The George Boole Foundation Limited has posted an advance notice of their SDGToolkit (see right). This is a Software-as-a-Service (SaaS) cloud-based implementation with a global reach. This service is scheduled for launch next month (April 2021).
A general article on the content of this new system has been prepared by John Penrose of AgroInfoSys entitled, "Advance notice on the SDGToolkit 2021"
Currently SEEL-Systems Engineering Economics Lab has designed and is currently implementing analytical models that will introduce a project appraisal system that is more suited to the design of projects in an environment experiencing rising temperatures and CO2 concentrations. Research at SEEL, based largely on Brazilian evidence, has identified short term meteorological variance to be of more economic significant in planning projects. This has given rise to analytical tools that determine risks of yield variance. As a result of this development there is a need to revise conventional project economic and financial appraisals. In particular cost-benefit analysis and other economic rate of return options all need to undergo adjustments to produce reliable results upon which to base investment decisions. Introduction
There is a natural cycle in the variance of temperatures and rainfall around historic recorded averages. The variance tends to follow known cycles of between 4 to 5 years. Therefore in any particular year with average conditions associated with any particular day in the year can be followed by a year in which temperatures and/or rainfall rise or fall by a significant amount. For temperature, peaks can be exceed 5o
C above or below average. As a result the actual impacts of climate change are being felt now both in productivity and income terms because in any year temperatures can rise, not by the long term projection of 1.5o
C in 25 years, but by more than 5o
C in any current period. In certain crops such a temperature rise can be significant impacts and with rising average temperatures these will become progressively extreme while following the annual variance cycle.
|McNeill, H.W., "The Role of Micro-Bio-Climatic Zoning & Genotypic Mapping", Agricultural Research, Development & Dissemination, SEEL-Systems Engineering Economics Lab, 2006, ISBN:978-0-907833-26-0.|
This suggests that "net zero" by years such as 2030 or 2040 or 2050 will be too late for some production systems.
Already in Brazil some coffee production units have had to move three times within a period of 30 years as a result of the flower of Arabica coffee being sensitive to temperatures in excess of 33o
C and resulting flower mortality and no coffee production as a result. This resulted in necessary relocations of coffee production to higher cooler regions. At the other extreme frost is also an issue but becoming less so with temperature rises.
Hector McNeil's 2009 paper entitled, "The Role of Micro-Bio-Climatic Zoning & Genotypic Mapping
", set out the locational-state relationships between different crop or natural plant genotypes and microbioclimatic zones within which they are located. This was an elaboration on earlier work completed in 1968 which described the existence of microbioclimatic zones within larger "major bioclimatic zones".
The adaptation of natural vegetation and crops to specific bioclimatic zones is something that has occurred as a result of many centuries of evolutionary pressure linked to competition, temperatures, water availability and fertility. Adapted plants have become dominant because they can tolerate the "normal" seasonal variances in the microbioclimatic zone within which they are located. The stability relates to boundary conditions of expected maximum and minimum temperatures and water availability in any particular production zone. However, with long term temperature rises the boundary conditions are changing not as a stable condition but rather one in which variance does not return to previously normal ranges. With increasing average temperatures the temperature maxima are increasing and with this the water availability is decreasing as a result of evapotranspiration rising with temperature.
As a result SEEL is developing quantitative "relative stability horizon" analyses (RSHA) or estimated time periods during which crop production projections remain reliable. As a result the standard project financial and other projections are subject to significant alterations in analysis on a case by case basis linked to the specific geographic locations of projects. From a recent review of documentation the main development agencies have not yet referred to this particular issue nor have they introduced any additional guidelines to address it. SEEL, however, is finalizing analytical tools (ATs) to manage this analysis to add to the SDGToolkit. The emphasis here is on the OQSI evaluation criterion of "resilience". All of the OQSI recommended procedures for project design, including a specific set of performance and sustainability criteria1
, have been adopted as the basis for the SDGToolkit development.
Concerning the need to change appraisal methods, Hector McNeill explained that,
Managing production strategies
"The intellectual investment in the economic analysis of agricultural projects and the associated techniques are undergoing an enormous upheaval because well tested systems are not providing accurate projections. The economic projects need to be based on the feasible maintenance of yields of specific genotypes of crops as they face increasing temperatures and reductions in water availability. Whereas project prospects in the past could rely on RSHs of 20 years we are now having to deal with RSHs of 15 years and in some cases 10 years or less. In many cases current genotypes have precarious economic prospects and need to be substituted. Somewhat like the race for vaccinations under Covid-19 and the problem of variants, cash flows are becoming increasingly dependent on adjustments to genotypes and production systems linked to water conservation. Any project design and appraisal system needs to take such issues into account."
Unlike the case of vaccinations however, in most parts of the world there are already genotypes adapted to higher temperature and lower water conditions and many of these occur as "landraces" or commonly produced crops that perform well under "average conditions" of ambient temperatures and water availability over the year. Locational-state genotypic sequencing (LSGS)
|LSGS-LOCATIONAL-STATE GENOTYPIC SEQUENCING|
McNeill, H.W., "Temperature and water stress profiles associated with seasonal variance under rising average temperatures",Agricultural Research, Development & Dissemination, SEEL-Systems Engineering Economics Lab, 2021, ISBN:978-0-907833-51-2.
As a basic locational-state principle, in a region with significant differences in elevation, the landraces at lower altitudes will tend to be more tolerant of higher temperatures than those growing at higher altitudes. Therefore the safeguard against losses in production arising from rising temperatures and falling water availability, is to introduce landraces from lower altitudes as temperatures rise or to move production with a current landrace to higher ground. Clearly it is easier to introduce lower altitude landraces. Therefore over time, rather than follow standard crop rotations, projects need to apply a strategy of locational-state genotypic sequencing (LSGS) in order to raise the likelihood of securing relatively stable yields "on average" in spite of long term temperatur rises.
In diagrammatic format analysis, the diagram on the right shows an early explanation of this issue.
The diagram shows an expected variance map when there is no rise in average temperatures demarcated by the blue box RRRR. The line of average temperature or rainfall is marked as TB and the max and min variance is indicated by the blue double-ended arrows.
With rises in average climatic temperatures the short term variances for temperature will rise to higher temperatures as indicated by line RR2 and the maximum water availabilities (water surplus or deficits) will decline as indicated by line RR3.
Associated with each condition strata demarcate the boundaries between genotypes that are adapted to each range of conditions a-b, b-c, and c-d for temperatures and e-f, f-g and g-h for water availability. This map is equivalent to a terrain model segment where each strata represents a specific altitude then by transferring lower altitude genotypes to higher altitude sites can adapt production systems to the changing conditions.
Although this diagram shows water deficit rising over the long term, within seasonal variances the combination of temperature and water availability can vary. Soil conditions and especially texture, on any particular site, are an important co-determinant of yields because of its impact on water holding capacity.
Implications for project evaluation
Applying a locational-state genotypic sequencing strategy would not disturb the standard projections for economic rates of return on projects. However, part of the justification for project plans to include locational-state genotypic sequencing is to carry out analyses to estimate the impact of not carrying out such sequencing. This can also provide a basis for estimating the economic advantage to be gained from locational-state genotypic sequencing. Some zones do not have a particularly bio-diverse representation of crop genotypes. This can be the result of extensive use of selected genotypes or GMOs. In such cases the economic evaluations of the likelihood of future yields, caused by temperature and water stress, can provide an economic basis for justifying genotyping selection or breeding to raise the probability of obtaining more suitable genotypes within required periods. The types of comparison are shown in the diagram below.
|Illustration of the likely impact of LSGS strategies|
project cash flows of production systems
Source: SEEL-OQSI Workshop, SDGToolkit ATs, Briefing note: "Managing the economic evaluation of agricultural projects under climate change", SEEL, Portsmouth, February, 2021.
The diagram above refers to mixed production systems involving perennial and annual crops with different degrees of temperature (T) and water deficit (W) sensitivities. The cash flow of a LSGS strategy of genotype substitution over time is compared with no LSGS application where the vulnerabilities of static production systems impact cash flow due to TW impacts.
In terms of relevance to the SDGToolkit, Hector McNeill stated that,
"We have a set of ATs to address the main points of failure of the current Agenda 2030 project portfolio covering financial and real income returns and carbon footprint and carrying capacity impacts, these are parts of a library referred to as Options Benefit Analyses (OBA). It is evident that this interaction of temperature and evapotranspiration creates a binary issue of temperature and water stress. Therefore in order to carry out logical analysis we are adding two additional OBA tools that project temperatures and water deficits within short to medium term based on known variance cycles. However to make use of the information generated, it is necessary to relate the results to the degrees of tolerance of existing or potentially introduced or bred genotypes. The groundwork required is sample surveys to identify, classify and give identifiable names to landraces. Identify genotypes in equivalent locational-states in other regions of the world and to review available bred and certified genotypes available. In reality a considerable amount of this information is available but it is a matter of organizing it to address this issue."
"Our development of robust ATs to handle the new OBA tools for temperature and water linked to genotypic tolerance are underway and prototypes will be tested during the first week of April, 2021. I have requested for these not to be included in the launch version of SDGToolkit in order to have time to finalize their design and obtain stakeholder feedback. They should be ready to be included as part of the first system's update scheduled for June 2021."
|Posted: 20100417||We welcome questions and feedback:|
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