food supplies because grain (e.g., corn) or cultivated pasture used to feed them could be used directly as human food and, thus, they assume that the use of livestock for food production 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 that convert human inedible sources of energy and protein to human food, the protein conversion efficiency on a human-edible basis is usually greater than 1:1. This indicates that the humanedible animal protein product is greater than the humanedible feed consumed by the animal (CAST 1999),

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 countries1. Gross efficiency Country Argentina

Species Energy Protein

Swine Poultry Milk Beef Mexico Swine Poultry Milk Beef South Korea Swine Poultry Milk Beef United States Swine Poultry Milk Beef

0.15 0.18 0.19 0.02 0.13 0.2 0.12 0.06 0.20 0.21 0.26 0.06 0.21 0.19 0.25 0.07

0.07 0.30 0.16 0.02 0.08 0.33 0.11 0.02 0.16 0.34 0.19 0.06 0.19 0.31 0.21 0.08

Human-edible efficiency Energy

Protein

0.24 0.28 4.61 3.19 0.25 0.34 0.79 16.36 0.35 0.30 3.74 3.34 0.31 0.28 1.07 0.65

0.11 0.69 1.64 6.12 0.21 0.83 1.06 4.39 0.51 1.04 14.3 6.57 0.29 0.62 2.08 1.19

Note: 1Gross efficiency was calculated as outputs of human-edible energy and protein divided by the total energy and protein consumed by the animals. Human-edible efficiency was computed as outputs of humanedible energy and protein divided by human-edible energy and protein consumed by the animals. Source: Adapted from CAST (1999).

International Journal of Sustainable Development & World Ecology confirming the undeniable benefit of ruminant 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.


Understanding sustainability terminology 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 (also known as the Brundtland Commission’s Report) (United Nations 1987), 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 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. Some believe that sustainable agriculture is more a learning process than prescribed specific and rigorous application of technologies, practices, or policies (Pretty 1997). Regardless of the definition(s) adopted for sustainability, one needs to clarify what is being sustained, boundaries of the system, intensity of disturbances, and timescale. 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

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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. 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 range. A system (or an organism) is sustainable when it continues to coexist 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. 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. In other words, resilience is achieved through reinforcing and

Figure 2. Schematic illustration of different levels of hypothetical sustainable responses by a system after a period of perturbation(s) or stress(es).


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balancing feedback loops that permit the system to persist over time despite temporary disruptions, by either intrinsic (e.g., endogenous) or surrounding environment 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 an increased growth rate (Ryan 1990). Resiliency is the ability of a system (or organism) to be resilient or to absorb stress without suffering permanent damage to the biological system. 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 back, 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 neither be resilient nor sustainable anymore, and the fate of the system (or organism) is a general failure (collapse) 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 weight gain 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, 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 the output level for a period of time and then recover growth rates (but less likely the total yield vis-à-vis their genetic potential) to pre-stress levels. Others have defined natural systems as resilient when they tend to maintain their integrity under disturbances, and stability is 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. Folke (2006) provided additional information regarding the logic behind the definition of resilience by the ecologist C.S. Holling. Later, Holling (1996) discussed about the differences in the definition of engineering resilience versus ecological resilience. 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). Many studies have assumed ecosystem resilience as the capacity of a system to absorb disturbance, and few studies have focused on the ability of resilient ecosystems to re-organize while undergoing changes, generating adaptive capacity to sustain themselves (Folke 2006). 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 component. 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 after that interference, however, the system (or organism) may or may not find a new level of stability. We conclude that SI is the best overall concept that describes the approach 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 impact on the environment. The origin of SI dates back to the 1990s


International Journal of Sustainable Development & World Ecology when the term was developed for African production systems in which low yields and high environmental degradation predominated, prompting an immediate pejorative use of SI to be synonymous with and applicable only to smallholder-oriented or organic production technology (Garnett & Godfray 2012). SI is not the only concept that associates sustainability and agriculture production. There is substantial overlap and 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 & Godfray 2012; Kuyper & Struik 2014) that have triggered negative, defensive perceptions for those within mainstream agricultural production and research uncomfortable with its implications. Tittonell (2014) concluded that SI is more loosely defined than ecological intensification. Struik et al. (2014) indicated that SI is by definition an oxymoron 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 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 climatesmart 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 important to stress that SI entails the increase of food production from the existing farmland without additional environmental impact. The responsive behavior shown in Figure 2 illustrates SI: the 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) was 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 always evolves and transforms itself; thus, it cannot entail a production goal. It is an intrinsic characteristic of the system that needs to be fostered and then 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 does so by increasing the output per unit of input. A systems approach incorporates multiple means to attain system sustainability. For example, input resource use and

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energy intensity reduction, water 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, supply, distribution, allocation, storage, and utilization may prevent food from reaching those who 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 systems thinking put forward by three individuals: the American ecologist Bernard Patten, German sociologist Niklas Luhmann, and Austrian-born architect Christopher Alexander. Patten’s (1978) contributions to the importance of defining an 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 the environment in exchanging energy, matter, and information that keeps the system active and sustainable. The parallel insights of these individuals remarkably emphasize the ideas discussed for business dynamics (Forrester 1961, 1971, 1973; Sterman 2000; Maani & Cavana 2007; Morecroft 2007; Warren 2008) and animal agriculture (Tedeschi, Nicholson and Rich 2011) 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. How 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 assesses the scientific evidence of strengths and weaknesses inherent in deploying sustainable agriculture in various production, marketing, and policy approaches. It also examines its unintended consequences. The NRC committee (2010) 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 presentday 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.

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Sustainable livestock intensification Livestock production impacts the environment in many ways. About 20–30% of land utilized by humans is grazed while another 7–10% produces animal feed and forage (Janzen 2011). 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 the 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 to aquifers and surface water originates from livestock (Janzen 2011); thus, sustainably managing livestock N and P excretion (Klausner et al. 1998; Eghball 2002), 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 (SLI) is needed in animal production through the use of technologies that can be used to increase food from animals. 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 conservation. Livestock production in sub-Saharan Africa has negatively impacted wildlife biodiversity mostly due to competition and disease (Ripple et al. 2015). Some 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 SLI concept. The foremost intriguing question is whether the available data on livestock is sufficient to adequately evaluate SLI implementation. In particular, the FAO (2014) has questioned the validity and quality of available data on livestock in Africa. Additional questions include: will SLI adequately support the Feed the Future initiative? What are the differences between regional and global applications? Are rational management and human intervention the keys to

fostering resilient systems? The following are a few examples among many.

Smallholder animal farmers and sustainable livestock intensification Strategies for intensifying ruminant production around the world cannot ignore smallholder or subsistence farmers 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 (Stür et al. 2002; Atuhaire et al. 2014), 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 female participants keep progress to a minimum (Esilaba et al. 2005; Mekonnen et al. 2010; Fon Tebug et al. 2012; 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 (Stür et al. 2002; Esilaba et al. 2005; Bayemi et al. 2009, Mekonnen et al. 2010; Le Gal et al. 2013).

The role of decision support systems to assist sustainable livestock intensification The lack of awareness and limited knowledge of mathematical models and how they can be used to design more efficient and profitable animal feeding and management systems 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 others (Garnett & Godfray 2012), who recommend that a more system-oriented approach to decisionmaking is needed to develop substantial programs of future activity related to SI. System integration through modeling is necessary for successful sustainability. Some (Liu et al. 2015) have indicated that ‘holistic approach to integrating various components of coupled human and natural systems across all dimensions is necessary to address complex interconnections and identify effective solutions to sustainability challenges.’ This is in line


International Journal of Sustainable Development & World Ecology with the concept of social-ecological resilience in which the focus is on the complex adaptive transformability, learning capacity, and ability to innovate; it relies on integrated systems feedback and cross-scale interactions that are inherent to the dynamics of the system (Folke 2006). 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, the number of animals per livestock operation has increased significantly because large-scale facilities are more profitable, despite the fact that crowded animals might be more susceptible to spreading infectious disease, thereby increasing health costs (Tilman et al. 2002). Accurate 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 nitrogen and phosphorus 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 (Tedeschi et al. 2003; Eckard et al. 2010; Tedeschi, Callaway, et al. 2011; Gerber et al. 2013; Knapp et al. 2014). Diet and feeding practices that have been reported (based on survey analysis) to improve sustainability include (1) minimizing water pollution, deforestation, and air pollution from an environmental perspective; (2) producing animal protein affordably without competing with crop cultivation for human food or compromising ethical aspects of livestock wellbeing; (3) reusing feed waste after ensuring its safety; and (4) providing incentives to those adopting sustainable diet and ethical feeding practices (Makkar & 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, Cavalcanti, et al. 2014) with varying results,

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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 create forecast confidence regions. Forecasts are valuable to quantify technology impact or alternative production options on the 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 wholefarm modeling (WFM) simulation (Parsons et al. 2010; Snow et al. 2014). Though animal WFM submodels can vary considerably among species or systems (Tedeschi, Herrero, et al. 2014), 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). Current sustainable intensification opportunities for livestock and grazing 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 useful, however, in developing resistant or resilient species or ecosystems. The genetic attributes of invasive species that allow them to thrive in harsh or rapidly changing environments can likewise be used to ensure the success of crops/grasses of interest under the same challenging conditions. Droughts in the United States in 2011 and in northeastern Brazil in 2013 as well as many other regions of the world, including southern Europe and most Mediterranean areas, raise a concern about the 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? A conceptual prototype to analyze adaptive responses of perennial herbaceous species has been proposed (Volaire et al. 2014). The authors point out two major challenges for plant breeding and agroecology research to cope with recurrent drought problems: (1) selecting a new plant material that


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incorporates long-term survival and persistence to droughts (i.e., drought resilience or drought resistant) and (2) using forage mixtures to ensure species diversity that would maintain a critical level of herbage mass production through environmental fluctuations. The importance of species diversity in grasslands is not only for persistence but also for productivity, especially below ground (Kahmen et al. 2005). The use of agroforestry (e.g., agro-silvo-pastoral systems) is a potential SI approach for grazing ruminants in several regions around the world. This approach has been illustrated through feeding Leucaena leucocephala, a legume tree that is widely available in tropical and subtropical ecozones (Campbell et al. 2014). They report that adding small amounts of Leucaena leaves to dairy cattle feed can increase daily milk yield and weight gain by at least threefold, besides decreasing methane produced per kg of meat and milk (Thornton & Herrero 2010). In contrast, others (Rueda et al. 2003) indicate that even though supplementation with sorghum grain increased milk production and growth by 25–50% 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.

The next big problem: water scarcity Available water for food production is the sum of green water (naturally infiltrated into the soil) and blue water (water in rivers and aquifers). By 2050, 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 (Rockström et al. 2009). These are seriously alarming estimates. Without taking into account the impact of climate change on agriculture, the FAO (2011) estimates that agricultural irrigation will have to increase 11% to meet consumer demand for high quality food as human population approaches 9 billion by 2050. This estimate could be exacerbated by climate change (Schmidhuber & 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’ (2011). To make things worse, 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) (Hilker et al. 2014). 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 twenty-first century, leading to negative cascading effects on global carbon and climate dynamics (Hilker et al. 2014).

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 twentieth century, livestock breeding was very effective in implementation of selection improvement programs. The important traits of the time were quantitative in nature (milk yield and weight or growth rate) with moderate to large additive genetic control in many populations supporting that selection. 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, which were required in 1977, while producing 82%, 82%, 88%, and 83% of the manure, methane, carbon dioxide, and total carbon footprint, respectively (Capper 2011a). As growth increased for cattle in beef production, however, mature BW also increased, accompanied by increases in daily resource use and GHG emissions (Capper 2013). The beef production system in the United States is segmented, and efficiency in one segment is often antagonistic to efficiency in another segment. For example, although the 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. BW 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 gas emissions such as methane would positively associate with mature cow size (Capper 2013). Smaller cows would require fewer resources for comparable production of 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 (Stackhouse et al. 2011; Capper 2011b, 2012). System sustainability should consider more than those genetic characters traditionally representative of animal productivity (Hermansen & 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, which are considered to be 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


International Journal of Sustainable Development & World Ecology results in the short term. This is the opposite of the twentieth 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 assessments 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 experiencing 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 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, 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. Response flexibility to environmental stress could strongly influence the 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). 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 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

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weight important characters for selection. Although a global emphasis and perspective is beneficial overall (Herrero & Thornton 2013), improvement planning must be implemented at not too broad a geographic scale, as different genotypes (aggregates) will perform unequally in local environments. The development and implementation of genomic improvement programs (Capper et al. 2008; Stackhouse et al. 2011; Capper & Hayes 2012) could increasingly impact the 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, producer antagonism, consumer perceptions, and social acceptability of SI technology will have an important effect on designing sustainable systems (Croney et al. 2012; White & Capper 2013); widespread public support for genuinely sustainable systems, including openness to lifestyle changes, will foster greater SI successes. Conclusion SI will be necessary to increase food production that meets the needs of a growing world population. Addressing multiple facets within animal agriculture systems, especially in economically and environmentally challenged environments, will likely be needed. The feed efficiency may be the single most important variable that drives up output per input ratio while concurrently reducing ruminant production carbon footprint 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 nutrient use efficiency are priorities for operations incorporating or research programs developing SLI. Taking full advantage of genetic potential within animal genomes, whether from a breed or selection perspective, will likewise contribute. Certainly, investing in these options within the world’s socially and economically marginal populations that constitute today’s predominant animal agriculturalists seems to be 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 the experiences in spreading the use of SI, including (1) synergizing scientific knowledge and farmer experience into technologies and practices that integrate crops and animal production with agroecological and agronomic management; (2) developing social infrastructure that builds trust among individuals and agencies; (3) improving farmer awareness, knowledge, and capacity through extension and other education programs; (4) engaging with industry and private partners to supply goods and consulting services; (5) focusing on gender-neutral


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education, microfinance, rural banking, and agricultural technology needs; and (6) fostering public support for agriculture. SI 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. Food production increase has to be achieved through enhanced yield (output/input) rather than expanding land area 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 have to concede that a catchy phrase or slogan such as sustainable livestock intensification will not miraculously solve the problem in hand and it may unintentionally backfire if interested groups apply it in corrosive ways for which it was not intended. Finally, the question that remains unanswered is: will SLI effectively increase animal product supply 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.

Disclosure statement No potential conflict of interest was reported by the authors.

Note 1.

http://www.feedthefuture.gov

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