Modelling Value at Risk: Evidence from the Saudi Stock Market

This paper aims to estimate Value at Risk (VaR) of Tadawul All Shares Index of Saudi Stock Market (TASI) over the period January 2004 – December 2017. It applies the following methods, empirical quartile, historical simulation (HS), and percentile, parametric via delta normal, GARCH, IGARCH, Monte Carlo simulation and bootstrapping simulation. It uses 5% and 1% critical value under Normal distribution. Back- testing based on likelihood ratio LR accepted empirical quartile at both five and one percent, while accepting delta normal, historical simulation, percentile, IGARCH, and Monte Carlo at one percent. The worst loss obtained is approximately 4%.

INTRODUCTION
There is unanimous agreement about the concept of Value-at-Risk (VaR). All definitions comprise four elements, that is single measure of certain amount of portfolio loss over specified period with a probability (1-α) due to market movements (Linsmeier and Pearson 1996, Manfredo and Leuthold 1998, Tsay 2002, Fernandez 2003, Lamantia, Ortobelli, and Rachev 2006, Čorkalo 2011, Bucevska 20 13, Adabi, Mehrara and Mohammadi 2015, Bingqiu (2016 and Glyon 2017). No doubt, that VaR took great importance since the recommendation of Basel Committee in 1996 (Aloui and Ben Hamida 2015). VaR is the dollar or percentage loss in a portfolio (asset) value that will be equal to or exceeded only X percent of time.
There was high expectations laid on attracting foreign portfolio investment because of opening the Saudi stock market to foreign portfolio investment since June 2015. Foreign participation would open up opportunities associated with access to a huge untapped market. Saudi stock exchange has met conditions related to "size and accessibility" criterion. Two major risks are probably face KSA that is an increase in short-term volatility in the financial system and its monetary policy being dependent on that of the US (Aljazira Capital 2015). Altogether, these entail estimating as accurate as possible market risk quantification. The Tadawul All Share Index (TASI) is a major stock market index, which tracks the performance of all companies listed on the Saudi Stock Exchange. The year 1985 witnessed the introduction of index has a base value of 1000 and its recognition on June 30, 2008 (Yahoo Finance). However, factors including a strong economy support Tadawul (Aljazira capital 2015).
The motivation behind this paper is the scarcity of empirical research concerning VaR for Saudi Stock Exchange. To my knowledge, only one paper has dealt with estimating Value at Risk for Saudi and Gulf Cooperation Council stock markets (Aloui, and Ben Hemida 2015). The difference between Aloui & Hemida and me is that they used only non-linear GARCH class

Historical Simulation
The historical simulation approach requires few assumptions about the statistical distribution of the underlying market factors taking the current portfolio, subject it to the actual change (Linsmeir and Pearsom 1996). It does not constrained by requiring assuming normality. All we need to do is to sort data from worst to best so here we are looking at the tail to answer the question what is the α% VaR in other words with (1-α)% confidence level we don't expect the loss to be worse. Then we are answering it by just looking at our historical sample so by means of the percentile function to identify five percent tail of the overall count, the VaR is right outside the five percent tail (Ba ach). Tsay (2001) sets ∆P v = w 0 be the change in value of the assets in the financial position from time t to t + l, designates the cumulative distribution function (CDF) of ∆P v by x y D .Then states the VaR of a long position over the time horizon l with probability p as: The VaR in the (1-α) confidence level is the sample mean minus the percentile of the standard normal distribution multiplied by the portfolio standard deviation (Chen, Chen 2013

Risk Metrics
In the context of risk measurement, a Risk Metrics is the concept enumerated by a risk measure. When choosing a Risk Metrics, an agent is picking an aspect of perceived risk to investigate, such as volatility or probability of default. PH{ = ÄÅÇ6É ÅT zÅÑ9É9Å6 − 1.65Q 0Ü+; PH{(à) = àPH{ â 0 = 0; Q 0 -= JQ 07+ -+ 1 − J w 0 -= äã{åç 1,1 :wÅéèÑÑ ê9ÉℎÅÇÉ íw9TÉ Data: To calculate VaR at 99.9 confidence level, we need not less than1000 observations. Extracting VaR from historical data requires choosing the desired confidence level and picking out the nth observation in the historical data that corresponds to that confidence level. Thus, VaR is the nth percentile of the values in the chosen data set (Tsay 2002).

Conditional Value at Risk (CVaR)
CVaR is generally a better approximation of potential losses for several different scenarios where the highest loss are on the right. We define a probability level (1-α)% and consider the scenarios whose losses exceed this level. The (1-α)% defined as the average of losses in these scenarios. It is an average of expected losses of the lowest cases than a wide range of potential losses (Čorkalo 2011). To find exactly how much we will lose in average in our worst-case scenarios we have to look at CVaR values. CVaR (α %) tells us that in worst ten percent of our returns the average gain will be 0.xxx percent. CVaR (CL) = 1/VaR reference*sum from the first sorted return through VaR cell reference. Count = count built in function, VaR (CL) reference = (1-confidence level (CL)*count (Uryasev and Stan (2011); Čorkalo 2011, Diedwaedo)

Quantile and Order Statistics
Assuming that the distribution of return in the prediction period is the same as that in the sample period, one can use the empirical quantile of the return r to calculate VaR (Tsay 2002). Arrange returns in increasing order. Assume that the returns are independent and identically distributed random variables that have a continuous distribution with probability density function (pdf) f (x) and CDF F(x). D 4 = : -− : : -− : + w ì > + : − : + : -− : + w ì c ; î + < 6: < î -; : ' = î ' 6

Classical value at risk versus GARCH-based models
Volatility clustering occurs when a period of large returns is followed by a period of small returns (Nelson, 1991). The second property (fat tail) indicates that large positive or large negative observations in financial data occur more frequently as compared to the standard normal distribution. Nonlinear dependence explains the relationship between multivariate financial data. Nonlinear dependence between different assets have co-movement in the same direction relevant to some market conditions (Danielsson, 2011).

Backtesting
Backtesting refers to testing a predictive model using existing historic data, i.e. a kind of cross validation applied to time series data in trading strategy, investment strategy or risk modeling. It seeks to estimate the performance of a strategy or model if had been employed during the past period then requires simulating past conditions with sufficient details. One limitation of backtesting the need for detailed historical data, a second limitation its mobility to model strategies that would affect historic prices, finally backtesting like other modeling is limited by potential over-fitting i.e. it is often possible to find strategy that would not work well in the future. Despite these limitations, backtesting proved to be a useful tool. Backtesting refers to testing predictive model using existing historic data. It a kind of cross validation (Hurlin and Tokpavi 2014) We reject the null hypothesis if the outcome is greater than ù + 1 https://www0.gsb.columbia.edu/faculty/pglasserman/Other/masteringrisk.pdf

Monte Carlo Simulation (MCS)
Monte Carlo simulation involves conducting repeated trials of the values of the uncertain input(s) based on some known probability distribution(s) and some known process to produce a probability distribution for the output. That is, each uncertain input or parameter in the problem of interest is assumed to be a random variable with a known probability distribution (Čorkalo 2011, Cheung & Powell 2012. (Chen, Chen 2013) gives the Brownian motion: Where St is the share price at time t, e is the natural log, ∆É is the time increment (expressed as a portion of a year in terms of trading days) and °0 is the randomness at time ∆É introduced to randomize the change in share price. After some rearrangement the above equation becomes Monte Carlo simulation multiplies the last entry by the exponential of the sum of drift and the product of the standard deviation by inverse of the normal distribution.

Bootstrapping Simulation
The key difference between Monte Carlo simulation and bootstrap is that Monte Carlo uses algorithm to generate portfolio path forward in time whereas bootstrap uses the historical return instead of the algorithm. The benefit of the bootstrapping is that it implicitly takes the volatilities and correlations present in the historical data (Dutta and Bhattacharya 2006). The standard steps are first we index the historical returns (daily returns) by means of natural logarithm. The second step is the essence of bootstrapping we randomly select cross-sectional vector that means we go back and select randomly a day from a historical window use that days returns. We use the built-in Excel function [INT(RAND()*last date)+1] to generate random uniform variable that is going to be between 0 &1. Third we random selection to simulate forward that give us the price of portfolio at some point in the future. The fourth step we are going to repeat that as many time as we want which gives us n number of hypothetical portfolio in the future. The final step we sort that list from top to bottom best to worst and look up down the list to find the value at risk. Bootstrapping does not need to specify distributional assumption, not bound to the problem of the normal distribution, the second thing it automatically pick up correlation if it exist between stocks. Bootstrapping is an improvement of basic historical simulation uses random sampling with replacement. Ten day VaR is very common in terms of market risk and stock prices(DuttaA, and Bhattacharya (2006)).

EMPIRICAL RESULTS Data
We downloaded daily data of close prices of TASI from meta-stock covering the period January 2004 to December 2017 containing 3270 observations. We convert the data into log returns (r). There are two advantages of log returns. The main reason why we use log returns because they are time additive (time consistent). The second desirable property is that if these log returns are normally distributed which is a common assumption under short periods then adding these variables produces end log return that is also normally distributed. One disadvantage of log return are not a linear function of the component or asset weights.

Descriptive Statistics
Mean The mean returns is close to zero as well as the median indicates that. There is a wide range of returns, skewed to the left as revealed by the negative sign which is an alarming a matter, while the positive sign of kurtosis suggests leptokurtic distribution means fat tail i.e. higher probability of observations in the tail, too many points cluster close the mean and too many points cluster away from the mean. According to this leptokurtic distribution, we have to be very careful about the negative region.

Unit Root Test
Augmented Dickey Fuller rejects the Null Hypothesis that r has a unit root since all absolute values of t-Statistic at the three critical values (-3.43216, -2.86223, -2.56718) are less than the calculated ADF t-statistic -51.6312 that indicates stationary return data. Results of GARCH(1,1) reveal that the constant term is significant at five percent, whereas news about the volatility from the previous period, and last period forecast variance are significant at one percent. The sum of (α + β = 0.134302) is less than one indicating convergence to the long-run mean ¢ £Ü § = ).))))+-. The value at risk percentages calculated by delta normal valuation method and empirical quantile passed the likelihood rate at 5% since the calculated LR is less than ù ).•®,+ which is 3.84. Hence, there is five percent chance to lose -1.7%, 1.57%, -4.3% or more of the index value Copyright © Society for Science and Education, United Kingdom on any given day respectively. The application of the empirical quantile, historical simulation, percentile and IGARCH gave value at risk percentages that passed the LR at 1% ù ).••,+ that is 6.63. VaR percentages of the delta normal and IGARCH are two extremes, which are different from the other three methods, lie closely to each other. Thus, the empirical quantile is the only method that passed LR test at both critical values.

Monte Carlo Simulation
Samples of five period has been forecasted using Monte Carlo simulation, and has been plotted in the following chart.
The first, third and fifth series were below 114.1 that is TASI end point indicating negative returns, whereas the second and the fourth series showed gains. Series 2 indicates the worst loss at the seventh period (-3%) followed by series 3 (-1.9%) and series 4 (-1.4%) at the sixth period.

Bootstrapping Simulation
Series 3 showed the worst loss, followed by series 1 and 5. The first series has the worst loss in the first four of the simulation period. There is apparent difference between simulation results of Monte Carlo and bootstrapping. Negative returns were dominant in bootstrapping

DISCUSSION
The basic historical simulation is the most popular approach to value at risk employed by companies and banks at least. It assumes mean daily return of zero, often done for a short period. Its advantages stems from its simplicity, flexibility, and free from the complexity of normality assumption. Its major drawback is its need for long time series that requests continuous updating and the jeopardy of picking extreme values. The application of this method to TASI log return introduced two different estimates in magnitude and acceptance by LR backtest. Its VaR 5% is almost half that of VaR 1% in addition to its failure to pass the LR test compared to the VaR 1%. The conditional value at risk is supposed to be an improvement to VaR. It revealed almost two fold VaR percentage compared to basic historical simulation however, it did not pass the LR test. The percentile approach follow suit to historical simulation. The empirical quartile shares the same advantages with the afro said methods; however, its drawbacks are its assumption of unchanging distribution of returns, and its inefficiency if p is close to zero. Nevertheless, it proved to be the only method that passed the backtest at both levels.
The delta normal approach assumes the prevalence of standard normal distribution. It is an alternative to variance/ covariance approach; nonetheless, it captures only linear risk exposure. Its estimate at five percent level is relatively different from other approaches despites its success in passing the LR test. Its VaR 1% did not make it. The application of GARCH family to value at risk produced unfavorable results in terms of backtesting passage specifically GARCH (1,1) and IGARCH (1,1). Another drawback of estimated IGARCH (1,1) its failure to remove the ARCH effect, which accomplished by IGARCH(2,1) Bootstrapping simulation is an improvement to basic historical simulation. It showed worst losses compared with Monte Carlo


“We do this because the market demands it”: alternative meat production and the speciesist logic

The past decades’ substantial growth in globalized meat consumption continues to shape the international political economy of food and agriculture. This political economy of meat composes a site of contention; in Brazil, where livestock production is particularly thriving, large agri-food corporations are being challenged by alternative food networks. This article analyzes experiential and experimental accounts of such an actor—a collectivized pork cooperative tied to Brazil’s Landless Movement—which seeks to navigate the political economy of meat. The ethnographic case study documents these livestock farmers’ ambiguity towards complying with the capitalist commodification process, required by the intensifying meat market. Moreover, undertaking an intersectional approach, the article theorizes how animal-into-food commodification in turn depends on the speciesist logic, a normative human/non-human divide that endorses the meat commodity. Hence the article demonstrates how alternative food networks at once navigate confines of capitalist commodification and the speciesist logic that impels the political economy of meat.

The political economy of meat
A most dynamic phenomenon has arisen in the international political economy of food and agriculture; globalized meat consumption is booming, the livestock sector is expanding. The global average of yearly consumed meat has grown substantially, from 23 kg/capita in 1961 to 43 kg/capita in 2013. 'Emerging economies' like China and Brazil (though not India) carry the strongest consumption increase. At the same time we see how Europe and especially the United States remain the fiercest meat consumers on the planet, while low meat consumption continues to define food habits in the world's 'least developed countries' (Rae and Nayga 2010;see also;Pica-Ciamarra and Otte 2011;FAOSTAT 2018). This observable trend typifies the international political economy of food and agriculture (Fine 1994;Koç et al. 2017), an intricate nexus of global trade relations comprising a most contentious feature-conflict between corporate and alternative arrangements for producing, distributing and consuming food (Friedmann and McNair 2008;McMichael 2008;Campbell 2009). With the globalized tendency of expanding meat consumption and production-and its entailed social conflicts-we here recognize a political economy of meat.
Scholarly research seems to mirror the defining, contentious feature of that political economy of meat. On the one hand, rural development scholars welcome the dramatic rise in global meat consumption as "the next food revolution" (Delgado et al. 1999). The key argument here is that livestock farming provides high net income (Kaufmann and Fitzhugh 2005;Nin et al. 2007), which means that smallscale farmers in 'developing countries' are now offered significant economic opportunities (Delgado et al. 2003;Hall et al. 2004). This "pathway from poverty" is particularly paved by large agri-food corporations that connect smallholders to previously unreachable global markets (Brown 2003;Waldron et al. 2003). On the other hand, critical scholars have disclosed how small-scale farmers, vertically integrated into agri-food corporations, have become alarmingly dependent (Heffernan 2004); unable to afford the required technical upgrading, livestock smallholders, marginalized 1 3 from other marketing options, often end up severely indebted (Khan and Bidabadi 2004;Millar and Photakoun 2008;Lundström 2011). To cope with these undesired social outcomes, scholars viewing livestock production as poverty alleviation here concede to cooperative solutions, predicting that "smallholder livestock farming in developing countries will be driven by collective action" (Narrod et al. 2010).
By acknowledging resistance from agrarian social movements, the dynamic consumption of food animals, defining the political economy of meat, therefore becomes an inherently political affair (Williams 1999;Neo and Emel 2017). The critical food studies field, emblematically focused on that precise vibrancy of politicized foodways, have come to document a variety of social struggles for food sovereignty, organized searches for political and economic autonomy across the food chain (Patel 2010;Ayres and Bosia 2011;Riches and Silvasti 2014). This line of research has, in order to capture the agency behind these heterodox political economies, increasingly come to study how alternative food networks (AFN's) bridge the consumption-production divide of globalized capitalism (Renting et al. 2003;Goodman et al. 2012). The AFN conceptualization aims to transcend narrow foci on re-localized foodways (Wald and Hill 2016), mere value-adding in alternative food production (Blumberg 2018) and contextually disembodied views on ethical consumption (Grasseni 2013). Hence the notion of alternative food networks carries decisive, political implications. Reflecting the contentious dynamic of the political economy of food and agriculture, the AFN conceptualization typifies prefigurative searches for autonomy, vis-à-vis hegemonic foodways (Lang and Heasman 2004;McMichael 2009;Wilson 2016). Aside from their ideological fuel (Sage 2003;Forssell and Lankoski 2015), alternative food networks usually emerge to cope with harsh, economic realities (Gordon and Chatterton 2004;Grasseni 2013;Rakopoulos 2014).
Given the conflictual dynamics of globalized meat production, it becomes particularly topical to study how an agrarian social movement-an alternative food network advocating food sovereignty-navigates the contentious political economy of meat. In this article, we will begin mapping that uneven topography by specifically exploring how capitalist commodification of animal-derived foods intersects with the logic of speciesism, the normative divide between human and non-human animals that underpins meat production. As suggested in the following section, an intersectional analysis through the social movement lens-focused on the ramifications of alternative meat production-arguably captures the elusive nature of the speciesist logic. Through an empirical case study, we will then see how that peculiar silence, the invisibility of speciesism, impregnates an alternative food network that otherwise engages in intersected struggles for autonomy and equality. In this regard the concluding discussion addresses how an advanced alternative food network-in our case Brazil's Landless Movement-relates the logic of speciesism to its applied, political intersectionality.

Political intersectionality
This article maps the political economy of meat by analyzing speciesism's location in intersectional resistance struggles, often conceptualized as political intersectionality, through an empirical case study of a collectivized pig farm, linked to a most iconic agrarian social movement, one of the strongest advocates for food sovereignty: Brazil's Landless Rural Workers' Movement (MST). As we will see in the empirical analysis, MST typifies an alternative food network seeking to enact political intersectionality, while at the same time navigating the political economy of meat.
The notion of political intersectionality, or intersectional resistance, stems from the feminist school that in the early 1990s began theorizing the interlocked workings of sexism and racism, under a capitalist political economy. Over the years, intersectional theory has not only been used to expose interlinked logics of domination, but also to comprehend how resistance is articulated against that multiplication of oppressions. Intersectionality scholars here distinguish between structural intersectionality, the wickerwork of interlinked power structures, and political intersectionality as the compound, many-faced resistance struggles against these entangled axis of power (Crenshaw 1991). The notion of political intersectionality thus aims to capture the "reshaping modes of resistance beyond allegedly universal, singleaxis approaches" (Cho et al. 2013, p. 800). In this vein, social movement scholars have accordingly come to document collective action that articulate-at the intersection of resistance-a variety of socio-political struggles (Okechukwu 2014;Davis 2016;Daum 2017). Hence the notion of political intersectionality aptly applies to agrarian movements that, in their cultivation of alternative foodways, consciously entwine politico-economic struggles for autonomy and equality.
But intersectional theory also offers an analytical lens for comprehending how the innate logic of speciesism operates through the political economy of meat. In the research field of critical animal studies, scholars have come to include speciesism in analyzes of interlocked workings of domination under capitalism (Nibert 2002;DeMello 2012). With the notion of intersectionality, critical animal studies have documented how the speciesist operative-that of social differentiation-intersects with the logic of racism (Svärd 2014;Monteiro et al. 2017;Olivier and Cordeiro-Rodrigues 2017), as well as sexism (Adams 2010;Rothgerber 2013;Allcorn and Ogletree 2018). Guided by the intersectional approach, the human-animal nexus has been exposed as a normalized, yet highly unequal, and exploitative, social relation (Cudworth 2014;Nocella et al. 2014;Wyckoff 2015). Accordingly, social movement scholars have documented how the Animal Liberation Movement actively seeks to link its critique of speciesism to parallel struggles against sexism, racism and capitalism (Johnston and Johnston 2017;von Essen and Allen 2017), a collective resistance that becomes notably enacted through conscious, dietary refusals to consume animal-derived food products (Hamilton 2016;DeLessio-Parson 2017;Glover 2017).
From this intersectional point of view, sexism is identified as particularly informative to the speciesist logic; human othering of non-human animals mirrors a relational setup akin to the objectifying logic of sexism (MacKinnon 2004;McWeeny 2014;Adams 2016). That objectifying, speciesist logic informs, I would argue, a most fundamental working of the political economy of meat: the transformation of cattle, pigs and chickens into food commodities (Torres 2007;Neo and Emel 2017). This process of commodification, the making of market commodities for value extraction, famously identified by Marx as a key function in the capitalist mode of production, is arguably a linchpin of the globalized political economy of meat. Food animals are commodified to generate profit, rather than food (Gunderson 2013), which in turn requires normalizing notions to establish non-human animals as precisely as property (Francione 2004), the most fundamental requirement for commodity exchange. Capitalist commodification of meat accordingly depends on an anthropocentric understanding of the human/non-human divide (Morton 2017), a psychosocial process by which we differentiate between, and then assign certain values to, the variety of non-human animals (Joy 2010;Cudworth 2011;Dowsett et al. 2018). By acknowledging how humans value, classify and conceptualize non-human flesh as edible food, a process indeed variegated and contextually embedded (Chiles and Fitzgerald 2018), we may very well diagnose speciesism-the hierarchical divide between human and non-human animals-as an innate logic that propels intensifying commodification of meat. The logic of speciesism arguably denotes, as John Sanbonmatsu (2011, p. 21) puts it, "a complex, dynamic, expansive system that is materially and ideologically imbricated with capitalism as such." At the same time, the speciesist logic seems to have this elusive character; it is rarely exposed, evaded even by social movements enacting political intersectionality. Carol Adams (2016, p. 24) has famously theorized how food animals become "an absent referent": when speaking about meat as food, the actual animal, this complex and unique individual being, is peculiarly unrecognized. In this vein, Melanie Joy (2010, p. 30) argues that the anthropocentric gaze-viewing animal flesh as edible food-depends on a "belief system in which eating certain animals is ethical and appropriate." Such an intricate belief system also informs, I would argue, the commodification of meat. At the most extreme stage of this animal-into-food transformation-in the modern slaughterhouse-workers tend to nurture a social distancing to cope with violent labor practices (Smith 2002;Dillard 2008;Baran et al. 2016). Quite similarly, Rhoda Wilkie (2010) documents how livestock farmers develop emotional detachment towards their food animals, a detachment exponential to the level of involvement in the meat commodification process. Livestock farmers constantly need to negotiate this "fine perceptual line", as Rhoda Wilkie (Wilkie 2010, p. 182) puts it, "of seeing animals as both economic commodities and sentient beings." And it is that precise balancing act we find at MST's collectivized pig farm in Southern Brazil.

Confines of alternative pork production
Brazil is a key player in the international political economy of meat. Besides having the second largest cattle herd in the world, it has recently emerged as a leading national producer of poultry and now also pig meat. Furthermore, as we can see in Table 1, Brazilian meats have become high-ranked, export commodities in the World Economy.
This incredibly fast-growing meat sector has been particularly intense in Southern Brazil (Florit and Sbardelati 2016). And precisely here, in this hotbed of the political economy of meat, we also find an articulate agrarian social movement that actively seeks to navigate that economic reality. Brazil's Landless Movement (Movimento dos Trabalhadores Rurais Sem Terra-MST) was forged in this agrarian context nearly four decades ago. It was actually the cattle ranches of Northern Rio Grande do Sul that were first targeted by the emergent landless movement, occupied by rural workers who firmly believed they, if settled, would increase the productive output with enhanced food quality (Medeiros 2012). Forty years later we find that very same movement, now including experienced small-scale farmers, organized in producer cooperatives to advance their food sovereignty. Quite tellingly, the contemporary organizational basis for Brazil's Landless Movement, at least in Southern Brazil, is now producer cooperatives rather than geographical MST-sites. As a key member of the global peasant movement Via Campesina, MST is not only a collaborate architect of the food sovereignty concept; Brazil's Landless Movement also typifies the image of a politically holistic, alternative food network. For MST participants, food sovereignty seems to align with their search for autonomy or, as a key MST coordinator puts it, the struggle of "communities of the people to construct their own destiny" (Itelvina Masioli, quoted in Wittman 2010, p. 34). This search for autonomy is, as I have argued elsewhere (Lundström 2017), quite formative for Brazil's Landless Movement. MST's search for autonomy refers not only to the state, or allied political parties, rural labor unions, and supportive factions of the Catholic Church; autonomy also signifies, especially for settled MSTparticipants, independence from agri-food corporations. Hence, MST-participants typically struggle for economic self-determination, to reap the fruits of their own labor, becoming their own bosses. In order to facilitate and propel mutual aid between small-scale farmers, they have set up a number of collective producer cooperatives, in which labor and revenues are equally divided among cooperative members (Fabrini 2003;Lazzaretti 2007;Thomaz 2015). As we shall see, these cooperatives are typically understood, by the members themselves, as micro-scale examples of alternative food production.

"But we cannot escape the workings of capitalism"
In 2012-2013 I had the opportunity to conduct ethnographic research in Southern Brazil, attending a variety of MSTactivities: everyday agricultural labor, collective meals, festivals, school lessons, youth and women conferences, settlement inaugurations, demonstrations and occupations. Along with this participant observation, I interviewed approximately 100 MST-participants, both individually and in focus group settings. My book The Making of Resistance: Brazil's Landless Movement and Narrative Enactment (Lundström 2017) contains a historiographic analysis extracted from these field notes, interviews, and from MST's internal newspaper between 1981 and 2013. This article, however, presents a different set of findings; it focuses on MST's experiential navigating of the political economy of meat in Southern Brazil.
Throughout my ethnographic field work, I soon found how MST-participants were referring to cooperativism as an anti-capitalist practice, consciously activated to restrain commodification of human labor. Interviewees depicted how the autonomous small-scale farmer-in control of production, manufacturing and marketing-targets the central social relation of capitalism: the exploitative wage labor. By organizing production through various forms of cooperation, MST participants described themselves, as do many small-scale farmers experimenting with cooperativism (Schneider and Niederle 2010), as being more autonomous than rural workers and tenant farmers. At the same time, interviewed MST participants also portrayed structural, economic confines to the promise of cooperativism. The procedure of vertical integration, especially applicable in the capital intensive soy sector, was reflected upon with caution. MST-farmers here described, on the one hand, how collective work increased their economic autonomy, although they, on the other hand, had no real option than to approach large-scale soy corporations to establish reliable distribution channels. When I specifically asked about this precarious situation, interviewed MST-farmers replied, with notable ideological frustration, that vertical integration with soybean corporations was a mere economic necessity; it meant reliable market access. The interviewees portrayed a no-escape situation, an economic reality that eventually required submission to, as they so often came to put it, "the capitalist logic." The very same confines-of collective attempts to elaborate alternative food production-were described by farmers trying to navigate the political economy of meat. In order to document these experiences more closely, I spent a good part of my field study at one of MST's pork producing cooperatives in Southern Brazil. This particular setting was selected due to its positive recognition within the Landless Movement; it is frequently depicted as an example of an organizational rigor that allows for both political radicalism and economic sustainability. To study how these politicized farmers navigate the political economy of meat, I recurrently visited this MST site, taking part in the daily work as a participant observer, conducting informal interviews as well as formalized, in-depth interviews with the cooperative leadership, along with, as we will see exemplified below, focus group interviews. The focus group method was chosen to specifically capture collective, political reflection-and its entailed silences (Wilkinson 2008). In my particular search for reflections on the speciesist logic, I combined moderated focus groups, semi-structured by a thematic interview guide (Morgan 1988;Krueger and Casey 2000), with informal group discussions that naturally came about through my temporary participation in the meat production. Through this ethnographic approach, I hence came in contact with nearly all members of the MST pork cooperative.
Fifteen years before I visited this MST-site, the farm land had been part of a huge ranch, owned by one family only. Landless rural workers, organized under the MST-banner, began occupying the ranch, claiming the land instead for plentiful families. After years of intense struggle, evictions and repercussions, state officials eventually legalized the claim of the Sem Terra; the land was expropriated and divided between the hundred families that now had become settled, small-scale farmers. In this process, some 30 families chose to merge their lands to facilitate cooperative production of grains and legumes. But on their collective land the MST settlers also encountered an abandoned pig pen, along with a minor slaughterhouse. The new-founded cooperative soon decided to incorporate pork into their collective, agricultural portfolio. They began to breed and slaughter pigs, initially on a small, subsistent level, but soon succeeded to scale up the stock and process facilities.
These MST-farmers now run a collectivized pork cooperative; labor and surplus are equally divided between the cooperative members. Participants meet on a daily basis at their collective lunch, and attend weekly meetings and festivities. The cooperative members typically rotate between the various work sectors: breeding and slaughter of pigs, grain and legumes production, collective child care, kitchen activities and cooperative administration. Surplus is distributed on a monthly basis, according to labored hours in any of these sectors. The cooperative organization orbits these same work sectors; discussions and decision-making are enacted in de-centralized, work-sector settings, independently electing coordinators that, on a 2-year basis, constitute the cooperative's rotating leadership. "We are not some corporation, with bosses and all; here we all take responsibility", one of the cooperative members explains.
Production of pork meat comprises a substantial part of the MST-cooperative. Around 1000 pigs are continuously held for breeding, and some 100 pigs are daily slaughtered (including a slaughter service offered to local pig holders). The cooperative's pork meat is finally cut and sold at the local butcher's shop, a distribution channel described by cooperative members as an intentional market choice. Since the large, corporate meat processors dominate metropolitan and international markets, the MST pork cooperative prefers this alternative foodway to guarantee product quality, but also to secure their economic autonomy. "You see", one of the coordinators explains, "otherwise we would be completely dependent on the meat corporations. They alone benefit from all their rules and standards. We've not been struggling for our piece of land, and creating this cooperative, only to become dependent on the big corporations." At the same time, this limited market access, disabling the cooperative's meat commodity to reach larger consumer groups, also restrains and thereby threatens the economic sustainability of the pork cooperative. Interviewees portray a conflictual situation in which alternative foodways operate alongside-albeit confined by-the political economy of meat. In other words, the politicized, collective and horizontally organized pork cooperative, opting for local distribution channels, clearly qualifies as an alternative food network that seeks to navigate the political economy of meat. And very much in line with the scholarly recognition that AFN's are not quite so neatly separated from-but rather asymmetrically competitive to-conventional foodways (Sonnino and Marsden 2006), these politicized smallholders of livestock also express an ideological frustration of submitting to the very "capitalist logic" which they so characteristically struggle against. In the following interview excerpt, from a focus group interview with five cooperative members (selfidentified as male and female, aged between 17 and 62), we discern that precise ambivalence: Marcela: Some things we're doing within capitalism. But our work is different. It's collective, it's not me alone. You see, capitalism has always reinforced the ego, it's about me, what I want. But we have been pushing the question of organic production, instead of monocultures. We have always emphasized 'never stop at just one area of production'. Because today, if our cooperative agrees, we'll never stop at the primary material, we'll proceed with manufacturing. We would never leave production, because the primary material means resistance, it means autonomy. This is one aspect of our organization here. Another is the social, which is important. We have our own childcare, where we all work, where we all have the opportunity to work. The youth remain here on the land with us. It's not that we, the parents, capitalize while the youth is left with nothing. We are distributing the surplus among us. We have everything for our existence right here with us. But we cannot escape the workings of capitalism, because of our pork production.

Natália:
Because the cooperative has the character of a corporation. It´s different in its logic, but the production, the logic of selling, is the same as within capitalism. You cannot escape this is if you want to survive. For people to survive, there is no way to escape. But our cooperative, for sure, has a distinct role in society.
Fernanda: It has this whole structure, a different organization, a mode of production that is already differentiated.
Bruno: Any piece of machinery, everything that you buy, it's all capitalism. Most of it, everything that is beautiful; it all goes to the big corporations.
Fernanda: And at the same time there is nothing you can do. You live in a system that is capitalistic. But you can have a different logic, another opinion. We have another way of life, but we cannot escape totally. You sell, you buy, what else can you do?
In this focus group discussion, participants collectively construe the producer cooperative as an alternative to capitalist social relations, albeit operating within its economic logic. The pork cooperative is portrayed as a prefigurative, alternative foodway, contrasted against-yet also confined by-the "workings of capitalism." On the one hand, these livestock smallholders speak of economic autonomy, how their cooperative carves out a space of self-determination, which allows them to work collectively and share their surplus equally. On the other hand, they also find it necessary to comply with a dominant market logic: they have to produce and distribute a commodity that is compatible with the political economy of meat, the very economic reality these MST-farmers find themselves located in. And this exact commodification process also entails, as we shall see, additional contention. The commodification process, at the heart of the political economy of meat, compels livestock smallholders to negotiate everyday, routinized transformations from animals into food, flesh into meat, and in our case, pigs into pork.

"Not much of a life, is it?"
MST's collectivized pork cooperative engrosses the breeding, raising and slaughter of pigs, each production step following the general stages of modern pork production. The female pigs are first inseminated, and as they give birth they are moved to a farrowing pen to rear their piglets. When these piglets turn 2 months of age they are separated from their caregiver and placed in a separate pen, in order to enable yet another insemination procedure. Between 4 and 6 months of age, the pigs are considered to be in a 'growfinish' stage, in which they are intensively fed for fattening. The MST-held pigs, like all domestic pigs across the global meat industry, are finally put to slaughter at the age of 6 or 7 months, bred and raised in complete confinement.
"Not much of a life, is it?", one of the breeding-workers suddenly breaks in, while explaining to me the stages of pork production. Still in her youth, this cooperative member, herself born on the settlement, explains that she will soon move out, study at the university. "But I will not be a veterinary", she exclaims, as if referring to an expectation from the cooperative, with its ever-growing need for just that type of on-site professionality. While perceiving, on an everyday basis, the pigs' complete life cycle, from breeding to slaughter, birth to death, she here expresses a subtle hesitation towards the pork commodification procedure. Yet the speciesist logic is not called upon to address hesitation about these confines; her critical thought lingers, and is soon lost in detailed, technical explanations about the stages of pork production.
The speciesist logic is in fact never brought into the open, exposed, let alone questioned, during my field work with this pork cooperative. And here we shall remember that Brazil's Landless Movement typically embraces what we must recognize as political intersectionality. Over its near 40 years as an articulate, social movement, MST has been engaged in variety of social and political struggles; it has expressed solidarity with indigenous and anti-racist struggles (Nugent 2002;Lundström 2017), as well as industrial unionism (Sandoval 2007). MST has participated in the broad, alter-globalization movement (Karriem and Benjamin 2016) and more recently in urban, radical-democracy mobilizations (Vanden 2014). Furthermore, and perhaps most notably, Brazil's Landless Movement has quite actively adopted an explicit, feminist agenda, seeking to restrain social tendencies of male domination (Silva 2004;Naase 2009). But MST's political context also contains a fast-growing Animal Liberation Movement (Levai 2013;Barboza 2017;Freire 2017), one that has been particularly active in southern Brazil (Carbornar de Souza 2016). However, disregarding the human-animal issue raised by this neighboring social movement, MST's political intersectionality yet evades the intricate question of non-human exploitation. During my 6 months of field study, the human-animal question was never even remotely addressed; at collective meals and farming practices, food animals remained what Carol Adams (2016, p. 24) aptly calls "an absent referent." This remarkable silence-MST not speaking about a parallel and most noticeable social movement-is also found at the nation-wide level; in Journal Sem Terra, the movement's internal newspaper and key vehicle to connect MST communities across Brazil (Bezerra 2011), the animal rights thematic is entirely ignored. From a computerized search in all Jornal Sem Terra issues published between 1981 and 2013 (a corpus comprising approximately 4.5 million words), direito dos animais (animal rights) is only mentioned once, while bem-estar animal/proteção animal (animal welfare), libertação animal (animal liberation), vegetarian* and vegan* are not mentioned at all.
Yet Brazil's Landless Movement still nurtures an immense critique against the corporate, large-scale meat industry, pointed out as a key driver behind intensified land competition. As the vast pork and poultry sectors require an ever-growing amount of arable land to produce animal feeds (Dickson-Hoyle and Reenberg 2009;Davis and D'Odorico 2015), Brazil's Landless Movement characteristically targets this precise tendency of corporate land grabbing (Lundström 2011). Yet MST's general response, as an alternative food network, has not been to question the innate logic that drives Brazil's land-consuming and rapidly expanding meat sector. To the contrary, as we see in the collectivized pork cooperative in Southern Brazil, settled MST-farmers instead try to find an alternative course for navigating the political economy of meat. Their approach has been to elaborate a collectivised economy, with equal surplus distribution, circulating divisions of labour, de-centralized decision making, and representation through a rotating, female-inclusive leadership. But as MST-participants submit to, as they put it, "the capitalist logic"-through their pork production-they also silently submit to, I would argue, the speciesist logic, the innate idea that legitimates and drives commodification of food animals.

"We do this because the market demands it"
The speciesist logic is a delicate matter; is seems never fully accepted, but instead recurrently upheld-precisely by dodging hesitations about the violent nature of meat production. Even in direct animal presence-through the process of meat commodification-individual, non-human animals become invisible, yet peculiarly present as "an absent referent." MST's collectivized slaughterhouse in Southern Brazil typify that elusive submission to the speciesist logic.
During my field study I noticed how the slaughterhouse workers shifted between doing their routinized killings in complete silence, and joking brutally about their labor. "Wanna try the knife?", one of them asked me, chuckling; "You afraid of blood?" Another worker was clearly drunk. This particular day he was responsible for luring or forcing each pig out of the pen, into a narrow hallway leading up to a hatch, behind which the pigs were to be electrically stunned, hanged upside down, and then have their throats cut. "Yes, of course they know what's going on", he told me. "They see the other pigs disappear, one by one, and they smell the blood." And then quite swiftly, clearly troubled by my questioning, he continued his work, now with noticeable stronger affection, pushing and beating the next pig lined up for slaughter.
Inside the slaughterhouse, the floor, walls and ceilingand the workers themselves, all dressed in white clothes, rubber boots and aprons-are painted in blood. The pigs, although partly stunned, kick and shake after having their throats cut, making the blood spurt all over. Drained on their blood, the pigs' bodies are put into a rumbling machine, filled with hot water for scalding and then dehairing the bodies before their primary cuts. I see how some of the pigs seem to be yet alive in this procedure, still kicking when placed in the dehairing machine. The workers take no notice when this happens, which is frequently; they continue their work, in silence. "We do this because the market demands it", one of the workers explains, as he notices my astonished, wide-eyed observation. Although his explanation most probably refers to the rigorous procedure undertaken to guarantee a certain product standard, which the MST-workers view as a market-dictated confinement, it also accentuates elusive submission to the speciesist logic, upheld and fortified though the intensified process of meat commodification. To survive in the political economy of meat, as a small-scale, alternative meat producer, the MST cooperative must comply with standardized market demands. And to do that, they need to submit to the speciesist logic, the human-animal divide so conspicuously manifested in the modern slaughterhouse.
Then, in an assembly line, the MST slaughterhouse workers enact a professional, routinized array of work tasks. Everyone knows exactly what to do; the scalded pigs are now de-capitated, emptied from internal organs, cut in half with a splitting saw, and then washed and finally refrigerated. The working environment is sutured with noise, especially from the loud splitting saw, but also from live pigs that scream, throughout their very last act of defiance. At the coffee break, taken in the colorful garden located outside the slaughterhouse, the collective mood instantly changes. The conversation frequently touches upon, as so often in MSTsettings, socio-political themes. How could the cooperative relieve economic poverty in their neighboring, rural surroundings? How should they advance feminist issues at the local school? The discussion is vivid, reflective and openended; it appears as if anyone political question could be brought to the table. Yet this exciting discussion, engaged while sipping coffee in the gazing sun-all dressed in white, blood-stained robes-enclose a most absent referent at our work break, this temporary pause in commodifying pigs into pork meat.

Concluding remarks
While I approached Brazil's Landless Movement, with sincere respect and eagerness to learn about the most compound making of resistance, I soon came to ask myself how such a reflexive milieu could overlook a pressing issue raised by social movements in MST's immediate surroundings. How could speciesism be so profoundly ignored in MST's intersectional, political thought?
I believe the answer is partway found in recognizing speciesism's function in the political economy of meat. As we have seen in our case study from Southern Brazil, settled MST-farmers, well vested in the pork producing sector, have little choice than to submit to the "the capitalist logic", being unable to "escape the workings of capitalism." They describe blunt, economic relations that their pork cooperative simply have to cope with; they depict economic confines to alternative meat production. And what's more, these confines of "the capitalist logic" also entail, as we have seen, most embodied confines prompted by the speciesist logic. Because at the heart of the capitalist, political economy of meat resides the commodification process, this intricate animal-into-food-transformation, informed by the speciesist logic. Since speciesism adds another layer to the confines of the political economy of meat, alternative food networks, in our case a collectivized pork cooperative tied to Brazil's Landless Movement, inescapably need to navigate multiple confines.
In this delicate balancing act, livestock smallholders seem to evade full exposition of the speciesist logic, while at once partaking in its silent approval. The slaughterhouse workers of MST's pork cooperative nurtured an ambiguous relationship towards their animals; while executing confinement and slaughter, some workers also expressed what could be read as hesitation, a silent doubt, about their everyday work tasks. It so appears that speciesism has this intangible character, always eluding full exposition, which makes it less visible, even for radical social movements with intersectional sensibilities. Although alternative food networks, typified by Brazil's Landless Movement, advance the meaning of political intersectionality, their navigating of the political economy of meat necessarily submits, albeit quietly, to the speciesist logic that drives capitalist commodification of food animals. On a critical note, then, one must consider the ramifications of an 'alternative' meat production that allows (non-human) labor exploitation to continue under the socialist banner (Hudson 2011). Given meat production's dependence on concentrated, "institutionalized violence" (Cudworth 2015), with documented asocial effects on communities that hold slaughterhouses (Fitzgerald et al. 2009), we clearly recognize boundaries to alternative food networks' political intersectionality, boundaries following from the amalgam confines of capitalist meat commodification and its innate, speciesist logic.
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Indonesian sustainable food-availability policy assessment using system dynamics: A solution for complexities

Abstract In spite of being a large paddy and maize producer for the last decade, Indonesian farming has been suffering from high population, land conversion and low agricultural employment, which have been more daunting than environmental issues in achieving sustainability. They also arise in complex interrelationships, which create loops and cannot be managed individually. This paper presents a system dynamic simulation of Indonesian sustainable food availability assessment to support policy adjustment in achieving sustainability. A system dynamic model has been built to represent Indonesian sustainable food availability as a holistic system which contains economic, environment and social aspects elements and their interrelationships. Simulation has been also done in three stages for a seven policy-scenario and six combination policy-scenario options to analyse how they affect food availability and farming emissions. The principal contribution is a systemic and quantified assessment of sustainable Indonesian food availability using system dynamic for policy adjustment support. ISFA ratio and Food availability scores were set as the goal performance. As the result, a combination of scenario 5 and 4 has been the selected scenario for ISFA ratio and food availability scores 14.61 kg/capita/GgCO2 Eq and 159.6 kg/capita as the most sustainable policy.

Indonesian sustainable food-availability policy assessment using system dynamics: A solution for complexities ABOUT THE AUTHORS Weny Findiastuti is a doctor candidate in Department of Industrial Engineering at Institut Teknologi Sepuluh Nopember, Indonesia and lecturer in Departement of Industrial Engineering at Universitas Trunojoyo, Indonesia. Her research interest is in macro-level quality and sustainability include eco-efficiency and food security. Currently she is working on eco-efficiency of sustainable food security. Moses Laksono Singgih is professor of productivity and quality in Department of Industrial Engineering at Institut Teknologi Sepuluh Nopember. He has Master at Industrial Engineering, Institut Teknologi Bandung, Indonesia and PhD from Industrial Economics, University of Queensland, Australia. His current research interest is in regional productivity.
Maria Anityasari is researcher at the same institute. Her research interest includes in socio engineering, green building and sustainability practices, management of change, system design and risk management. She has Master and PhD from UNSW Sydney, Australia. Her current research focuses on sustainable manufacturing and sustainable oriented communities.

PUBLIC INTEREST STATEMENT
Being a large cereal producer doesn't mean that Indonesia is released from complex food availability problem, especially in environment and socioeconomic complexities. As a national level problem, policy adjustment in sustainable food availability achievement is often more influenced by socioeconomic aspect than environmental one. This study proposes system dynamics methodology to accomplish policy assessment within complexities in socioeconomic and environmental aspects for supporting policy adjustments. These aspects are exposed for their relationships to configure causal effect relations and quantified in system dynamics simulation to evaluate about thirteen scenario policies at year 2025 for their sustainability performance.

Introduction
Indonesia is an agricultural country and has been one of the largest paddy and maize producers among the South-East countries for the last 10 years. In 2016, Indonesia produced 79,171,920 tons of paddy and 24,839,830 tons of maize with a production growth average of 1.87% for paddy and 7.13% for maize (Secretariat, 2016). As a commitment to Agenda 21's sustainable development, the Indonesian Ministry of Agriculture has set sustainable food availability as one of the agriculture policies to produce food without harming the environment. Nevertheless, policy adjustment in achieving Indonesian sustainable food availability has brought new challenges. Despite high cereal production over the recent decades, Indonesian farming is also suffering from high population growth, shrinking sown area, land conversions, low agriculture investment and decreasing agricultural employment (WFP, Pangan, & Pertanian, 2015). As a developing country, these socioeconomic aspects have been more threatening than the environmental impact itself (Atkinson, 1995;Deng, Huang, Rozelle, & Uchida, 2006, 2008Iftikhar & Mahmood, 2017;Kc, Fraser, Kc, & Fraser, 2017;Thibert & Badami, 2011). Furthermore, at a national level problem, every environmental policy always brings a socioeconomic impact that cannot be ignored. These aspects also create unpredictable and related complex relationships that create loops and feedbacks (Hall & Carter, 2014;Koning et al., 2008;Wei, Yang, Song, Abbaspour, & Xu, 2012).
To answer those challenges, this paper seeks to assist the policy adjustments by conducting a system dynamics simulation modelling of Indonesian Sustainable Food Availability (ISFA). The simulation model was designed and run to answer "what-if" questions for Indonesian government policy scenario options in achieving sustainable food availability. Conflict of economic, social and environmental aspects was carried out by observing the problem as a holistic system and exposing the relationships between aspects. Elements of those aspects were identified in accordance with certain Indonesian food availability problems and then related to each other by conducting a system dynamic model for sustainable food availability of Indonesia. Relationships among those elements were arranged and depicted in a Causal Loop Diagram (CLD), then simulated through Stock-flow Diagram (SFD). The goal was set as achieving minimum requirement of food availability score and high ratio of food availability to its farming emission, namely the Indonesian Sustainable Food Availability (ISFA) ratio.
There are seven scenarios determined to represent seven policies assessed for high performance of sustainable food availability, stated as ISFA ratio (Equation 1) and food availability (Equation 2). In an assumption that a combination of two policies will be implemented by the government, simulations were done in three stages to assess seven single scenarios and combination of two scenarios. "What-if" questions are answered by the simulation results that predict food availability score and ISFA ratio in year 2025 for every single scenario and combination of two scenarios.
Food availability is one of the food security dimensions and refers to supply of food security and is determined by the level of food production, stock level and net trades (FAO, 2008). It is a critical dimension of food security for its role as assessment of the food supply determinant. Accordingly, sustainable food availability is about providing food from farming activities without harming the environment. The term food availability is preferred over food production or food farming to emphasise the population effect on food production.
Complexities arising between food, environment and socioeconomic issues in the farming activities have been realised and it is discussed that it requires a systemic approach to deal with the complex and non-linear relationships between them (Antunes et al., 2017;Banos-González, Martínez-Fernández, & Esteve-Selma, 2015;Griggs et al., 2013;Hubeau et al., 2017). Kc et al. (2017) adjusted strategies for food production by considering socioeconomic aspects. A framework for a holistic sustainable irrigated agricultural system was also conducted by Antunes et al. (2017), with consideration of socioeconomic and environment aspects; however, the complexities of relationships between those aspects are not considered in those studies and an independent assumption on factors is still applied. A partial consideration and complex relations abandonment on these aspects may create unimplemented policies due to imbalanced socioeconomic conditions, sustainability deceleration and incompatibility with the real problem. Table 1 lists several methods and indicator aspects used in food security policy assessments. Some of the research employed only environment indicators for sustainability (Khanal & Mishra, 2017;Larson, 2013;Li & Zhang, 2017;Maltsoglou et al., 2015;Rutten et al., 2016), while others have tended to evaluate the sustainability by combining environment, economic and social indicators. However, the research has not addressed the relationships between indicators that could support policy adjustment.
System dynamics (SD) handles non-linear and complex relationships wherein mathematical modelling has limited competence (Sterman, 2004). It also occupies qualitative and quantitative relationships with feedback and time-dependent behaviour through simulation (Coyle, 1996;Sterman, 2004). Qualitative evaluation is managed through CLD, while the quantitative is handled through SFD simulation. Being a national level problem, parameter uncertainties also often occur in fertiliser consumption, agricultural investment, cereal consumption per capita and population growth. This condition is controlled by growth and shrinking rates from statistical quantification of real system data with 90% level of confidence. System's fluctuations are better accomplished by system dynamics simulation in sensitivity analysis that occupies how the system behaves (Sterman, 2004).
In similar studies on food availability model, SD was performed in increasing crop productivity policies with environmental impact on land degradation by Bala and Hossain (2010) in Bangladesh. Being focus on sustainable crop production and aquaculture, the study modelled and simulated food availability policies in concern of ecological impact of forestry and shrimp production. Bala, Alias, Arshad, Noh, and Hadi (2014) modelled food security in Malaysia. Despite detailed analysis and simulated environmental impacts, these models did not include socioeconomic aspects such as growth of investment, employment, housings, infrastructures and industrial areas. The exclusion of these certain social aspects may cause missing information of certain unaddressed social aspects that have an essential impact on Indonesian sustainable food availability. By involving social aspects, a study by Pradhito, Chou, Dewabharata, and Wirjodirdjo (2013) modelled Indonesian ricefulfillment in the context of the supply chain based on rice supply-demand balancing performance. Tsolakis and Srai (2017) also constructed system dynamics model for smaller farmer role in food security. However, these researches have not yet considered the environmental aspects which control sustainability.
By means of the consideration socioeconomic and environment aspects in a holistic view of a system, this paper contributes a system dynamics simulation model of Indonesian sustainable food availability to support policies selection to achieve sustainability. For general purposes, the model proposed may well represent the common sustainability problems in developing countries where land conversions, employment and investment are always the principal attributes, but are frequently overlooked.
The paper was prepared based on two research questions: • What complexities occur in Indonesian sustainable food availability?
• How can policy decision-making be supported without eliminating those complexities?
The first research question is answered by conducting a Causal Loop Diagram (CLD) in Section 3. CLD is a step-in system dynamic that exposes the qualitative relationships among elements of the aspects and identifies feedback loops in the relationships, which indicate the complexities. The second question is answered by configuring and running the simulation model for four determined scenarios that have been selected based on the Indonesian Ministry of Agriculture for 2015-2019 in Pertanian (2015). For the simulation, CLD has been transformed into a Stock-flow Diagram (SFD) to quantify the relationships. SFD is the simulation model to calculate the food availability score and farming emission score.

Indonesian sustainable food availability (ISFA) complexities
Instead of high paddy and maize producer, Indonesian farming activity is facing complex problems which frequently associated with socioeconomic aspect, such as uncontrolled land conversions (LANDac, 2016; Van der Laan, Wicke, Verweij, & Faaij, 2017), investment (Khanal & Regmi, 2018) and agricultural employment (Flor et al., 2015;Ma'ruf & Safruddin, 2017). It is also stated that regional developments, the government has built the Trans-Java Toll road that has already reduce the productive sown area and will continue to grow along with the growth of urban areas around the toll exits (Sayaka & Tarigan, 2011). In environment aspect, Indonesia is suffering for excessive fertiliser use, CO 2 emission, climate change and water management in the last decade that threat sown area and its productivity (Caruso, Petrarca, & Ricciuti, 2016;Gribaldi, Nurlaili, & Rahim, 2016;Khanal & Mishra, 2017;Kirono et al., 2016;Surahman, Soni, & Shivakoti, 2018).
Those socioeconomic and environmental problems relate complexly. While government inhibits the road infrastructure expansion in order to expand the sown area other economic sector problems arise along with road infrastructure slowing down growth. Limited infrastructure growth also decreases cereal production by limited access and high price of fertiliser. Conversely, when road infrastructures accelerate fertiliser consumption, the sown area could shrink through degrading soil. This situation shows that government should consider all aspects as an integrated system. A separated solution in one or two aspects would disrupt others.

System modelling of Indonesian sustainable food availability (ISFA)
The ISFA system model was developed based on the current problem faced. It consists of social, economic and environment aspects related to the problem explained in Section 2. Table 2 lists elements of the ISFA system with their definition and unit of measurement. Here, "cereal" represents the term "food", which consists of paddy and maize. These elements relate to each other complexly and configure the ISFA system. Element relations in the ISFA system are illustrated qualitatively as a CLD in Figure 1 and are quantitatively evaluated in the simulation model of the SFD in Figure 2.   Figure 1 illustrates CLD of ISFA system. Arrows explain the relationships of elements in the system. An arrow head is an "effect" of a "cause" in an arrow tail. A positive relation, shown by (+) signs and solid-lines, means conditions in a "cause" node would affect the same conditions of an "effect" node. A negative relation, shown by (−) signs and dotted lines, means conditions in a "cause" node would conversely affect conditions of an "effect" node. For example, an increase in cereal available causes an increase of food availability (positive relation), while an increase in population causes a decrease in food availability (negative relation). Complexities are also shown in this diagram by the occurrence of feedback loops, for example, sown area loop below that describes complexity in land conversion: Sown area →(+) Cereal production →(+) Cereal availability →(+) Cereal consumption per cap →(+) Cereal consumption →(+) Population →(+) Road infrastructure or Housing or Industrial area →(−) Sown area The loop can be explained as: Beginning from the sown area, when the sown area expands, it increases cereal production and cereal availability. These increases cause an increase of cereal consumption per capita because, when cereal production is high, people tend to increase their consumption. The increased consumption per capita will increase total cereal consumption. When cereal consumption is high, it tends to increase the population requirement of nutrition and health. However, high consumption stimulates the need of road infrastructure, housing and industrial area, which shrinks the sown area. The loop begins in a sequence after the sown area shrinks and decreases cereal production. Figure 2 illustrates SFD of ISFA system. Elements of the system became variables at SFD (see Table  2) and were then simulated for baseline condition and seven policy-scenarios to achieve the goal which are set as the ratio of cereal availability and farming emission (ISFA ratio) and food availability (FA), as written in Equations (1)  Equation (1) is a ratio of sustainability (kg/capita/GgCO 2 Eq) that is derived from the general ecoefficiency ratio developed by European Environment Agency (EEA) as in Equation (3), where more welfare states the human need completion in a broad sense and less nature refers to less environmental impact (WBCSD, 2000). Derived from ISFA ratio in Equation (1), food availability (FA) is formulated as net cereal available or cereal production (CP) divided by population (Desimone, Popoff, & WBCSD, 1997), as in Equation 2 in year (t) (WFP et al., 2015). (3), while farming emission (FE) states the lower nature by less emission of the farming activity. Minimum score of FA which is set as 109.5 kg/capita/year also required for the policies. This score is derived from the Indonesian normative consumption rate per day, 300 g/capita/day (300 g × 365 days × 1000 −3 kg/capita/year) (WFP et al., 2015).

Food availability (FA) is a completion of human need in Equation
Eco-efficiency ratio = More welfare Less nature Farming emission (FE) score is formulated in Equation (4) that consists of land-used emission (LE) and fertiliser emission (FerE). Land-used emission (LE) in Equation (5) refers to CH 4 emission caused by farming activity in CO 2 equivalent unit determined for specific land used, which the cropland category is according to IPCC (Intergovernmental Panel on Climate Change). It is quantified by sown area (SA) and duration of cereal growing days (GD) variables. Fertiliser emission (FerE) refers to CO 2 emission from urea fertiliser consumption (FerC) for certain sown area, which is formulated in Equation (5) Figure 2 refer to stocks which are accumulated flows to that level and which are formulated in Equation (7) (Bala, 1999). The accumulations begin from the initial value of data in 2004, as listed in Table 3. These variables accumulate as rising flows that appear as straight arrows. They represent information flows and the flow is controlled by a flow rate with circular arrow. Straight arrows that flow into stocks are called inflows, and outflows flow out of stocks.

Rectangular boxes in
The source and sink of the structure are represented as a cloud that denotes infinity and marks the boundary of the model (Bala, 1999). Referring to Table 2    Ir shrinking (t) = Ir (t) × Ir shrink (t) Equation 8 explains that stock of irrigated sown area (Ir) grows and accumulates along the period (t) according to inflow of irrigated area growing. It also shrinks according to outflow of irrigated area shrinking. In baseline condition simulation, it is set that irrigated sown area begins with initial value of 4,867,165 hectare (Table 3) grows at 0 ha/year (not growing) and shrinks at 2% shrink rate per year, as in Equation (9). Similarly, Equation (10) explains stock of population (Pop) that grows and accumulates according to inflow of population growing along the period (t) at 1.3% of population growth rate.

Validity and sensitivity test
Validity and sensitivity tests are series of tests to assure that the built model is applicable in representing the real world. The validity of the ISFA system simulation model has been performed for the simulation model. The test has been done by comparing the simulation result with historical data shown in Table 4, using the two-independent sample Mann Witney U test in Minitab 16 software. Using null hypothesis (Ho): P1 = P2, significance level (α): 0.05, the test resulted in p-value: 0.7642. For p-value > α, it failed to reject Ho, by which it can be concluded that the null hypothesis of simulation result and historical data are similar. This indicates that the simulation model is valid or successfully represents the real world.
Sensitivity analysis is done to ascertain how sensitive the model is to the changes of one or more parameters. When the model behaves as the real world does under extreme conditions, it can be concluded that it is applicable in representing the real world (Breierova & Choudhari, 2001). Sensitivity analysis in model behaviour also addresses the parameters uncertainty test in the real system (Sterman, 2004). Extreme conditions for food availability have been represented in three extreme population values: 1 × 10 8 , 3 × 10 8 and 5 × 10 8 . The result of sensitivity analysis is described in Figure 3.   lower food availability, as the real world behaves. It can be concluded that the model is sufficient to represent the real world and is robust to any extreme conditions.

Simulation-running and result discussion
Simulation answers "what if" questions related to strategy adjustment. This study simulated the system dynamics model of Indonesian sustainable food availability to evaluate baseline condition and seven determined scenarios based on best performance of sustainable food availability for the year period of 2004-2025. The model was simulated in three simulation-stages for highest ISFA ratio and highest food availability score, with minimum food availability score requirement of 109.5 kg/ capita/year (Equations 1 and 2). The higher score of ISFA ratio is the better sustainable performance of the system.
Baseline condition is existing condition at year period 2004-2013 and simulated for year period 2004-2025 (22 year). In this baseline condition, food availability is influenced by: • Shrinking sown area was a result of land conversion into road infrastructure (1%), housing (5%), industrial area (2%) and fertiliser degrading factor (10%).
• Shrinking of irrigated sown area for 2% per year, and • Fertiliser consumption increased 5% per year.
• Increasing agricultural investment for 5% per year.
• Decreasing agricultural employment for 0.8% per year.
The scenario condition is simulated baseline condition with some policy-treatments at year 2015-2019, as stated by the Indonesian Ministry of Agriculture. Scenarios were developed to represent these policies. As it is assumed that the policy implemented is a combination of two policies, simulations were done for seven single scenarios and a combined scenario. The best performance of single scenario was combined with other scenarios for the next stage of simulation. Those three stages are:

Scenarios for ISFA system simulation
Scenarios were developed to represent Indonesian government policies for sustainable food availability achievement in year 2015-2019. The scenarios developed are: • Scenario 1 is increasing sown area (SA) variable during 2015-2019 for 40,000; 130,000; 250,000; 280,000 and 300,000 hectares.
According to the Indonesian Ministry of Agriculture during 2015-2019 (Panudju et al., 2013), it was expected that the scenario would increase cereal production and food availability.
It refers to the urea fertiliser subsidising policy to increase cereal productivity This scenario represents the intensification that is accomplished by giving subsidised fertiliser to farmers (Julianto, 2017).
This scenario refers to the subsidising bio-fertiliser policy that represents the government commitment to prevent land degradation.
This scenario refers to the agricultural investment policy 2015-2019 by reducing loan interest for agricultural investment from 4.5 to 4.25% per year (Setiawan, 2017). It is expected that growth of agricultural investment will grow 11%.
• Scenario 5 is expanding the irrigated sown area (Ir) variable 500,000 Ha/year during 2015-2019 by irrigation facilities improvement.
It refers to the irrigation canals and pumps regeneration policy to expand the irrigated area that has been decreasing at 2% per year at baseline condition (Pertanian, 2015).
• Scenario 6 is decreasing the potential factor shrinking parameter from housing, industrial area and road infrastructure area for 50% of baseline condition value during period 2015-2019.
This scenario refers to controlling the land conversions policy by performing the land protection constitution accurately.
• Scenario 7 is decreasing population growth at 2019 from 1.3% at baseline condition to 1.2%.
This scenario refers to the controlling population policy that undertaken by the Indonesian government since the 90s and which has resulted in 0.1% declining population growth for the past ten years (Panudju et al., 2013). In 2015, the government published a health guarantee card for parents and three children and population-growth is expected to decline faster, to 0.1% by 2019.

Stage-one simulation result
At this stage, simulation was done for baseline condition. Figure 4 describes the result in food availability, farming emission and ISFA ratio. It is seen that ISFA ratio (line-3/green) has a linear-increasing trend, while farming emission (line 2/pink) and food availability (line-1/red) have exponential-declining trends.
Concerning ISFA ratio, decreasing farming emission below the food availability margin could cause the increasing trend of the ISFA ratio. From the increasing ISFA ratio scores and decreasing farming emission trends, it can be inferred that the system is in a desirable trend for environmental performance. However, despite these desirable trends, an exponential-declining trend for food availability endangers the food security. For year 2025, the food availability score is 99.93 kg/capita, this doesn't meet the minimum requirement of a food availability score of 109.5 kg/capita. Farming emission is 10.92 GgCO 2 Eq and ISFA ratio is 9.15 kg/capita/GgCO 2 Eq.
Analysing this result without further evaluation in a systemic view may cause misperception of these favourable environmental performance scores. These increasing ISFA ratio and declining food availability scores should be evaluated through analysing cause-effect relationships in the Causal Loop Diagram (CLD) in Figure 1. There, it is illustrated that declining food availability scores are affected by declining cereal production as a cause of shrinking the sown area.

Stage-two simulation result
Stage-two simulations were done for all seven single scenarios. The results are illustrated at Figure  5. Baseline condition is in the blue area while seven scenario-conditions are in coloured lines. It is seen that, at year period 2004-2014, the ISFA ratio of all scenarios have the same score and start to vary at year 2015 for the scenarios starting to be implemented. Table 5 lists the scores of ISFA ratio and food availability at the end of the period, 2025.
Figure 5(a) shows that the scenarios at the end of period, scenarios 2, 4, 5, 6 and 7 reach a high ISFA ratio beyond the baseline condition. The highest ISFA ratio is reached by scenario 2, which is increasing fertiliser consumption; however, this scenario doesn't meet the minimum score of food availability (109.5 kg/capita/year) as listed in Table 5. Despite a high achievement food availability at year 2017 for 142.07 kg/capita (see Figure 5(b)), at the next year it decreases until the end of the period to 103.89 kg/capita (see Table 5). The degrading condition can be explained by the CLD of ISFA system in Figure 1 in that fertiliser consumption is negatively related to sown area and then positively related to farming emission, which means higher fertiliser consumption decreases sown area. A high ISFA ratio is caused by decreasing farming emission, since there is decreasing sown area. Subsidising urea fertiliser for scenario 2 tends to raise excessive fertiliser consumption. According to Ye andVan Ranst (2009), Dong et al. (2012) and Delang (2017), excessive urea fertiliser consumption will degrade soil quality by 50-100% in four years. This explains degradation of sown area at year 2018 and the rest of the period. This condition means that scenario 2 is unsustainable.
An unsustainable result also occurs at scenario 1 and scenario 3, which fall under the baseline condition. CLD at Figure 1 explains that low ISFA ratio for scenario 1 is for increasing land-used emission, since sown area is increasing. Despite a sustainable policy by substituting chemical fertiliser by bio-fertiliser, decreasing ISFA ratio and food availability below baseline condition at the end-period makes scenario 3 an undesirable choice. However, Ye andVan Ranst (2009), Dong et al. (2012) and many other researchers have stated that bio-fertiliser is a long-term policy and will reach high performance after more than ten years, but that is out of the simulation period.
In 2025, highest food availability score is reached by scenario 5 at 152.62 kg/capita (see Table 5) and meets the minimum score of 109.5 kg/capita/year. Considering these results, the selected scenario for first stage simulation is scenario 5 (expanding irrigated sown area). For the next stage simulation, combinations of scenario 5 with other scenarios were simulated for best performance of combined scenarios according to food availability and ISFA ratio.

Stage-three simulation result
Stage three simulated combinations of scenario 5 (expanding irrigated sown area) with other scenarios. The combination scenarios are:  (1) Scenario 5-1, a combination of scenarios 5 and 1, which are expanding irrigated sown area (Ir) and expanding sown area (SA) variables, respectively.
(5) Scenario 5-6, a combination of scenarios 5 and 6, which are expanding irrigated sown area (Ir) and decreasing potential factor shrinking parameter from housing, industrial area and road infrastructure area, respectively.
(6) Scenario 5-7, a combination of scenarios 5 and 7, which are expanding irrigated sown area (Ir) and decreasing population growth at 2019 from 1.3% at baseline condition into 1.2%, respectively.
Simulation results in ISFA ratio and food availability are described at Figure 6 and Table 6. It is seen in Figure 6(a) that the ISFA ratios of all combined scenarios are higher than baseline condition except for scenario 5-3. At year 2025, the highest ISFA ratio and food availability is scenario 5-2 for 29.13 kg/capita/GgCO 2 Eq and 165.36 kg/capita above the minimum food availability requirement 109.5 kg/capita (see Figure 6 and Table 6). However, as explained in the previous section, this combination is not desirable for unsustainable scenario 2. Figure 6(b) and Table 6 also illustrated that decreasing trend in food availability of scenario 5-2 will most likely continue after year 2025 and tends to endanger overall food security condition.
Second place for ISFA ratio and food availability is combined scenario 5-4 for 14.61 kg/capita/ GgCO 2 Eq and 159.6 kg/capita, as listed at Table 6. It is also shown in Figure 6(b) that scenario 5-4 has increasing food availability trend until 2025 and will most likely continue after 2025. CLD at Figure 1 also explains that increasing agricultural investment will increase agricultural employment. This social-aspect effect affords benefit of unemployment decreasing to the Indonesian government as a developing country.  These conditions describe that scenario 5-4 is more preferable for sustainable food availability. Consequently, it is decided that the best selected scenario for sustainable food availability is scenario 5-4, a combination of expanding irrigated sown area by irrigation facilities improvement and increasing agricultural investment by lowering loan interest for agricultural investment.
Selecting scenario 5-4 out of scenario 5-2 also causes ISFA ratio and food availability trade-off between scenario 5-2 and 5-4. Higher ISFA ratio and food availability scores than scenario 5-4 are traded by lower decreasing trend of food availability in 2020-2025. Table 6 list simulation results in food availability at period 2015-2025 where scenarios 5-2 and 5-4 are implemented at 2015-2019.
Year 2020, when scenarios are not implemented, trend of food availability decreases as illustrated in Figure 6(b) and quantified as -0.01045 and -0.00201 for scenario 5-2 and 5-4 (see Table 7). Lower decreasing trend makes scenario 5-2 is assumed to endanger broader food security system.

Conclusions
This paper aims to assist policy adjustment by conducting a system dynamics simulation model for Indonesian Sustainable Food Availability (ISFA). System dynamics methodology is used to handle complexities that have arisen as ISFA problems since there are complex relationships between socioeconomic and environment aspects. Although complexities in food availability and food security have been realised and the need of a holistic system approach has been discussed, most previous research assumed economic, social and environment aspects as independent factors. This independent factor assumption or partial consideration and the complex relations abandonment on these aspects may create unimplemented policies due to imbalanced socioeconomic conditions, sustainability deceleration and incompatibility with the real problem.
Food availability was measured legally and evaluated for the sustainability in Food security and vulnerability atlas of Indonesia 2015 by WFP et al. (2015). However, the evaluation is completed without indicators relationships consideration, complexities are simplified by assuming indicators as independent variables, and sustainability assessments are completed through ecological footprint that does not occupy related farming process. Without indicators relationships exploration, results cannot be further evaluated to analyse root causes and how the results occur.
Being different with previous studies in food availability policy assessment (see Table 1), relationships between indicators are considered in this study. Complexities in relationships are handled using system dynamics by conducting the integration of all elements of the socioeconomic and environment aspects as a holistic system. Beginning from CLD, all indicators in these aspects are connected as causal relationships and then converted into mathematical equation relations in SFD. Simulations have been run for a 22-year period of 2004-2025 representing the long-term strategies of the Indonesian Ministry of Agriculture. Scenarios representing government policies were performed for the period 2015-2019 and were then simulated until 2025. There are three simulation stages, (1) baseline condition simulation, (2) seven single designed scenarios, and (3) combination of two designed scenarios that were evaluated for their relative highest ISFA ratio and minimum requirement food availability score at 109.5 kg/capita/year at year 2025.
Regarding to all simulation-stage calculations, it is decided that scenario 5-4, a combination of expanding irrigated sown area and increasing agricultural investment policy, is the best policy for ISFA. To achieve sustainable food availability, Indonesian government should develop a new system of ISFA by implementing this selected policy. Adding and maintaining irrigation pipes, pumps and channels periodically can expand irrigated sown area for 500,000 ha/year. Without creating new sown area which causes increasing CO 2 emission, this new system can increase cereal production. Lowering agricultural loan interest into 4.25% can increase agricultural investment at 11%. By combining these policies into real system of ISFA, 159.6 kg/capita of food availability and 14.61 kg/capita/GgCO 2 Eq of ISFA ratio will be achieved at year 2025.
It is also our finding that best performance of ISFA ratio, food availability and lower CO 2 emission do not always mean the best performance in sustainability. It needs further evaluation by analysing the relationships and complexities between economic, social and environment aspects to assess how the policies can be implemented sustainably in the real system. Implementing highest performance ISFA ratio and food availability scores in simulation, scenario 5-2, a combination of expanding irrigated sown area and increasing fertiliser consumption policy, into the real system without further evaluation on CLD will make the ISFA system more unsustainable.
It is a necessary assumption that the final simulation result for scenario 5-4 is valid for year period 2004-2025. The solution might not be applicable for a year period that is not addressed in this simulation period. In spite of a significant exploration in variable relations complexities, it is a weakness of the system-dynamics simulation in that it might not can be applied into long-term policies (more than 25 years) on account of bias risk-result in the real system.
Further research for broader system of food security that include food access, food utility and food stability might be needed to evaluate holistic food security system in more comprehensive assessment.


Agricultural Extension in Latin America: Limiting Factors

Although a large amount of capital has been invested in the design and operation of agricultural extension1 in Latin America, the results have been quite unsatisfactory. The reasons that could explain this inefficiency are probably found in the limited level of training -in this topicby engineers (agronomists), extension agents and even postgraduate teachers. In addition to the lack of interest in the study of regional culture and the absence of theoretical-methodological models, which ultimately result in little interest from public and private institutions to develop research in extension.

Introduction
Although a large amount of capital has been invested in the design and operation of agricultural extension1 in Latin America, the results have been quite unsatisfactory. The reasons that could explain this inefficiency are probably found in the limited level of training -in this topic-by engineers (agronomists), extension agents and even postgraduate teachers. In addition to the lack of interest in the study of regional culture and the absence of theoretical-methodological models, which ultimately result in little interest from public and private institutions to develop research in extension.

Theoretical Analysis
Although Rogers [1] had already warned in his classic book "Diffusion of Innovations", when the assassination of JF Kennedy occurred in 1963, the information spread to a region hidden in the mountains of Colombia, while that the extension programs had not been able to disseminate technology based on the work of extension experts on biological-ecological elements, but certainly not on cultural, social and human aspects.
But what did agricultural culture mean for extension workers in Colombia? Probably they thought about traditions, indigenous behaviors or parties in the village. Although the previous thing if they are cultural manifestations, they are not the culture. In this sense, we will define a theoretical position that interprets the theory of social systems of Luhmann [2]. The culture of the people is rather a historical process in which the producers have been adapting to their surroundings, building a solid process of identity between the inhabitants and the groups of these regions. In this process, through a complex of relationships and communication, which matures over time, it becomes a collective memory and then a social memory, which nobody sees and nobody can explain, becomes the guide to social behavior of those regions.
Why do they "mano vuelta"2 or "mayordomía"? We could ask: "I do not know, that's how it's always been done here" the interviewees would reply. It is likely that the cultural agreement is to avoid doing something in the city or not welcoming strangers without the approval of peasants meeting. It is also possible that they manage their work productivity because they cannot manage the productivity of capital and they do not own it and therefore they ask their relatives and friends for help to do jobs that, even if they are worth capital, but do not cost them.
If the extensionist, his boss, public and private institutions and the government itself do not understand the above, they are more likely to never understand the limiting factors of extension. Thus, without systemic agricultural research, without cultural and social sensitivity and without knowledge of the subject, the extension models will always be incomplete and the efficiency of the programs on the subject will spend enormous amounts of capital, but will not achieve that the producers adopt new technologies.

Methodological Discussion
Even when Shutter & Orozco [3] had already suggested psychological approaches to extension, based on the achievement of: 1) Changes in producer knowledge when attending the demonstration of a technology specific for the first time; 2) Changes in the attitude of the same producer when testing the same technology on his farm. Finally, considering that he knew the technology, he tried it and he liked it, we would go to 3) Changes in the use of the aforementioned technology, considering that it adopted it. This process has been proven in commercial activities, with a high level of efficiency in adoption. It only remains to observe that the technology is in force for the needs of the producer and if it maintains an acceptable profitability. The producer will keep it in use, as long as the profitability is greater than one. As can be seen in Figure 1, the producer subject of the study (observer of the first order) when achieving technological adoption, becomes an Innovative Producer. This will now receive visits from producers from other regions and the same process of changes will be developed, at least in 10 locations and now we will probably have 10 more innovative producers. Following the process in expansion we will have adoption of technologies in use when expanding the program in predesigned regions. When implementing an extension program, such as the previous one, the interdisciplinary group must attend a community meeting and offer knowledge of the program to the vast majority, request permission and study their culture before initiating actions. In addition to the above, we must understand the reasons why they carry out their agricultural activities and through this we will understand how they do it and the environmental and economic factors they have come to adapt and adapt their environment to the needs of family and community life. A theoretical and methodological element that probably contributes to the achievement of the extension program is the use of systems theory adapted to agriculture. The use value of such a procedure would make it possible to speak now of Agroecosystem, as a system, to design, implement, implement and evaluate agriculture from an ecological and systemic approach, of course more realistic than other theoretical perspectives. But, what is an agroecosystem. Based on the postulation of García [4] on the concept of system, we will say that: "The agroecosystem (AES) is a representation (a model) of a cut of the regional agricultural reality to which the observer of second order aims to modify. The AES is studied as an organized whole, related to the cutting of the AES is studied as an organized whole, related to the cut of the mentioned reality, so it should not be studied in parts, nor separated from its regional environment at least (system=reunited).

Conclusion
We could conclude, that the AES is an element of episteme (science) therefore it is an abstraction (It is not real) what is real is the Doxa, which contains the everyday and that for our case would be the crops, plantations and the cattle. The advantage of considering the AES as a model is that it is now possible to model the structure and functioning of the Agroecosystem design in a computer.