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Part 8: The significance of an extended farm typology for agricultural sustainability

Hector McNeill
George Boole Foundation
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.

In the previous article in this series Part 5: "Impact analysis - the role of locational-state theory in improving project results" the general relevance of locational-state bioclimatic variables to the analysis of farm production was described. Reference was made to farm typologies as a common basis for grouping data for analyzing agricultural farm profiles. In this article this theme is extended in the context of data quality, survey stratification and data analysis linked to the rapid changes in bioclimatic conditions.

The most common typologies applied are often limited to farm types based on farm size and production systems. These two are commonly applied in agricultural economics surveys.

During the last 45 years, climate change in the form of rising temperatures and changing water balance has accelerated1. However, in the context of securing the necessary improvements in sustainability under conditions of climate change, field workers have found data collected does not provide sufficient information for tackling sustainability in a decisive manner. As a result, there is a problem with diagnostics and ability to feed back useful advice to farmers on how they might alter their production in practical terms to increase sustainabilty while achieving a compensatory financial return.

Indeed, normal procedures for estimating economic rates of return for farm and project investments and policy payback, are turning out to be increasingly defective; estimates of the attainable rates of return to the environment remain rudimentary. This state of affairs has enormous implications for the operations of international development and aid agencies, banks and agricultural groupings, who fund agricultural projects and provide advice on project and policy cycle management and national strategies.

There is a need for increased production sustainability through more effective farm planning and project design. With bioclimatic change, the specific information requirements relate to determining the likely changes in crop yields over plan and project implementation periods. Improved procedures and methodologies for data collection and analysis, appraisal of projects, policies and farm plans have become an imperative.

Extended farm typologies have two important contributions to make. One is their role in improving the relevance of data collected while helping reduce data collection costs by up to 90%. Such data can also be more relevant for farm planning, project and policy designs to improve sustainabilty performance based on practical change strategies.
Hector McNeill


The evidence climate change in terms of rainfall intensity linked to temperature rises is particularly evident in rain fed agricultural production systems. Examples were provided in Part 5: "Impact analysis - the role of locational-state theory in improving project results". The important message is that unless care is exercised in the selection of crops for production in any specific location there is a risk that within a decade, conditions will have changed to such an extent that the production of some selected crops is no longer feasible. This is because of the ecophysiology of the crop, linked to its genetic makeup and DNA is no longer able to adapt to seasonal variance in temperatures and water availability in terms of maintaining yields or even having any yield at all. Basically, the agroecological classification that was applicable at the initiation of project is in transition and, as a result, the agroecological conditions will have changed, impacting yields and costs within a decade.

This has significant implications for the decision analysis applied to agricultural projects in terms of the selection of production systems that combine sustainability with financial feasibility, while allowing for the adjustments that need to take place under changing bioclimatic (agroecological) conditions.

Currently methods applied in agroecological zoning are based principally on calculations based on the slow moving changes in average annual temperature and rainfall regimes. Agroecological zoning, such as those applied by FAO, are climatic agroecological zones, they are based on selection of an instance in a time series of average conditions. However, although we speak in terms of "climate change" the varying impacts of this are a direct function of seasonal or meteorological variance around the moving climatic averages. Depending upon the region and local conditions there are seasonal cycles in levels of rainfall and ambient temperature that seem to alternate over a 4-5 year cycle on average (see right). So around the moving average of temperature and rainfall CT-CT there are seasons when variance is negative (below average) MMin-MMin and others when it is positive (above average) MMax-MMax so both temperature and rainfall can vary over a large range, in either direction and independently, by a factor of at least 15% around the moving average. So a peak average temperature during the flowering period of coffee, based on a climatic agroecological zone, might be 30oC but in a warmer seasonal peak, the 15% gain would represent a rise to 34.5oC.

The association of coffee crop damage with temperature from: Pinto, H. S., "AgroTalk - The Brazilian agriculture zone", CEPAGRI, EMBRAPA.
At 30oC there is no damage to coffee flowers (Arabica spp.), so the fruit setting takes place and coffee berries are produced achieving a normal yield. On the other hand at 34.5oC 90% of the coffee flowers are killed resulting in no fruit setting or berry formation and about 10% of normal production (see left).

This is an example of location becoming unsuitable for coffee production as average temperatures rise but the warning comes in an initial warmer seasonal cycles when a crop fails. This is followed by subsequent seasonal peaks in temperature, separated by the characteristic cycle, when the percentage of crop impacted increases. In this case, locational-state analysis points to temperature as being the problem although the rising temperatures are likely to also increase evapotranspiration in that season, and therefore water deficit which, in rain fed coffee, is likely to result in an overall decline in yields even if not all flowers are affected. The locational-state solution is to move coffee production to a zone with lower temperatures which in practical terms means moving from the current altitude to a higher altitude where average temperatures are lower. Moving to an altitude some 400 metres higher than the current production would lower average temperatures by 2.5oC or an average of 27.5oC. Applying the same seasonal variance calculation to this the maximum expected variance in warmer seasonal cycles would be 31.6oC which would see a damage to the crop reduced to just 12%. The lower evapotranspiration, depending upon the rainfall and soil texture, could result in higher compensatory yields than outweigh this level of loss resulting from flower damage.

The geographic movement described above can be represented on a general biomass locational-state production surface, on the right, as the movement from point A in a regime of higher temperatures to point B a regime of lower temperatures and improved water deficit (water availability) at a higher altitude. The particular production surface for coffee in the hostile environment is however, not as shown on that projection but in reality it takes the form of the production surface below in which reflected the reduction in biomass achievable as a result of the higher temperature at flowering time.

The ecological and evolutionary context

It is worth dwelling on the implications of what all of this means in terms of production systems. Under normal conditions of evolution plants adapt to conditions so that those with the highest tolerance of ambient conditions survive and become dominant. However the relatively slow process of natural evolution and the imposed selection of dominant plants has been significantly disrupted by an acceleration in temperature rises and changes in water balance. As a result, human survival rests on the ability to understand the association of different crops and their range of varieties, or genotypes, to ecological locational state niches or micro-bioclimates. This has an important implication for the concept of production systems because a single production system as part of a production system typology become extended by reference to the varieties or genotypes best adapted to the location where the production system is applied.

The term "production system" is often primarily concerned with the technological aspect of production linking named crops to levels of mechanization and labour relations and occasionally, crop rotations. However, the example provided above makes it more than apparent that, for example, a production system including Arabica coffee in one location is not the same as the same production system in the second location. One is feasible in economic terms and the other is not. These differences have no connection to typical typological classes of farm size and production system but relate to the combination of the individual genetic makeup of crops (genotypes) and the critical environmental locational-variables necessary for the crop to produce on an economically viable basis.

The prime technology

The prime technology applied in agriculture is made up of the reproduction and growth processes of living plants and animals and the degree to which they are genetically adapted to the natural environment where they are located and how they respond to inputs supplied by the farmer, not only in the form of compensatory inputs for natural resources such as fertilizer and irrigation water but also in the form of manipulative hand and/or power driven mechanization and structures. In terms of the rapidly changing bioclimatic conditions the most critical variables making up this technological resource include:
  • crop by name
  • crop genotype (variety)

    • locational state bioclimatic tags (associative variables):

      • temperature
      • altitude
      • longitude
      • latitude
      • photoperiods
      • rainfall
      • water deficit
      • soil fertility
      • soil texture
Therefore production system specifications can be more useful if they contain the associated information of locational-state genotypic and bioclimatic variable tags which can associate crop genotypes and specific micro-bioclimatic conditions of any production system. This provides a better indication of likely associated yields and unit costs. Without aligning crop genetic characteristics with production system micro-bioclimatic specifications it is difficult to plan farms or design projects and policies that will help us combat the impacts of the dynamics of climate change illustrated.
The impact of locational-state on agricultural production

In the light of the examples given above an idealized cash flow projection is shown as curve CF1 based on the agroecological conditions at the design stage of a project.

Within a normal trajectory of climate change affecting temperature and indirectly water availability, the impact of rising rates of evapotranspiration and water deficit can result in the cash flow outcome becoming CF2.

Where the reproductive cycles are affected, such as in the case of coffee flowers being killed above 33oC, (Arabica spp.) the result is zero production and a negative cash flow. These locational-state trajectories occur typically over a period of between 10 and 15 years.

  • Allow for dynamic change in all aspects of a project

  • Identify and quantify the most likely changes as part of project design

  • Adjust project financing arrangements to take change into account

  • Adjust monitoring and evaluation procedures to this reality

McNeill, H. W., "Implications of CEPAGRI data for future proposal appraisals and evaluation methods", DAI 2020-2030, SEEL, January, 2021.

McNeill, H.W., "The Role of Micro-Bio-Climatic Zoning & Genotypic Mapping", Agricultural Research, Development & Dissemination, SEEL-Systems Engineering Economics Lab, 2006. "

The significance of this topic

The George Boole Foundation is embarking on it's second decade of development work in this field, founded on almost 40 years of decision analysis development work applied to agriculture by SEEL. The reason this topic is a priority is that currently very few formal and informal farm planning and project design appraisals take any of this into account. As a result all of the considerable intellectual effort is linked to an assumption of stable environmental conditions, such as the World Bank's J. Price Gittinger's "Economic Analysis of Agricultural Projects" and most of the widely adopted project cycle management procedures and evaluation techniques no longer apply in their entirety but require a major adjustment. The box on the right, for example, compares a normal cash flow curve F1 with one facing increasing water deficit (F2) and another facing the reproductive system temperature sensitivity described in the case of coffee (F3).

Holistics & TOC

In addressing the topic of this article there is a tendency for some to state that a holistic approach is required linked to a Theory of Change.

If one attempts to take into account what are considered to be key factors without a quantitative method of linking factors in a coherent manner, analysis becomes confusing. The same is true of TOC where results tend to be biased towards the specific experience of those managing the TOC sessions.

There is a need for due diligence procedures that avoid limitations on the scope of analysis imposed by any gaps in disciplinary knowledge and experience within teams, to ensure that all critical factors are taken into account and afforded due consideration. To make this approach feasible well-defined procedures associated with a systems approach that apply appropriate quantitative methods are also required to identify the most significant risks associated with bioclimatic change on a logical and quantitative basis in any particular project site.
Costs and yield

In introducing necessary change to improve performance in terms of social, economic and environmental sustainability farmers need to track what will happen to costs and yields as a result of any decision. Yields/ha. multiplied by farm gate prices determine farm income and whatever change is introduced will have an impact on costs. Therefore the mainstay countering any costs incurred to change production sustainability is crop yield and farm gate prices. Costs therefore need to be carefully managed because any bioclimatic changes occurring during implementation that affect yield, will impact costs and net margins.

The main change is that economists need to make use of techniques that enhance their communication with extension services and plant geneticists who have some understanding, at least, of agroecological zoning (AEZ) concepts. However, as mentioned, AEZ alone is insufficient as a tool to plan, other than providing a starting reference point. The challenge is to get to grips with the specific factors that cause of variance in crop response curves (yields) and to relate these to the environment within which production systems operate.

Risk and uncertainty

Assessing sustainability and depend on some form of assessment of risk and uncertainty and quite often project proposals roll up these considerations in "assumptions". So stating that is assumed that no change occurs or that a community remains interested in supporting a project is not addressing the questions posed. Sustainability depends upon a project production system's resilience. This depends upon an assessment of the likely risks that will be encountered during implementation and post-funding operations of a project. By collecting the meteorological data on a site and the climatic data (the average trend lines), the nature of water and temperature regimes become evident. The average trend provides a projection of the reference points around which the meteorological maxima and minima (MMax and MMin) are likely to occur. Thee will be associated with likely water deficits in specific years as well as temperature. Depending on the crop it is therefore possible to estimate realistic levels of risk measured in terms of likely biomass (yield) and damage associated with temperatures. Here, we are no longer dealing with assumptions but rather with quantifiable estimates of likely impacts on yields and costs. Based on this information, how does one go about designing a project or assessing the performance of a farm to plan for a better sustainable performance in the following year?

How to get started

Whereas this collecting the necessary data and applying it appears to be a difficult task the sequence of necessary actions to start a beneficial process is fairly straightforward. The aim is to collect, analyse, and take decisions to improve agricultural performance. Since data collection is expensive the strategy is to proceed on the basis of minimizing the costs and maximizing the utility of information collected. This is where farm typologies come into the picture.

In order to draw up a cost-effective survey, the statistical design of a survey can be enhanced through the establishment of stratified sampling and analysis. To facilitate this process appropriately extensions to typological groupings can be added, beyond be basic "farm area" and "production system". Whereas the statical design is the job of the statistician the identification of farm types is a matter of involving local knowledge from extension personnel or farmer associations with good local knowledge. Workshops to identify, first of all, the range of farm sizes and farming systems can kick off the proceedings buy the discussion needs to include he sharing of knowledge and information on terrain altitude ranges, available agroecological data and maps and information on soil textures. Crop geneticists and plant breeders who have local knowledge will usually know the relationship between available genotypes and the suitable agroecological conditions for most of those known and they should know where to obtain additional information. The next step is to map out the range of production systems by associating them with altitude. In regions with a large range of altitudes it is often possible to identify modifications in production systems that already exist in relation to altitude. For example temperate fruits will grow well at higher cooler elevations in Mediterranean climates and, for example, in more temperate zones, mountain pastures are largely dedicated to summer grazing. There are often associations between water availability and altitude and lower temperature micro-zones in comparison with water availability at lower altitudes. In rolling terrains with only small differences in altitude such differences are less apparent or relevant.

Through such an exercise the particular significance of each of the locational state bioclimatic variables to the local farm population can be determined. This is a logical procedure and is very useful because it is fairly easy to understand and the exercise of typing helps teams start off in the right direction with reduced likelihood of wasting time.

In 2019, the Sustainable Development Report confirmed that over 65% of production and consumption sustainability and climate change indicators had not yet been fully defined (specified). This would appear to represent an impossible barrier to progress but a benefit of extending typologies with locational-state bioclimatic variables, is that, with, or without, SDG indicators, preparation for more effective farm planning and project design can proceed in the right direction by gaining access to the relevant information on the ground with stakeholders. The more significant point about extended typologies is that they help statisticians, many of whom are not qualified in agricultural science or technology, stratify survey design to facilitate a reduction in data collection points from a full population to a small sample. This can reduce the cost of surveys while still collecting very precise, representative and therefore useful data, based on an extended range of farm types.
Data warehouses & big data

For some time, as a way to economize on data collection, some statistical authorities have encouraged the use of "administrative" data collected by government agencies for various reasons as a way to expand datasets. This has given rise to the notion of big data from which to build data warehouses so that users can generate "dash boards" and a range of correlative analyses.

In practice there are dangers in creating apparent correlations which, in reality, do not represent cause and effect relationships. Administrative data tends to be collected for specific purposes and survey designs address different levels of error. Some administrative data is notoriously unreliable especially that related to regulatory enforcement or inspections where agents involved can give misleading data for political, economic, social reasons as well as a device to avoid detection and prosecution.

In the case of agricultural yields and costs, "administrative data" is seldom coherent enough to make useful contributions to understanding relevant associations. However, meteorological data covering rainfall and temperature regimes is often useful in this context.

By increasing typologies to reflect local conditions the accumulation of data series based on sample surveys stratified to take into account the specific characteristics of the population can create a year-on-year accumulation of very high quality information in a multi-farm portfolio. With time, this represents by far the most cost-effective and reliable basis for data collection.

Incomplete and complete datasets

In order to understand the significance of this mapping process, reference is made to one of the most important concepts of locational-state theory. This is the difference between complete and incomplete datasets. The reason field operators and experienced planning personnel often find they are working with inadequate data for the resolution of sustainability issues, is that the datasets are incomplete. There are insufficient variables within the data set to diagnose and separate the contribution of farm practice to crop yields from the contribution of the natural locational-state bioclimatic variables. This lack of cross-association of data means that differences in observed yields cannot be adequately explained. As a result the unexplained variance in the dataset is too high to be able to draw useful conclusions on the questions that need to be answered. For example, to improve sustainability and yields should a farmer alter some inputs, change the crop genotype or introduce another crop, what are the implications of altering liquid hydrocarbons inputs in the trade-off between carbon footprint and financial returns? A more complete dataset contains more associations between farm practice and natural locational state bioclimatic variables leading to a higher proportion of explained variance and an ability to pinpoint where a farm, in comparison with farms within the same type, can introduce improvements in practice.

Data coherence

In order to facilitate diagnosis, datasets need to be comparable in terms of being able to make objective quantitative comparisons to identify what a farmer needs to do to improve performance from the standpoint of worker conditions, financial return and sustainability. Therefore even when locational-state bioclimatic variables are included, the variables need to be comparable in terms of units of measure and data types. This state of operational consistency is referred to as coherence.

By increasing the range of locational state tagged farm types, the datasets collected transition from incomplete to more complete datasets within which data can explain to a more precise degree "explained variance" while the amount of "unexplained variance" is reduced.

In the classification of farm practice into poor, average and good productivity and yields within farm types, it is easier to separate out the relative contributions of farm practice and technique, on the one hand, and the specific contributions of natural locational-state environmental factors to production, on the other. The use of this data in farm planning is set out in some detail in Part 5 of this series, "Impact analysis - the role of locational-state theory in improving project results"

Typological references and survey stratification

The reason for creating farm types is that it helps data collectors as well as analysts deal with subgroups of farms within which the datasets relate to similar conditions, so farm size is related to range of associated characteristics so a very small farm tends to have higher costs than medium sized farms where resource allocation is easier in terms of optimizing the use of land resources and larger farms usually have lower overheads, variable costs and higher yields. Farm production systems do tend to vary with the size of farms but farm production systems usually relate to the mix of enterprises such as animal production, mixed farming, annual cropping and perennial crops all of which, as a function of size have different levels of mechanization and levels of inputs geared toward promotion of yields.

If no account is taken of temperature and water regimes of soil types, the variance encountered within farms separated on the basis of size and production system, will be high. However, if the typology is extended by adding altitude, then if farm size, production system and altitude are included in the typology, then a factor that indicates relative temperatures can help explain differences in crop yields. Photoperiodism is the effect of duration of daily cycles of daylight length to night, which affects the flowering time of some crops as well as growth characteristics affecting those part of the crop of economic importance. Photoperiods are directly linked to latitude and the day in the year. Very obvious an necessary associations within production system typologies includes temperature and rainfall regimes and soil texture that influences water holding capacity of the soil and its availability to crops.

Distinguishing project design considerations from operational farm planning

In project design we are laying the foundation of a production system. In this work it is self evident that the crops identified need to be adapted to the bioclimatic conditions as well as an estimate of the likely changes in conditions likely to occur within the next 15 years or so. The risk analysis described is a way to build in schedules for different annual crops that are adapted to the projected changed in conditions. In the case of perennial crops, many of which might have no production during the first two or three years serious consideration need to be given to the projected risk factors that take into account likely instances of MMax and MMin conditions.

In the case of farm annual planning on operational farms, site of poor, average and good practice for different farm types (collected and complied from the population sample survey) plus the rainfall and temperature data for that season combined with any temperature corrections introduced according to altitude and taking into account soil texture can help a farmer pinpoint what the cause of difference between own performance and, say, average or good performance. For example in the case of drier conditions yields might have declined even although inputs, such as fertilizer, were correct. On the other hand the same inputs of fertilizer in a season with better water availability can result in higher yields. The variance in crop yields due to variations in weather conditions can be larger than variations associated with different input levels. However, in order to distinguish between the two and know whether or not to change inputs, a farmer needs access to the up to data set for the last seasons performance and comparable data for each farm type.

Time is of the essence

Here we are not talking about published papers or reports that appear perhaps a year to so after collection. What is needed is close-to real time data delivered following a survey which needs to take place during the year as well as to record yields and sold values but to make the data available to farmers or extension services within a week or so that in the time between harvest and preparing for the next season the required information is made available in a timely manner for planning to take place and inputs identified and secured in adequate time. If the survey techniques include field data collection procedure than can ensure direct or subsequent transfer into online databases the analysis and formatting of results can be completed very rapidly. If farmers do not have touch screen telephones, extension agents or farmer associations could download the farm performance data and distribute the reports in paper form. A degree of data analysis of the salient point of the last season's performance can provide a useful context for farmers making use of the reports to guide their diagnosis and farm planning.

The main associative variable relevant to determining the natural environmental factors contributing to yield differences
(the significance of any set will vary with the range of production systems, farm sizes and terrain conditions of the target popultion of farms/project sites)

Type variable (tag)
1 Area of the farmNumber depends on populationThe number of types will depend on the known distribution of areas in the sample to be surveyed. Farm sizes can vary from year to year as a result of inheritance conditions as well as consolidations.
2Production system – enterprises & intensityMain contribution to variance in data This does not vary much under normal circumstances but includes mixed (animal and crop production), mixed cropping or mono-cultures.
3Economic size - aggregate farm GM A sum of the contributions of each crop to farm marginsRelated to size of farm and relative crop areas and variations in yields and factor and produce market prices
4UAA – utilized agricultural areaMain contribution to variance in data This can vary from year to year, often associated with information on how rest of farm is occupied
5Genotype (varieties of crop) Main contribution to variance in data Genotypic mapping i.e. distribution of age of perennial plants and their genotypes (varieties) Location, production region agroecological zone (AEZ) as exists This will depend upon the distribution of target farms across a zone or several zones or micro-zones.
6Area of each cropMain contribution to variance in data This can vary from year to year for annuals and more gradually for perennial crops
7Age of crop at harvest timeMain contribution to variance in data Usually not collected but useful
8Location, production region (AEZ)Main contribution to variance in data Usually not collected but useful
9Average altitude (GPS)Main contribution to variance in data Usually only measured once but can be multiple readings each associated with a site for a multi-plot farm with separates operational areas that cover several altitudes (e.g. summer mountain grazing)
10Soil textureMain contribution to variance in data Usually only measured once on representative basis and recording the percentages of clay, silt and sand particles as well as notes on organic matter. Often farms will have identifiable areas characterized by the "weight of soil" where sandy is "light" and "clay" is "heavy". Significant differences should be recorded.
11Average temperature regimeMain contribution to variance in data Usually taken from meteorological records. A local meteorological station, automatic system beneficial (see under available water below).
12Average rainfall regimeMain contribution to variance in data Usually taken from meteorological records. A local meteorological station, automatic system beneficial (see under available water below).
13Available waterMain contribution to variance in data measure directly or remotely using met data Water moisture can be measured using a tensiometer but this is expensive and only takes isolated time-based reading unless an automatic system installed. It is possible to base water moisture on “water deficit” and to calculate on the basis of basic AEZ data but over the whole year remotely using average temperature and rainfall data combined with percolation and run-off estimates and evapotranspiration estimates to calculate the “net water” in the soil and soil texture can provide indication of the availability of the water to plants.
14PhotoperiodsRelevant to specific crops

Type variable (tag)
Impact on yields and costs
1 Area of the farmSmaller farms tend to have higher variable and operational costs compared to large farms related to returns to scale, equipment operations spending more time “in work” than in turning around and bargaining power in the purchase of inputs varies according to the quantities purchased.

Depending on technologies, small farms can have higher yields per hectare.
2Production system – enterprises & intensityThis has a significant impact on costs and yields.
3Economic size - aggregate farm GM Simply summation of crop areas x individual GMs. Indicates relative significance of target crop.
4UAA – utilized agricultural areaA basic measurement to prevent errors in joint area estimate of other crops by relying on area of farm.
5Genotype (varieties of crop) Check to see if bioclimatic zone of AEZ is appropriate for the genotypes used – yield effects
6Area of each cropDepending on technologies, small farms can have higher yields per hectare.
7Age of crop at harvest timeImportant in explaining why sometimes yields are different linked to cumulative temperatures and sunshine over life of plant.
8Location, production region (AEZ)This is from published data and useful to compare with observed data below
9Average altitude (GPS)In generally higher altitudes are cooler and impact crop yield depending on the type of crop and genotype. Each genotype has an optimal temperature range. Some crops have yield response curves that are highly sensitive to photoperiodism linked to latitude and longitude. This data can be measured using GPS devices.
10Soil textureWater holding capacity of soil is linked to texture and controls the availability of water to plants having a direct impact on yields.
11Average temperature regimeTemperature regimes have a direct impact on yields
12Average rainfall regimeRainfall regime has a direct impact on yields
13Available watermeasure directly or remotely using met data Water moisture is based on an annual balance of a calculated water deficit. This has a direct impact on yields
14PhotoperiodsCritical to flowering and other critical processes in some crops

Source: SDGToolkit Manual V.1.3, "Measuring risk and developing change strategies for projects and operational farms",SEEL-Systems Engineering Economics Lab, 2020

SDGToolkit Manual V.1.3,"Guidelines on the reduction of costs and error in sample surveys of farms", SEEL-Systems Engineering Economics Lab, 2020

SDGToolkit Manual V.1.3,"Guidelines on the reduction of costs and error in sample surveys of critical data for project design",SEEL-Systems Engineering Economics Lab, 2020

SDGToolkit Manual V.1.4,"Guidelines on the determination of economic rates of return and real incomes (ERRRI) for agricultural projects under climate change",SEEL-Systems Engineering Economics Lab, 2021

SDGToolkit Manual V.1.4,"Guidelines on the determination of rates of return to the environment (RRE) for agricultural projects under climate change",SEEL-Systems Engineering Economics Lab, 2021
Posted: 20200820
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