This is a relatively advanced set of constraints and policy analysis tools some of which can produce output which would interface closely with typical policy analysis documentation content of governments and development organizations. Indeed, from records of internal workshops, comments from one policy analysis suggested that the GCA has a more complete set of analyses than those commonly applied by government planning departments or international development organizations. However, this is because the required level of detail on agricultural issues is greater. This enables users to identify and measure the impacts of existing policies on gaps and needs. For example, are gaps generated by policy, natural resources, economic/market factors, or all of them? In terms of needs, do existing policies impede solutions or support them?

The entry menu to the GCA is shown below.

Currently, the GCA AT library contains 25 ATs and the following stages of analysis in the 3DP at the project level includes some 55 ATs.

Screen shot of GCA menu



Above, I listed 10 analyses that will be demonstrated in this article. The first was:

1. GENERATE POPULATION PROJECTIONS SEPARATING OUT YOUNGER COHORTS
This section starts off by projecting population numbers based on available official data. So by clicking on the access menu button the following menu selection appears:



The first menu item accesses a tool that calculates population growth based on official growth rates. The second tool completes the same projection based on birth and death rates but also adds estimates of the numbers born from the start date and their percentage participation of this cohort in the total population by advancing age group. This provides a profile of the changing requirements according to age cohorts. To secure a more complete cohort makeup as from a start date simply start the projection further back in time. Usually population numbers associated with earlier start date are available but there is a need to check on birth and death rates in the intervening periods before running the projection.

Example of population projections with cohorts is shown below.

This particular tool is used to generate baseline data for a wide range of associated analytical tools that cover economics, resource requirements, carrying capacity, food availability and real incomes. The example is for the United Kingdom.

The example is a single projection based on specific assumptions but any number of options can be generated. The data input screen is simple and easy to follow, as shown below.

On the right of the input dialog there is a guideline button. These buttons occur on all analytical tools and provide a guidance on the function of the tool as well as significance of the results and why these calculations are carried out. SDGToolkit has invested a lot of effort in the production of informative and useful contextual help; this helps users understand the processes involved, and the reasons for them, providing an underlying confidence in the process and credibility of results.

The first table is the data input and a summary of some results.
The system generates an automatic narrative to ensure a correct interpretation of results as shown below. This particular analytical tool is very simple so the narrative is likewise. However in some of the more advanced tools the narrative is an essential support for users where the analyses and results are more complex. The operational logic for the more advanced tools is based on advance decision analysis logic or AI.


The full dataset generated is provided below where the growth in the younger cohort features in the data in the four right hand columns.


The associated graphic output is shown below. The cohort data, depending upon the input data, provides important information in the fields of provisions for pre-natal services, child nutrition, educational requirements and many other life stage associations requiring the support of a sound local economy and access to services and essential products. This type of data is of importance in low income countries. However, the nature of economic "growth", in the United Kingdom has witnessed something like 35% of the population and their children enter into poverty status. The questions of poverty are addressed in other analytical tools that make use of baseline population and cohort data generated by this analytical tool.





2. CREATE COMMODITY BALANCE SHEETS TO FIND OUT THE DEGREE OF NATIONAL SELF-SUFFICIENCY IN NEEDED COMMODITIES
In the further development of this article I will present the outputs of named analytical tools in order to reduce the amount of content. In all cases the input dialogs are simple and guidance is also good so a user remains "in control" of the sequence of analyses, understands what the tool is doing and why.

All projections end up with unique IDs so they can be recalled later. The main purpose of the initial run through GCA procedures is to secure a "orders of magnitude" of constraints, their impacts in terms of gaps as well as identifying likely primary solutions. Once procedures have been completed users will have gathered a good profile of the levels of importance of different gaps identified and then the analysis can be refined to come up with more precise measures of gaps and needs.

In terms of the production of key food commodities, item 5 in the GCA is "National commodity balance sheets" which generate projections on the national production, inventory and trade balance of many commodities. The image below is a typical output. This tool provides useful insights into the degrees of national self-sufficiency in production. For example in the example shown below there is a significant production deficit indicated by the large negative trade balances in grain and flour/meal. Addition data generated in this projection tabulates the availability in terms of availability per capita in comparison with the nutritional requirements. Commodity losses in harvesting, grain and flour store are calculated which can provide some justification for better harvesting technique whereas storage losses arise from pest infestation as well as the drying out of stored produce resulting in loss of weight.

The balance sheet tools cover grain, vegetables, fruits, oilseeds, orchard crops, roots, vines, wine, meat, milk and eggs. Each tool provides the options available within each commodity complex.


3. CALCULATE PER CAPITA FOOD CONSUMPTION LEVELS TO IDENTIFY AND MEASURE THE SIZE OF DEFICITS
In the data input dialog there is a field requiring a per capita consumption figure in kg./annum/per capita. Here you either put the reported level or the desired level according to nutritional standards. The output of the Balance Sheet AT show the difference between this value and the value calculated on the basis of the balance sheet estimate of availability divided by the population. Naturally average figures are insensitive to acute problems of food intake and quality amongst lower income segments. This issue is handled in the analyses completed in items 6, 7 and 8.

4. PROJECT THE FUTURE NATIONAL REQUIREMENTS FOR SPECIFIC FOODS LINKED TO REQUIRED TARGET PER CAPITA CONSUMPTIONS LEVELS
In the context of commodity balance sheets and based on a national population growth and selected cohort analysis, it is possible to calculate a series of important analyses linked to nutrition, the need for certain types of food, what national land resources are required to produce the commodity through import substitution to alleviate deficits in per capita consumption. This is followed by more refined analyses determining the real incomes or purchasing power of population segments and the relationship between affordable prices for consumers and feasible prices for producers. Below is a typical output for this type of analysis.









5. WORK OUT THE AREAS OF LAND REQUIRED TO ACHIEVE DIFFERENT LEVEL FOR SELF-SUFFICIENCY FOR CRITICAL COMMODITIES
The areas of agricultural land required to increase domestic production can be calculated making use of the GCA item 4. The screen shot below show an output of estimates of areas of land required to produce 250,000 additional tonnes of corn, according to different levels of productivity. Each level of productivity, in this case rainfed crops, i s associated with a specific "technical package" of inputs and production system each with different carbon footprints. The yields, it should be noted vary with season also (dependency on temperatures and water deficit). This means that the high yield option only attains those yields in "good seasons". Therefore projections need to be based on averages on the production system used in the knowledge that temperatures and water deficits are rising.


6. GENERAL SCOPING OF RELATIONSHIP BETWEEN INFLATION AND REAL INCOME PROJECTIONS

General impact of inflation on real incomes

7. DETERMINE THE PROJECTIONS OF REAL INCOMES OF PURCHASING POWER OF MIDDLE AND LOWER INCOME SEGMENTS FOR FOOD ITEM
This brings us to analyses which begin to focus attention on potential crisis points not only at national level but down to community levels associated with real income levels. No doubt most have considered the subject of living incomes which has been reviewed in an article on this site in the series: "Economic Policies for Agenda 2030", entitled,

"Living Income - this should be a critical object of macroeconomic policy"
Here the quality of data input is crucial but then so are the dynamic factors linked to family size and projections of real incomes into the future where corrections need to be made based on changes in prices. The easy way out is to use the so-called Consumer Price Index (CPI) which are averages projected across a nation. In reality consumer prices vary significantly across nations and are quite location specific. Smaller communities using products transported from other locations often face higher unit prices and often different rates of inflation than say a town dweller. According to SEEL, CPI data, for many rural communities, usually underestimates inflation, sometime by a significant margin. It is worth mentioning that local farmers also face variable input price inflation, ending up with a "terms of trade" (the relative movements in input and output prices) that work against agriculture.

When attempting to sort out anything to do with an income that affords basic essentials on a sustained basis, within the context of SDGs, it is essential to take into account production economics as well as family budgets to determine a viable pathway forward for producers and consumers.

This analytical tool makes use of family expenditure data making use of existing or surveys organized by a project team. To allow for cultural habits and differences in these types of datasets some field are marked as "additional items".

The inflation rates applied to the projection of family purchasing powers can include the CPI, which is likely to be the lowest and often unrealistic. Other rates should include recorded worst state and average inflation rates. In reality carrying out local surveys over time will generate better quality estimates of actual inflation rates.

The analysis generates data for average family income levels and the lower segment family incomes as shown below.

Average family income - impact of inflation on purchasing power Lower family income - impact of inflation on purchasing power

8. WORK OUT THE MINIMUM PRICES THAT PROVIDE FARMERS WITH A COMPENSATORY PROFIT

Reviewing the trajectory of family disposable incomes spent on food items there is an accompanying trajectory of unit prices that can maintain the real purchases of food. In other words the family can at least consume the same physical quantities of food as at the start of a time series. However, this is only possible if the farm gate prices accompany or fall below this trajectory. Therefore depending upon the trajectory of farm variable input prices farmers will be able to maintain gross margins, or they can be driven to a loss. This would result, normally, in such output being diverted to higher income consumers or even to exports.

The decision of the feasibility of matching farm gate prices to an in market feasible price for lower income families depends upon the size of gross margin resulting and the decision of the farmer as to what is considered to be compensatory.

The left hand histograms show data base on average gross margin data while the right hand histograms allow for full seasonal variations associated with possible seasonal cycle variance in yields and embed risk factors.

Example 1: The feasible low income price range is not viable for farmers Example 2: The feasible low income price range is viable for farmers

9. ASSESS POLICY PROVISION OPTIONS, IF NEEDED, TO MARRY UP PURCHASING POWER IN LOWER INCOME SEGMENTS
TO UNIT PRICES THAT SUSTAIN VIABLE AGRICULTURAL PRODUCTION OF CRITICAL COMMODITIES
This last section is not really part of the normal concern for project teams but it is included to explain how much of the output of the GCA can provide valuable feedback to policy makers so as to assist them identify policies to ensure that funded projects end up achieving their objectives. Naturally this is of interest to project teams who do not want to design projects that will fail as a result of inappropriate policies. Teams should wish to avoid they types of impacts on low income consumers because that occur when, as often happens, when real income purchasing power dips, farmers will sell to higher income families. Often commodity dealers will purchase production for export or speculative hoarding to drive prices higher. Therefore, in order to satisfy the objective of helping lower income segment project stakeholders, a policy solution in terms of supportive measures is required.

In this case I focused on low income segments to quantify the extent of the national problem of poverty and their precarious position with respect to future ability to purchase food. The other analyses would have indicated if there was enough land to import-substitute and the pricing information indicates whether or not the farmers can compete with imports.

In terms of policy provisions, in the case of very low income families and farmers only having a marginal ability purchase and produce at desired prices, there is a need to review policy options to help sustain the viability of both.

The more significant factor is that these policy questions cannot in fact be resolved at this level of analysis. This is because the subsequent procedures in the 3DP conduct a more detailed evaluation of project prospects based on the conditions of localities where projects are considered to be located and based on 55 analytical procedures. In this sequence the additional constraints, or lack of them, in different locations where projects are expected to operate can add further details to orientate policy constituent targeting. For example, although production can occur in specific bioclimatic conditions in a specific area of a country, the constraints imposed by the terrain conditions and associated bioclimate in another area in the same country can alter the production potential and attainable gross margins of producers.

Climate change and the transition towards increasing Temperature and Water Stress conditions (TWS) is leading to falling attainable yields and therefore, in these circumstances, the "production functions" of the farms, used in project plans, need to change by including, for example, changes to the "production systems" including such techniques as water conservation and scheduling substitutions of crop varieties (genotypes) based on the meteorological cycles around the rolling means of temperature and rainfall. This locational-state genotypic sequencing (LSGS), developed by SEEL, is a way to lower the risk to yields as TWS conditions advance. Clearly this level of detail can only be collected during the project design stages to identify feasible project plans and costings. The ability to identify quite different production circumstances has an important contribution to make to the fine tuning of policy initiatives.

10. DIMENSION THE NATIONAL PRIORITY GAPS AND NEEDS TO ESTABLISH REQUIRED PROGRAMME SIZES
Dimensioning the size of national gaps and needs is of fundamental importance in assessing budgetary and investment requirements to bring about necessary change to address SDGs on a viable basis. The opportunity costs of inaction can be estimated and as a result the benefits of appropriate investment can also be estimated. By weighting investment requirements against the combination of farm production functions each with different input-output relationships provide the ability to weight investment requirements in a more realistic fashion. Therefore, as in the case of policy initiative analysis the establishment of multiproject initiatives as funded programmes depend on the information generated in the remaining 3DP procedures concerned with specific project constraints.

Why are nexus points important? A nexus point is an important connection between different factors that determine the output of a model. If data is not coherent (related) nexus points disappear or risk creating erroneous correlative relationships. All ATs are based on decision analysis models or determinant relationships of cause and effect, where the value of inputs determine the value of output based on a functional relationship or algorithm. In the GCA the correct way to run the sequence is to relate all data to a single country. As a result all of the data becomes coherent and relates to the substantive realities "on the ground" in the country concerned. An important conclusion of the evidence-based work conducted by the OQSI between 2010-2020 on project performance, is that a significant reason for project failure is lack of coherence between macro and micro dimensions resulting from: a lack of direct communication a lack of shared approaches to analysis In the case of the SDG environment where aligning national needs with project objectives, ensuring sequential coherence helps reduce this element of risk.

Summary of my performance

The volume of useful output generated within about 6 hours of work was impressive.

Normal error trapping works in the dialogs preventing decimals being entered where rounded figures are requested or rejecting text in numeric fields. Each dialog must be correctly completed otherwise you go nowhere.

SEEL went through my output based using the Real Time Monitoring and Evaluation (RTME) system which regenerates all of the analyses I completed. In general they gave me a reasonable mark for completing this work in the time with no previous training and just using their onboard guidance. They did question what they considered to be some unrealistic input figures (maybe some readers noticed these?).

However, they were disappointed in the fact that I had not completed a sequentially coherent series since, as far as they are concerned, this is one of the strengths of the GCA. The fact that I had frequently changed the target countries or did not specify a country, meant that I had amassed an incoherent dataset. As a result some important correlations or "nexus" points were lost (see box on right). Nexus points are essential glue in the macro analysis as well as in determining useful policy actions to ensure a sustainable operation and outcome of a multiproject initiative or a single project.

I found this slightly frustrating because the guidelines explain the importance of anchoring any sequence on a single country but I overlooked this important point. SEEL put this down to lack of familiarity with the system as a result of not having received any training on the system. In reality, SEEL had offered me a free short course but I had not taken this up. If I had, I am sure I would have realized the importance of demonstrating this important aspect and benefit of the GCA.

As a result I placed some emphasis in the intro to this review on the importance of sequential coherence. I have also agreed that when I review the project level ATs I will associate these with a coherent sequence from the GCA to demonstrate the power of this concept.

Posted: 20210811 We welcome questions and feedback:   To submit questions or comments on the contents of this article please contact the author or main reference source by email. The relevant emails are provided below:   Author:   John Penrose:     penrose@agroinfosys.org         Source:   SDGToolkit:     angus.raeburn@sdgtoolkit.com

---

APEurope.org