|Locational State Theory|The development of locational-state was initiated in 1985 a decade after the initiation of RIO-Real Incomes Objective economic research. Locational-state was a means of improving data specifications for decision analysis.
However, it has since evolved into a general theory for handling the substantive analysis of complex natural and economic systems and the development of decision analysis models.
Locational-state theory (LST) has a role in contributing to diagnostics on farms to reverse the negative trends concerning income disparity, general sustainability and climate change while sustaining economic feasibility.
There is a dedicated website for Locational-State.
The most significant lowest common denominator as an indicator of economic performance is the distribution of real incomes in the human population. On the side of the environment, the counterpart to real incomes is the carrying capacity of the renewable natural resources on the surface of the planet. To re-emphasize these points, inflation, employment, economic growth, are, in the end, reflections of the state of real income movements and desertification, drought, soil erosion and rising temperatures under "climate change" are symptoms of declining carrying capacity.
This article attempts to describe in a simple fashion the specific data sets which need to be used as a basis for stratification of collection and analysis on farms where changes in practice are required to support Agenda 2030 objectives linked to specific Sustainable Development Goals.
In summary this statistical and analytical stratification is based on four sub-groups of types delineated on the basis of:
Much of this content is based on ongoing development work advanced by SEEL-Systems Engineering Economics Lab applied and it is appearing for the first time here on Agricultural Innovation.
- Farm operations
- Locational-state biometric variables
- Locational-state economic variables
Please note that a considerable amount of detail is not included in this article in order to reduce its length. More comprehensive treatments are contained in the forthcoming publications, "The state-of-the-art and the future of Decision Analysis " and "Locational-State Theory" to be published by Hambrook Publishing Company in Q4 2020 and distributed via the George Boole Foundation Library.
We know that Agenda 2030 is not advancing effectively because although a form of economic growth is achieved, measured in terms of devaluing currencies, the more essential issues of growing income inequality, sustainability and climate change are getting worse. Amongst the projects that fail to achieve both economic and sustainability targets agriculture, according to the World Bank's portfolio of development projects over the last 35 years, had an average failure rate in excess of 35%. Our own research places the reasons why agricultural project results appear to be associated with reduced impacts are generally linked to:
- Deficiencies in the measurement and analysis of relevant biometric variables essential in determining risk and production possibilities
- The increasingly inappropriate methods applied to project design that reduce the practical effectiveness of project execution
Added to this we can state much agricultural experimentation and research that had taken place prior to 1970 had not taken into account bioclimatic realities where biolcimate is the environmental variables linked to water and temperature regimes and soil conditions.
The observation of comparative performances of farms can indicate to farmers what they need to change to attain a better performance. Performance can relate to environmental sustainability considerations as well as labour force conditions but any change needs to be associated with viable economic and financial performance. Farmers are naturally risk averse and therefore need to have the confidence that any changes introduced "will work". What works is the development of levels of competence or tacit knowledge in carrying out production in a different but feasible way. It is only in the carrying out of change that change can take place. Therefore an emphasis on the transfer of sufficient explicit knowledge to explain how to carry out a new technique is best accompanied by the opportunity for farmers to try the technique out in order to internalize the procedure and decide whether or not this is a viable proposition. Article 4 in this series covered this particular question.
In this article I want to review how the natural complexity and heterogeneity in biometric variables such as rainfall, temperature, soil texture influence on soil water holding capacity relate to agricultural production. I will then describe how this data is important in the design of the data collection and analysis systems so as to provide farmers with more reliable data upon which to base production decisions. Data reliability is essential to improve the predictability of decision outcomes so as to raise the confidence of farmers in taking beneficial decisions. The objective is to improve the impact of change introduced by farmers on sustainability.
With so many farming families facing the risk of eventual destitution as a result of the movement of the terms of trade against agriculture, caused by input price inflation, the underlying objective of the application of this type of analysis is to help contribute to a progressive support system for the agricultural sector which improves, on a constant basis, social and environmental sustainability while also maintaining a real income that provides a purchasing power to afford all of the future necessities of farming families.
The additional constraint is climate change and I will give some evidence of this as well as how farmers can be assisted in their response to this trend in their future plans to mitigate or avoid these impacts.Locational State TheoryIn this article I describe the development of Locational-State Theory (LST) as an approach of utility in analyzing climatic and meteorological variables. I coined the term "locational-state" in 1986 not as a theory but rather to place some rational order in a wide range of facts and relationships developed by many other workers, researchers and people involved in agricultural development and other domains, working across the globe. I make no claims as to originality of the content but the perspective provided by locational-state considerations is useful in helping focus attention of the essential data sets of direct utility to farmers. That locational-state developed into a theory relates to the fact that it has been found to have a generic application to most human activities and does not just relate to agriculture.
The agricultural system
One of the areas contributing to a constrained approach to observation and a rational analysis of agriculture has been how statistical design has been applied to agricultural research experiments and farm surveys. I have no particular problem making such a bold statement because Ronald Fisher, a pioneer in statistical analysis applied to agriculture, stated as much in 19261. He advised that the approach of separating out relationships to be investigated by focusing on one or two factors of interest to the researcher will often produce inconclusive results or confirm relationships that in the end only have partial utility. In reality what is required is the elaboration of analyses that includes more variables and this can often provide more useful results in terms of application to resolving practical production issues. The same is true in economics where many analyses are dependent upon the catch phrase "ceteris paribus" where research concentrates on a limited range of cause and effect relationships while holding all other variables at fixed values. This is of course unrealistic since systems do not operate that way in practice. In general macroeconomic theory this has resulted in some confusion, even today, in relating the impacts of such theory being applied in practice as policy, on economic units; prediction are often wrong resulting a low policy traction.
Although I was unaware of Fisher's observations on this matter at the time, as a post-graduate student it had occurred to me that logical, mathematical and statistical methods, as I had been taught to apply them, did not appear to be up to the job of analyzing what are normally complex agricultural production systems. There were two problems. One was the language used in describing the analysis usually defining an hypothesis to be carried out which was bound by the semantics of statistical terminology which for simplicity's sake usually ends up limiting the number of variables to be analyzed. Secondly, the methods of analyses applied were, in many cases, extremely complex, and often it was difficult to understand the relevance of results in terms of practical application for people such as farmers.
In 1968 I had the good fortune to participate in a systems engineering group at the Engineering School at Stanford University developing the specifications for an earth resources survey system. I was involved in gathering data on how to assess the remote sensing data collected, in relation to "ground truth", and also in establishing the potential uses of remote sensing data in agriculture and forestry.
McNeill, H. W., Contribution to systems engineering project Demeter, “An Earth Resources Satellite System”, Section: “3.1 Immediate Activities of a Managing Agency – 3.1.2 Truth Site Establishment & Ground Data gathering”, School of Engineering, Stanford University, Stanford, California. Final Report, June 1968
In this research I found the available information of biomass in ecosystems and the dynamics of range management more useful in coming up with propositions in agriculture and forestry than a lot of existing agricultural and forestry research; there was more related to the environment in forestry papers. This was because looking at natural systems the significance of the impact of terrain characteristics, location and altitude were items that could be measured and on which ecology possessed a fund of knowledge and information which was not available in most published works in agriculture. So here the impact of bioclimatic zones on the productivity of any plants including agricultural crops became very evident (see left). For example, the age of a plant, in relation to its location within a specific microbioclimatic region's water and temperature regimes, can have significant impact on the viability of the transition from flowering to fruit formation, flowers usually being very sensitive to high temperatures as well as to frost. Some genotypes are particularly sensitive to photoperiodism and therefore apt production regions are to be found within, sometimes quite small areas defined by longitudinal and latitudinal coordinates (e.g. non-GMO soybeans). These regimes also affect the ability of some natural pathogens to survive cold spells which break their reproductive cycles and incidence of disease as well as the overall impact on biomass production or yields.
However, from my experience of training in agricultural experimentation and reviewing a host of research publications it was notable that very little agricultural research, for example, paid much attention to the natural environmental impacts on agriculture when researchers were focusing on measuring the response of a crop to fertilizer or some new practice measured in terms of yield (biomass). There were some reports on the use of natural "indicator plants" which could be used to indicate a particular microbioclimatic zones suitability for certain crops. But in general few researchers had paid much attention to these important factors.
It is also notable that some indigenous communities had an extensive and profound understanding of the relationships between nature and the state of plants in the ecosystem. Many had their own plant classification systems and assignments of utility to different plants, which, in general were ignored by adventurers who reported back to their own "scientific communities" in far off lands, their "discovery" of plants and facts known to local communities for generations.
At the time I was also attending a course on development economics at the Food Research Institute at Stanford and I decided to develop a generic model 3DPF (Three-dimensional production function). The objective was to permit any number of variables and ranges of values of production inputs to generate an estimated output.
In 1969 a colleague at the National Commission for Space Research (CNAE now INPE), Mario Jino, a software engineer, generated a graphic plot based on the model which I had adapted to represent agricultural production. This was really a demonstration of a decision analysis model or simulator of the relationships between variable inputs such as fertilizer, water, temperature and any other inputs as shown in the diagram on the right. This model was an example of the types of relationships that exist between crop production and bioclimatic zones as set out in parallel work I had completed at the Engineering School on bioclimatic transitions associated with geographic location (see above left)
The lesson from this experience was that by not going via the statistical route I had avoided a considerable amount of confusion in analysis since the more variables that were included would have complicated the analysis of variance. The key factor was to include in the determinant model all of the variables that impact production or, in the case of agriculture, yield/ha. The issue remained, however, as to how this type of model might assist farmers in their decision analysis on what and how to introduce improvements in sustainability while also producing profitably.
From population and carrying capacity to climate change and Agenda 2030
The concern with planetary survival due to the over-use of natural resources is not something that became a concern in the RIO Earth Summit that took place in 1992. This was already the subject of a seminar series given at the School of Agriculture at Cambridge University in 1967 entitled "Population & Food Supplies" which was followed by a seminar series at Stanford University in 1968 with the same title coordinated by Bruce Lusignan.
It was only in 1972 with the MIT group's publication of the "Limits of Growth", funded by the Club of Rome, that there was a more general global media coverage of the issues involved. The emphasis then was more on use of natural resources and carrying capacity. It is notable that since that time the discussions have splintered into symptoms of declining carrying capacity such as desertification, drought, floods and temperature rises. However, the vital need to monitor carrying capacity as a summation of the state of nature directly caused by rising population numbers and changing process technologies, has been pushed into the background.
Although the impact of CO2 on temperatures has been established by Svante Arrhenius in 1896, the Intergovernmental Panel on Climate Change (IPCC) was only created a 92 years later in 1988. The IPCC was created by the United Nations Environment Programme (UN Environment) and the World Meteorological Organization (WMO) and the UN General Assembly endorsed the action by WMO and UNEP in jointly establishing the IPCC. 1992 Climate Change became a recognized as a major issue at the Rio Summit. It was only in 2015 that the United Nations launched Agenda 2030 to amalgamate a wide range of social, economic and environmental objectives embodied in 17 Sustainable Development Goals as a single global programme. So far the UN has reported that the Agenda 2030 portfolio is not performing effectively with economic growth being negatively correlated with income equality, sustainability and climate change.
Pioneering work in Brazil
|Culture of knowledge-exchange|
One of the notable features of my experience working in Brazil was the specific culture of knowledge sharing amongst agronomists and researchers at the Campinas Institute of Agronomy and the Brazilian Institute of Coffee. A lot of respect was afforded the more experienced colleagues and at the end of a work day the younger researchers would migrate to the rooms of more experienced researchers or colleagues for an end of day cafezinho.
These sessions were extraordinarily informative with the older colleagues holding forth on details of findings. The topics ranged across many fields from aerial photo-interpretation, GIS, remote sensing to ecophysiology, agroecological zoning, extension issues, genetic selection the use of inputs, plant pathology and even current policies. Unanswered questions would have been answered in following sessions with all involved having "researched" the issue. The other notable fact was that some unanswered questions later ended up as fully-fledged funded research projects; these informal and enjoyable sessions were a serious business.
In this period 1969-1979 there was no Wikipedia or Google around, they only turned up in the late 1990s and early 2000s. But the emphasis of precision, analysis and correctness of information shared between colleagues then, exceeded what is often found in such media today.
During this period I learned an amazing amount about Brazilian agriculture and coffee in particular as a result of the dedication of others to their work as a vocation.
I was very fortunate in working closely with the Brazilian Institute of Coffee while at CNAE as well as with researchers at the Campinas Institute of Agronomy (CIA) and later others at UNICAMP (University of Campinas). During this period I observed outstanding work carried out at the CIA Agro-climatology Section under Altino Ortolani and later at UNICAMP, largely headed by Hilton Pinto, who was originally working with Ortolani, on the development of data-based agroecological zoning work for crops in Brazil. In the period 1970 through 1980 Brazil produced some of the pioneering work by mapping out the agroecological zones for over 30 crops in Sao Paulo State and then embarked on the application of agroecological zoning in optimizing the location of coffee in areas with less production risks in collaboration with the Brazilian Institute of Coffee. In the meantime I transferred to FAO and headed a project to develop an automatic coffee inventory system for the Brazilian Institute of Coffee with a team from CIA and UNICAMP under Reges Scarabucci, including Mario Jino.
Some evidence of climate change impacts on agricultural production
All natural phenomena that influence crop yields do so as a function of their latitude, longitude, altitude and time as well as the age of the crop and all of which impact the yield.
For example frost in the State of Parana, occurring on average every 4 years in the areas south of the 23rd parallel South during the period 1965 through 1995. This resulted in falls in yields in the following years. At the time Brazil produced 60% of the world coffee so this was a serious issue given the inelasticity of demand for coffee generating significant price rises. Therefore coffee production (Arabica spp.) was moved to bioclimatic zones with less frost incidence. However, with global warming, for example in lower altitude regions of North East Sao Paulo State, farmers experienced a period when coffee yields were falling dramatically even although variable inputs and varieties were adapted to the region and as recommended by the best current professional advice via extension. The reason for the dramatic fall in yields was climate change-related rises in ambient temperature so that in the coffee flowering period, which starts around April to flower maturity in June in the Southern Hemisphere, the ambient temperature, on occasions, had exceeded 33oC and this condition is fatal for the flowers which are extremely delicate. As a result there was no pollination or fruit formation (berry) and therefore no production of green berries or only a very low yield.
As a result coffee production migrated to cooler higher altitudes in the State of Minas Gerais. In the meantime, where frost had been occurring in Parana in the past, for the last 15 years there have been no frosts as a result of global warming resulting in some coffee production returning to that State2.
Data for decisions
In 1982 to 1983 I was a member of a team set up by PROGNOS and General Technology Systems to determine the potential contribution of information technology and telecommunications to the EU economy. The work was for the Information Technology and Telecommunications Task Force (ITTTF) of the European Commission. As a result of my contributions to that work the manager of ITTTF asked me to join their team as a Senior Scientific Officer to head a systems team to identify applications programmes to help sectors of the European economy compete in a digitizing world using a global communications network to support an emerging knowledge economy3.
In that work I established user panels of sector stakeholders to help orientate this work. One of the most effective groups of stakeholders was made up of farmers and agricultural cooperatives. During the course of this work it became evident that there was going to be a problem in relation to the reliability of data supplied from remote locations to enable people to use this data to undertake decision analysis for important commercial decisions. The challenge therefore was going to be how can a person specify the information needed to take a specific decision? And and how does the person know the information sent is what was asked for? In the case of farmers, what information is needed to plan effectively? This was the same conundrum concerning agricultural data where there are often elements missing without users of the data realizing this.
In 1986 I realized that as a generic concept the missing data variables in specifying information requirements for a range of biomedical, manufacturing, logistics and most human activities in addition to agriculture, all related to their locational-state. The relationship between the phenomena and output as demonstrated in the old 1968 3DPF model is that output (yield) always has the same form of response curve to some input which shows diminishing response to increasing inputs (see left). This applies to activities in most sectors as a result of the rate at which tacit knowledge is accumulated through learning, for example, as well as the operational lives of equipment and machines being dependent upon their levels of maintenance and correct operational conditions.
I therefore began to develop a form of due diligence procedure to help specify "complete data sets" based on locational-state variables to be distinguished from the normal data sets which were normally "incomplete" from the standpoint of being able to build a useful decision analysis model to aid decision-making in a business, logistics company or a farm. Expressed in another way, complete data sets contain all of the data required to explain observed variance in data whereas incomeplete data sets lack critical data so that there was a gretare proportion of unexplained variance.
It became apparent later, that the locational-state logic is, in reality, a general theory because it is generally applicable. From the standpoint of a farmer, the standard procedure for planning annual production is to gather data on the current season's results by recording all inputs and their unit prices and the yield and also the farm gate price received for output. However, this needs to be complemented by a qualification of performance that is provided by the locational-state biometric variable values. These can help explain why yields vary to the degrees observed within any given season.
Climate and weather
Climatology concerns general conditions that develop over time so that all geographic locations have a typical climate described in terms of rainfall and temperature regimes and yet each year weather conditions (meteorology) vary around these climatic norms. It is the variations between annual conditions caused by different weather conditions that are associated with differences in yields from year to year. The extent of this yield variation due to weather conditions is often far greater than many imagine. For example, the graphs on the right shows the results of a 5 year experiment with barley conducted at Rothamsted experimental station in 1986 though to 1990. Each year the inputs of fertilizer were identical for the same variety of barley but the yields were very different.
In 1988 the yield reached 7 tonnes/ha.
In the years 1986, 1987 and 1990 the yields were similar at around 4.5 tonnes/ha. and then in 1989 the yield fell to 2.25 tonnes/ha.
The bottom graph combines all of the results to show the significant variation in yield associated with different weather conditions amounting of +/- 55% and -50%. In 1988 the water and temperature regimes favoured optimized growth and a dry harvest period minimize grain loss. In 1989 a cold spring and slow rate of emergence was followed by a cool and drier season leading to lower growth.
Therefore, locations established by agroecological zoning can, for example, be stated to be suitable for barley production and associated with an average expected yield of 5.5 tonnes/ha. However, the variance around that average can be highly significant and affect the income of the farm, depending upon farm gate prices, even to the extent of causing a loss.
Under cases of global warming it is the variance that occurs in this way that provides indications that alert farmers to needed changes. Note than in the case of the barley experiment 60% of the time (three years in five) the average yield was attained whereas 20% of the time (one year in five) a high yield was obtained and the lowest yield occurred 20% of the time (one year in five). The conditions associated with higher temperature variance are of significance since, for example in the case of coffee flowering and the risks of higher temperature killing the flowers. The general trend of climatic temperature rises indicates that if in any year this high temperature condition occurs then the likelihood of this occurring again will rise with the general rise in temperatures. Therefore to lower this risk it is necessary to plant coffee at a higher altitude so as to lower the seasonal extreme experienced at the lower altitude. In the zones where coffee has been exposed to this damage it is better to plan to replace the coffee with a crop that does not have this sensitivity.
|Key climatic data for yield diagnostics|
The key climatic data
The most important data that can help explain many variations in yields and general crop performance in any year is the comparative data on the general climatic profile and the weather profile results during the last production season. This includes the records on temperature, rainfall, evapotranspiration and water deficit where water deficit is, in simplified form, the difference between rainfall and evapotranspration and other soil water-holding factors such as soil texture. The data profiles on these data series are shown on the left.
A description of gross-margin farm planning is outlined below where the objective preference is the maximization of profit. However, the "decision-maker preferences" can include improved financial return on investment, real incomes, lowering costs, eliminating natural ecosystem impacts, reducing carbon footprints and contributing to the maintenance of carrying capacity. This is the basis of operation of the OQSI critical path for the selection of operational practice as illustrated below. This is a planning tool where any changes in the input and output (I/O) of the "technology and techniques selected" can be traced to economic and environmental impacts in order to select the best operational solution which should result in a positive or neutral result for carrying capacity. In response to the 2019 Sustainable Development Report that indicated the failures in the Agenda 2030 portfolio as economic growth being correlated to declining real incomes, falling production and consumption sustainability and rising temperatures (climate change), the OQSI accepted proposals from SEEL-Systems Engineering Economic Lab to recommend four types of cost benefit analyses referred to as Options Benefit Analyses (OBAs) where the measured benefits against costs are:
- OBA1 - cost benefit (financial return) analysis
- OBA2 - cost benefit (real income return) analysis
- OBA3 - cost benefit (carbon footprint reduction) analysis
- OBA4 - cost benefit (carrying capacity state) analysis
The OBA series has substituted the former OQSI recommended CBA series. In line with the LST animate-inanimate system relationships the whole process of productivity and tacit knowledge development is made up of a model that combines DNA-based plant, animal and human designs and explicit knowledge-based model-based designed inanimate machines.
Human observation, deductive reason, logic, learning as a basis for the design of advancing processes and physical task competence advanced on the basis of cumulative tacit knowledge
Machine-based procedural logic, data processing and physical capabilities - all the result of human learning and design
|OQSI Due Diligence Design Procedure (3DP)|
Critical Path for sustainable projects
ERR is the Economic Rate of Return;
RRE is the Rate of Return to the Environment;
Accepted change strategy based on best balance between ERR & RRE that upholds carrying capacity.
EK signifies explicit knowledge (information, data, algorithms)
TK signifies tacit knowledge (internalised and advancing capabilities).
Source: SDGToolkit Manual 2020.
If I have any comments to make about agroecological zoning as developed by FAO is that it has not yet addressed the needs of individual farmers in economic terms but has gone the opposite direction to GAEZ (Global Agroecological Zoning). The system has been used to demarcate zones of low risk for most crops types. These types of agroecological zones can bring a general economic advantage through government incentive systems only being applied in production areas where the agroecological conditions are apt for the crops concerned. This has been applied as a basis for reducing the risk to public expenditure and is used by banks and crop insurance companies to assess risk associated with submitted farm plans. However, because of the weather variance all agroecological zones will have a marginal area where the risks to production will increase and these margins will move with climate warming. Work taking these types of issues into account has advanced in Brazil.
In the 4th article in this series references was made to the importance of extension advice is assisting farmers improve their production performance. In any given year the state of performance is a trade-off between the application of the indicated "technical package" of seed, crop spacing, fertilizer input and phytosanitary precautions according to the expected average conditions. In any year the yield and therefore unit costs will differ from the "expected average". It is therefore important to detect as precisely as possible the actual factors causing the variance in yield either upwards or downwards. For this to be possible the farmer needs data on the locational state of own-farm as well as the comparative data for farms of the same type, preferably in the same region i.e. within a radius of about 150 km.Minimizing the costs of data collection and maximizing data utility
The basis of statistical design to minimize the costs of data collection while maintaining the utility of information collected to farm planning involves the applications of statistical stratification. In the case of taking advantage of LST-based knowledge, strtatification needs to be based on a farm typology. This is a description of different characteristics of farms in a region related to the following factors:
- Locational-state variables of the bioclimatic environment
- Locational-state economic variables of the economic (market) environment
Structures relate to those operational conditions which have a general impact on costs as a result of their structures including area, utilized agricultural area and production system
The operational factors are farming practice and variable inputs
The locational-state variables of the bioclimatic environment are beyond the control of farmers but by including these as a basis for defining farm types it is possible to reduce the variance of yields, for example, within these types. The typical bases for additional typing include altitude, water and temperatur regimes and soil texture linked to water-holding capacity and where water availability is estimated as water deficit.
This enables any differences in yields between farms, within types, to be directly related to farm practice e.g. different combinations of variable inputs.
Locational-state economic variable of the economic environment include distance from factor and produce markets which influence prices and costs, general input price inflation and farmgate prices
. Farmgate prices will often vary with season related to yields.
Typing is used in planning farm surveys to separate farms into groups that will have similar conditions with respect to yields and costs. This is used as an initial sample survey stratification to reduce the variance of results within each type. The careful characterization of types can help reduce survey costs which are becoming onerous from full population surveys to sample surveys economizing something like 95% of the full survey costs and yet maintaining the precision and representability of the data within types.
Then within these types the data on the yield and farm gate price for each crop associated with the respective units of variable inputs used per hectare on each crop and the unit prices paid for each input become more evident. This economic data is used to calculate the gross margin/ha.
|Record of farm performance as own-farm data|
(GM) associated with each crop where the GM is the revenue received for each crop/ha. minus the total value of variable costs used in each crop. Thus,
|GM = ||(Yield kg./ha. x farm gate price/kg.)||-||((VI1 x UP1) +(VI2 x UP2) + ... +(VIn x UPn))|
GM is the gross margin/ha.
Yield is the crop yield in kilogrammes/hectare
Farm gate price if the price received/kilogramme of crop sold
VI is the number of units of variable input
UP is the unit price paid per variable input
is the total number of variable inputs
Diagnostic procedure to improve sustainability
The process of diagonstics used to identify how a farm can improve performance is that of comparing own-farm data with three grades of practice for any given season for the other farms within the type on the basis of three standards of achieved performance or practice classified as poor, average and best
A farmer, maybe with the assistance of an extension agent, can locate the general performance of a farm within a practice class and then review the variable inputs to identify, where other farms have a better performance, the differentces in inputs. The basis of comparative perfomance can be profits, costs, or any measured sustainability indicator.
The advantage of this approach is that it is possible to calculate the opportunity costs of the decisions made in that production year associated with identified production performance deficits. This can assist farmers improve their practice based on evidence related to feasible performance data as a basis for improving their farm plan in the coming season.
Lastly the locational state information including GPS measurements (logitude, latitude and altitude), soil texture, temperature and rainfall and water deficit regimes during the production cycle are also required. As a result for each farm the following data set records the performance for the year concerned:Climatic and weather referencing
A farmer also requires, not only the comparative performances for the same types of in the last season but also the medium to long term averages that are based on records accumulated over, at least, the last 10-20 years. This information can indicate anomalies such as the impact of climate change related to warming.Concluding
In this article I have outlined some of the basic issues that influence agricultural production relating what are often self-evident facts that lie hidden in disparate "disciplinary groups". In order to develop a practical strategy to help resolve the main sustainability issues, there is a need to apply locational-state knowledge integrating separate realms of knowledge into applicable decision analysis models. The combination of object-orientated logic (objects, properties and methods), Boolean deductive logicis sufficient to gain valuable insights on how to improve sustainability. By emulating the key factors dynamically in quantiative models we can identify the options for courses of action.
We know that climate change in terms of global warming is occurring. Therefore the support of agriculture and communities needs to become more proactive in tracking the changing circumstances reflected in locational-state variables.
There is a need for more anticipation of likely changes such as the accelerating drying out of lowland regions as a result of accelerating evapotranspiration of water creating rising water deficits. This trend is resulting in millions of tonnes of addition water rising into the atmosphere within shorter periods of time and therefore leading to higher rates of cycling resulting in increasingly disastrous rain storms that dislodge soil on inclines that were previously stable. The incidence of mud slides and associated deaths resulting and the attendent suffering of families and communities from these events will increase.
In regions nearer the equator there will be a net loss of arable areas which equates with fall in carrying capacity. Hilton Pinto of CEPAGRI4
, reports that in the absence of climate change, cropland in Brazil is projected to increase to 17 million hectares in 2030 compared to observed area of cropland in 2009. Due to climate change impacts, however, all the scenarios simulated, result in a reduction of cropland in 2020 and 2030. In the pessimistic scenario Brazil could have 10.6 million hectares less land allocated to agriculture by 2030 as a result of climate change with the South Region being the worst impacted losing close to 5 million ha by 2030
In agriculture, agroecological zoning needs to become a dynamic process that is updated regularly to provide projections of where the optimised locations for different crops and specific genotypes will move to. This is an essential aspect of decision analysis to initiate rational processes of strategic planning. This can help prevent small farmers, with limited cash flow, from investing in the planting of perennial cash crops which, within a few years, are known to be destined to failure because of their location.
The battle against desertification will become increasingly difficult.
There is a general need to bring the analysis of economic and financial feasibility closer to the on-farm resolution of factors such as carbon footprints, carrying capacity and acceptable real incomes. LST has an important role to play here. On the other hand, LST is no more than a highlighting of the importance of already known but not well-disseminated knowledge on the impact of nature on human activities.
Subsequent articles in this series will connect the analysis in this article to specific actions on how to reduce carbon footprints, sustain carrying capacity and support acceptable real incomes by augmenting the impact of human actions to improve global sustainability. Hopefully this will point to operational advisory and practical extension structures that are better equipped to contibute to the vital decisions that can reverse the current trends in failure of the Agenda 2030 project portfolio where "economic growth" continues to exacerbate income disparity, CO2
and other greenhouse gas emissions, social conditions and natural resources carrying-capacity.
R. A. Fisher (1926);2
Video produzido por UNICAMP "Hilton Pinto sobre desenvolvimneto de zoneamento agroecologico no Brasil." 3
MITI ICOT Report 1982; 4
Pinto, H., "AgroTalk-Brazilian agriculture zone" CEPAGRI/EMBRAPA, 2012.
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