Difference between revisions of "ESApartnersupdateMay2021"

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=='''Africa RISING ESA Project Partners Meeting'''<br />'''6 May 2021'''<br />'''Virtual via Ms TEAMS'''==
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=='''Africa RISING ESA Project Partners Meeting'''<br />''' 3 June 2021'''<br />'''Virtual via Ms TEAMS'''==
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Meeting video recording: https://cgiar-my.sharepoint.com/:v:/r/personal/j_odhong_cgiar_org/Documents/Recordings/Monthly%20Seminars%20-%20Africa%20RISING%20ESA%20Project-20210506_120317-Meeting%20Recording.mp4?csf=1&web=1&e=M0aVYm
 
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'''Participants'''
 
'''Participants'''
 
# A. Kimaro, ICRAF
 
# A. Kimaro, ICRAF
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'''Agenda'''
 
'''Agenda'''
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*Seminar presentation: Better targeting – a pathway to more CA adoption? - Christian Thierfelder (CIMMYT) and Francis Muthoni (IITA)
+
* Ex-ante impact assessment of Africa RISING seed and fertilizer technologies in Tanzania with trade-off analysis model for multi-dimensional impact assessment- Dr. Lieven Claessens (IITA)
 +
* Understanding the potential contributions of ISFM to various sustainable intensification impact domains -  Dr. Job Kihara (CIAT)
  
 
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'''[[:File:CT Muthoni ESAmonthly meet 060521.pptx| Better targeting – a pathway to more CA adoption?]] - Drs. Christian Thierfelder (CIMMYT) and Francis Muthoni (IITA)'''
+
'''[[File:JR_3June_ESAPPT.pptx| Understanding the potential contributions of ISFM to various sustainable intensification impact domains]] - Dr. Job Kihara (CIAT)'''
 
----
 
----
 
*Download presentation from link in the title above.
 
*Download presentation from link in the title above.
  
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----
+
<b/>'''Discussion'''<b/>
−
Discussion
+
*Irmgard.  How can the difference in adoption of ISFM components between farmers in Babati and K/K be explained? Which are the components that the low adopters do adopt?
−
----
+
:*Job. Well, Irmgard, for the difference in adoption of the ISFM components, it is something that we have considered by looking at various components, and how the intensity of the ISFM influence multiple indicators. For example, in Babati, we presented those farmers with one component and their percentage ( those who are practicing ISFM and the practiced technology). In the writeup, we specified rates in portion, e.g., 65% of one ISFM group are all intercropping. We have those types of tendencies explained in the text and the presentation of the figures and the ISFM components.
 +
::*Irmgard. Okay, may you explain why are Babati farmers adopting more components than Kongwa and Kiteto farmers?
 +
:::*Job. Well, because Babati is a high-potential environment, they already have adopted improved varieties. Therefore, penetration of improved varieties is very high in Babati, and many of the farmers in the area, at least 75% > of the farmers practice intercropping. For Kongwa and Kiteto, the situation can be fascinated by the nature of the environment and may not be high penetration of improved varieties. It can probabilistically explain the different uses of ISFM components.
 +
*Irmgard. So, from the farmer, the need for these ISFM components would be high in Kongwa Kiteteo than Babati?.
 +
:*Job. I think so. But for the scaling perceptive, there might be less information going in Kongwa Kiteto relatively to Babati.  In Babati, we would expect to see farmers practicing five components, but, in this case, we have farmers with only two or three components.  I still feel there should be a push to make farmers go to four to five. But Yes, Kongwa Kiteto will be essential to make interventions on including these practices.
  
−
*R.Chikowo [Question]: yes good afternoon everybody, thanks for the presentation, am requesting on how do we operationalize for example you have given data on of about normal season below drought seasons, so what’s your suggestion? Are we going to use the weather forecast to make farmers change their  tactical  approach  to responsive farming. So is that the end point, is that how we are going to make it work because we have seen evidences  that CA has got advantages and during the wet year, its either , how do we communicate this information to our stake holders?  Thankyou.
+
*Job. I was shocked to find 0% components from 22% of farmers, any comments from someone in Kongwa Kiteto?
 +
:*Antony. To add up, the low adoption in Kongwa Kiteto can be because the ISFM components that go viral within the short period in this area is the water aspect, and depending on the technology, for example, the tied ridges has been a need in this area, for other technology like Chororo; the planting phasing goes very fast. Therefore, the easier of implementing the technology that captures water, farmers are quickly picking up. It can be one of the ISFM components that can bring up many things, but to what extent has that been captured and under which technology? is something we can look at the data set that Job has.
 +
*Shitindi. In addition to Dr. Job and Antony, the other factors can be access to the market. Babati has more access to the market of the ISFM technologies, primarily for the Legumes (pigeon pea) than farmers in Kongwa Kiteto. Since this has something to do with the financial muscles, and there are investments, the rate of the reinvestments with the ISFM has something to do with the adoption rate.
  
−
*M. Bekunda [Question]: Thank you so much for the presentations, you know the first presentation which is more real from Christian , I was seeing the output with very high variation. When we got to Francis’s presentation, he is operating with very narrow yield gain, I don’t know how this relate both?
+
*Irmgard. Thanks, Shitindi, but do you mean input or output market?
 +
:*Shitindi. I mean output market, Irmgard.  
  
−
:*F. Muthoni [Response]: Thank you, let me start with Regis, ok so we note very clear evidence that CA is given more yield advantage during  the season  with normal rainfall, we know as a result of low rainfall in southern Africa after everything  in a 5 year period out of the five years, 2 years is drought so we expect that CA  will be more important at that level. So we have data time series which is almost real to predict  but then if you want to look at the early age of a season we need to inform farmers, ok what is the outlook, we can  use the seasonal forecast as Regis mentioned so we can give early warning to the farmers that this year we will have drought then people who are likely to implement CA are likely to yield advantage. Then how do we communicate, but if am not very clear but . the evidence that we have now formed, my opinion , I mean the evidence we have now generated I mean, the next stage we should be going to  the extension and to the farmers and showing them the CA and how much they can get or benefit  and implemented in the drought so that  so that when the extension people are reaching out to the farmers they can pass this information. I don’t know Regis is that the way you expected?
+
*Swai. To contribute to Shitindi, Antony & Job’s comments on the issue of adaption, adoption is complicated because some of the ISFM technologies, like tied ridges, require massive labor. Hence the rate of adaptation cannot be similar when we compare the nature of labor requirements. Technologies such as mechanization are labor-saving. Currently, there is another survey going on where Kongwa is a part of it. The Kongwa Kiteto team may note that the current level of intensification, maize and pigeon pea component is becoming common like Babati. If we refer to the adoption level of ISFM using pigeon pea in Kongwa Kiteto, we can note the variations. The soil water technology is labor-intensive on one side, and mechanization technology is imperative for many farmers to pick up the technology.
 +
*Carlo. Job, how can the differences in yields computed among AR farmers be attributed to the AR project and not other intervening/confounding factors?
 +
*Job. It is unclear if it applies in this case but will use in the survey we are conducting. We have tried to categorize our intervention farmers and farmers whom we did not have an intervention with (the control farmers), and we have records of the list of all our farmers and use the data to set the proportion of the farmers that we will interact with and farmers how are not part of us from the project site to show the difference between those who have interacted with the AR project vs. those who have not. On the other hand, we want to access the initial farmers with the project and identify farmers who had been/ not been considered to see if there will be attributions that can be done. We are doing this with Julius Manda
 +
*Shitindi. Job, from the presentation, most of the domains are presented by the number of the ISFM components adopted by the farmers, but labor requirements also increase with the number of components adopted. We may establish the label of combinations of these components that people can see the most rewarding in terms of labor investment?
 +
*Job. I will consider that as something that we can interact with the data set and see how we can enrich our publication with what you have contributed to us.
  
−
:*C. Thierfelder [Response]: Let me compliment your answer Francis, I think the merit of this work has been that this is the first attempt on a richer level to define what we call recommendation domains where CA would probably give the best return on investment, and I think this has been achieved. I think how we now action that in a drought season or in a wet season  and if we tell farmers that in a wet season they should not do CA here the different and the secondary question we will still need to answer properly . firstly, what I think is that there are areas that perform in both that is in drought seasons and in wet seasons and there are areas that are very variable and very distinct so  like in drought season they may perform but in the wet season they may completely not perform  and in such areas we will need different technologies or a seasonal based recommendation. It is not that CA is a one sized fit solution, but there are areas where in the majority of years in places, we get a yield benefits and that’s why it is safer to go to those places we have identified that is a good starting point or a good entry point for a conservation agriculture promotion because there are years where the adoption will lead to more benefits than the conventional system
 
  
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::*R.Chikowo [Rejoinder]: so Christian, your very last statement is nice to listen to, but I think that’s the way to go. But I will say response farming with CA will not work because long term benefits of CA are based on sustained utilization of CA on a long duration. Now if we do responsive farming today, the next year CA, we do tillage, we will forfeit those long term benefits. So in order words, we should operate in the sense that if a long term duration like 10 years in sustaining the system would give a great positive result, I thought that would be the choice and that for responsive farming, CA is probably kind of . see where the problem is with seed variety, telling farmers to grow a seed and variety, next year like this kind of variety is directly related to season and quality. Now taking that sequence to CA, I think we will be going too far. Therefore  my suggestion would be lets forfeit  those negative season and too much rain fall. But we know in a 10 year 20 year duration the overall picture is that CA would have positive outcomes. I think we adopt this concept of responsive farming in a domain that doesn’t quite fit. If you understand what am talking about. Thank you.
+
'''[[File:June_3_Seminar_presentations_Lieven_3June_ESAPPT.pptx|Ex-ante impact assessment of Africa RISING seed and fertilizer technologies in Tanzania with trade-off analysis model for mutli-dimensional impact assesment ]] - Dr. Lieven Claesse (IITA)'''
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+
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::*F. Muthoni [Additional Feedback]: Yeah, maybe I can boost what Regis mentioned, I think those are good ideas that we are putting into consideration and recently I published a paper on the CA system in  Malawi they were seeing a similar result that if CA is working mostly in many places . since it is mostly giving yield advantage during the time when there is drought , and the drought is carried in 2 in 5 years, they were seeing  that it could be one of the impediment to the adoption because farmers are looking for short term gain, but CA have a long term benefit so those are the things we need to      in the message that we take to farmers. So if I can go back to Mateete question, he said that the input and the high variation for Christian and from my part showed very narrow yield gain.
+
*Download presentation from link in the title above.
−
 
 
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::*C. Thierfelder [Additional feedback]: Yeah, maybe what we do in our trial s I think is under semi controlled conditions. With relatively well managed plots, with inputs use that is at a certain level, with improved varieties and no wonder we see a yield gain that is somehow a little bit higher when you see    assessment , when you look at all types of farmers, and am sure within all this farmers that we have  reach are all farmers that get similar yield differences  but there others where you don’t see any difference at all. So I think you have to see a little bit in the context from where we come from, where  we measure and how do we exterprolitate  to a larger area . but I think that will be my response, I don’t know if francis has a better answer to that.
 
−
 
 
−
 
 
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*M. Bekunda [Question]: I think since we are here I can show you on this graphs what I mean. Because for example like that graph 22, the  yield range from 3.8 to 4, that’s a different from 0. 2 and to me, that difference in the two is not going to work if we will use them for bigger areas because the differences are much bigger.
 
−
 
 
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:*F. Muthoni [Response]: Ah ok now I get, if you look at the partial dependency yield it is something that we call Y harts, its like a standardized unit, its not the actual absolute yield, it is an index showing the response once you mute other variables. So If you look at the partial dependency I have said Y harts its not like where we measure yield like kilograms and per hectare. I think that is where the confusion is, but we can discuss the issues of machine learning. Thank you!
 
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*I. Hoeschle-Zeledon [Question]: I was first tempted to withdraw my question because  Regis own is actually like the one I wanted to ask but I feel it is too important not bring it up again. So how do we communicate our research findings to farmers? This is not only relevant to this research finding of this research study but in general. Its very nice to see that we have increased our scientific publication for the last 2 years, that’s good and that’s one output from our program and this is expected of us because we are scientists, so good. But we have to do something for the farmers, eventually something has yo change on the ground and with scientific  publication alone, this will not happen, that is why we all have to think seriously on how we use our remaining project  for a life time to produce product that are useful for farmers for extension. Eventually we will have the handbook and not everything will be handbook and not  every  body  will read the hand book so we need simple products  that are targeted to extension, people and to farmers in their languages that communicate our research findings so that they can adopt these technologies and so that they can adopt these recommendations that are coming on. So I want all of us to think seriously how we are going to do that over the coming months. Thank you.
 
−
 
 
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:*C. Thierfelder [Response]: I think I entirely agree with you and what you are saying , I feel that is very important. For us, we have presented to you and the rest of the group more or less the process we have used to get to this stage. And what is the next step actually is what can we draw out of the complex message that we have to a simplified message, amplify some maps, tell farmers in the majority if you are in this area that you will get a better return if you do CA. that’s some simple message we will like to get out but first of all, we have to get to this stage.
 
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*I. Hoeschle-Zeledon [Comment]:Yes, some of us are already advanced , but they have something clear that they have concluded research activities, they have published papers, so now what next can we produce so that eventually there is a change in the lively hood of these farmers.? Because this  change  will not happen from scientific publications, no.  and these messages have to be simple, I mean most of us who had the chance to join the meeting we had yesterday with the US AID Ghana mission, like they have realized that to those people there, we really, they are literates, they have good education, they have experience from all over the world but even for them, simple things are sometimes astonishing. So we can really take anything for granted especially when we talk to farmers and extension people. Ok that’s it yeah thank you.
 
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*P. Okori [Question]: Greetings to all of you, I hope you can hear me, I just wanted to ask one question. Maize in our region is generally be grounded they are different  maturity groups of this material, you have those that are a bit early more maturing and something is in the middle, most crops have that. Now, I believe that that crop        alone  influences the extent to which the benefits of the technologies  can be realized, may be I got you in the middle I heard you use DK products, anyway I don’t know what maturated group it is. But could you comment on that because generally maize production is going to be under all kind of materials. And broadcast recommendation as Regis was saying may not be necessary or put together may be the fine tuning of the plot level  which we are doing to bring that out, I was just wondering. Whether you look at those kind of things, over to you.  
 
  
−
:*C. Thierfelder [Response]:Yes Patrick that’s an excellent observation, for us, we have experimented long time now with variety by a management into action. It is an interesting aspect to look at, of course the variety and the maturity group is important  and depending on the rain fall, season that you expect in an area, growing the right variety is  the first and most important strategy. But if you combine that with a cropping system that is water saving and over the longer term improving soil quality, there is other side benefits like reduced erosion or so. This combination of bundling is actually helping to bring out more benefits than just from the variety.  And we have seen that under many cases where we did some varieties under  CA, as compared to different variety conventional agriculture . we get assistant benefits when you grow  both technologies together.
+
<b/>'''Discussion'''<b/>
 +
*Carlo. Lieven, why did you conduct the study using the LSMS data that expand across full of Tanzania and not TARBES data? while  the information is essentially the same, beside, data were collected among farmers in AR districts with and w/out AR technologies!
  
−
*P. Okori [Question]:I get that point that there is a very benefit, I was just wondering if the analysis you have taken care of that type of argument, when it helps you to become more      and the type of things Ingard is talking about, it becomes a little bit obvious. The reason am saying this is because almost every crop has the existing benefit that you can get is very much dependent on the extent to which the genotype can respond to the management you are putting in place. So if you miss out that bit of it and you lamp them it might be difficult to stretch the argument so that’s the question I was just asking. Thank you
+
:*Lieven. In general, it is ambitious, and it’s the assumption that we had to take. Since the big goal is upscaling, I thought it was a nice try to bring this to the national scaling with all the uncertain things, and there was an assumption we had to make.  But I would be happy to exercise with the TARBES data. However, TARBES data is still limited to those two study areas (Babati & Kongwa Kiteto) right?. .
  
−
:*F. Muthoni [Response]: Yes Patrick, that’s a good observation, maybe you can see the slide am showing, this is absolute and it is something that we have made many call from your fellow breeder Peter to characterize  the varieties. So if you see the slide, you can see that we have different types of varieties and we have considered maturity stage. What we did when we did the mothering is that we take an average of days of maturity like for example if you see ZM varieties it takes 110 to 120 days. So areas which are below that we mask out, like you can see the map, for DK, those places which are the     are the one that doesn’t meet the expectation of the breeder. So is something that we have to put in context. If I show you the map map for SC719  which is late is late maturing in the highlands. The zone that is suitable for that variety is very small so we accordingly remove it because that is very key.
+
*Mateete. Yes, Lieven. It is essential for AR project before you go wide nationally. So, if the TARBES data are used to do the second analysis, I think it would be interesting for the project. Because scaling out even within Babati, we work in fewer areas than the full descriptions or even in Kongwa Kiteto. So, the TARBES data would work out okay.
 +
:*Carlo. Lieven, why is this study called ex-ante impact assessment? It looks like scenario modeling to me.
  
 +
*Lieven. I don’t know the entail difference Carlo, but we did scenarios regarding the realist yields. For the impact assessment, we refer to the actual adaption rates or predictive adoption rates.
  
−
:*C. Thierfelder [Response]:I would like to say I final thing. You know we have looked yield as a factor in our decision making and CA is actually less promoted generally for it yield benefits . most of the benefits come from other things that you do when you do CA. these are the environmental benefits like the less degredation , there is soil health benefits and over time there may also be yield benefits, but in the first year there is no practical yield benefit. There are social benefits like reduced labor on planting in many cases, for example if you don’t want to do ridges anymore and other things, so al this entry point are our where about, we do look at them when we look at economics, this is more bio physical and it has been more strategic and bio physical. Actually another step should be the socio economic mothering . a couple of years ago we did that with IFPRI  in  Zambia and we looked at what would be the monitoring benefits if you adopt the CA, but that is not part of this presentation today.
+
*Comment. Mateete. There is a need to put all the presentations together and stragetical plan on how we could produce a project legacy docuement from the PPT slides presented. It can be of interest, so there is a need to look at the presentations again and discuss further.

Latest revision as of 03:59, 23 June 2021

Africa RISING ESA Project Partners Meeting
3 June 2021
Virtual via Ms TEAMS[edit | edit source]

Participants

  1. A. Kimaro, ICRAF
  2. B. Zemadim, ICRISAT
  3. C. Azzarri, IFPRI
  4. C. Thierfelder, CIMMYT
  5. D. Mgalla, IITA
  6. E. Swai, TARI-Hombolo
  7. F. Kizito, IITA
  8. F. Muthoni, IITA
  9. G. Fischer, IITA
  10. I. Dominick, WorldVeg
  11. I. Hoeschle-Zeledon, IITA
  12. J. Groot, WUR
  13. J. Kihara, Bioversity-CIAT
  14. J. Manda, IITA
  15. J. Odhong, IITA
  16. L. Claessens, IITA
  17. M. Bekunda, IITA
  18. M. Mutenje, IITA (consultant)
  19. P. Okori, ICRISAT
  20. R. Chikowo, MSU


Agenda

  • Ex-ante impact assessment of Africa RISING seed and fertilizer technologies in Tanzania with trade-off analysis model for multi-dimensional impact assessment- Dr. Lieven Claessens (IITA)
  • Understanding the potential contributions of ISFM to various sustainable intensification impact domains - Dr. Job Kihara (CIAT)

File:JR 3June ESAPPT.pptx - Dr. Job Kihara (CIAT)

  • Download presentation from link in the title above.

Discussion

  • Irmgard. How can the difference in adoption of ISFM components between farmers in Babati and K/K be explained? Which are the components that the low adopters do adopt?
  • Job. Well, Irmgard, for the difference in adoption of the ISFM components, it is something that we have considered by looking at various components, and how the intensity of the ISFM influence multiple indicators. For example, in Babati, we presented those farmers with one component and their percentage ( those who are practicing ISFM and the practiced technology). In the writeup, we specified rates in portion, e.g., 65% of one ISFM group are all intercropping. We have those types of tendencies explained in the text and the presentation of the figures and the ISFM components.
  • Irmgard. Okay, may you explain why are Babati farmers adopting more components than Kongwa and Kiteto farmers?
  • Job. Well, because Babati is a high-potential environment, they already have adopted improved varieties. Therefore, penetration of improved varieties is very high in Babati, and many of the farmers in the area, at least 75% > of the farmers practice intercropping. For Kongwa and Kiteto, the situation can be fascinated by the nature of the environment and may not be high penetration of improved varieties. It can probabilistically explain the different uses of ISFM components.
  • Irmgard. So, from the farmer, the need for these ISFM components would be high in Kongwa Kiteteo than Babati?.
  • Job. I think so. But for the scaling perceptive, there might be less information going in Kongwa Kiteto relatively to Babati. In Babati, we would expect to see farmers practicing five components, but, in this case, we have farmers with only two or three components. I still feel there should be a push to make farmers go to four to five. But Yes, Kongwa Kiteto will be essential to make interventions on including these practices.
  • Job. I was shocked to find 0% components from 22% of farmers, any comments from someone in Kongwa Kiteto?
  • Antony. To add up, the low adoption in Kongwa Kiteto can be because the ISFM components that go viral within the short period in this area is the water aspect, and depending on the technology, for example, the tied ridges has been a need in this area, for other technology like Chororo; the planting phasing goes very fast. Therefore, the easier of implementing the technology that captures water, farmers are quickly picking up. It can be one of the ISFM components that can bring up many things, but to what extent has that been captured and under which technology? is something we can look at the data set that Job has.
  • Shitindi. In addition to Dr. Job and Antony, the other factors can be access to the market. Babati has more access to the market of the ISFM technologies, primarily for the Legumes (pigeon pea) than farmers in Kongwa Kiteto. Since this has something to do with the financial muscles, and there are investments, the rate of the reinvestments with the ISFM has something to do with the adoption rate.
  • Irmgard. Thanks, Shitindi, but do you mean input or output market?
  • Shitindi. I mean output market, Irmgard.
  • Swai. To contribute to Shitindi, Antony & Job’s comments on the issue of adaption, adoption is complicated because some of the ISFM technologies, like tied ridges, require massive labor. Hence the rate of adaptation cannot be similar when we compare the nature of labor requirements. Technologies such as mechanization are labor-saving. Currently, there is another survey going on where Kongwa is a part of it. The Kongwa Kiteto team may note that the current level of intensification, maize and pigeon pea component is becoming common like Babati. If we refer to the adoption level of ISFM using pigeon pea in Kongwa Kiteto, we can note the variations. The soil water technology is labor-intensive on one side, and mechanization technology is imperative for many farmers to pick up the technology.
  • Carlo. Job, how can the differences in yields computed among AR farmers be attributed to the AR project and not other intervening/confounding factors?
  • Job. It is unclear if it applies in this case but will use in the survey we are conducting. We have tried to categorize our intervention farmers and farmers whom we did not have an intervention with (the control farmers), and we have records of the list of all our farmers and use the data to set the proportion of the farmers that we will interact with and farmers how are not part of us from the project site to show the difference between those who have interacted with the AR project vs. those who have not. On the other hand, we want to access the initial farmers with the project and identify farmers who had been/ not been considered to see if there will be attributions that can be done. We are doing this with Julius Manda
  • Shitindi. Job, from the presentation, most of the domains are presented by the number of the ISFM components adopted by the farmers, but labor requirements also increase with the number of components adopted. We may establish the label of combinations of these components that people can see the most rewarding in terms of labor investment?
  • Job. I will consider that as something that we can interact with the data set and see how we can enrich our publication with what you have contributed to us.


File:June 3 Seminar presentations Lieven 3June ESAPPT.pptx - Dr. Lieven Claesse (IITA)

  • Download presentation from link in the title above.

Discussion

  • Carlo. Lieven, why did you conduct the study using the LSMS data that expand across full of Tanzania and not TARBES data? while the information is essentially the same, beside, data were collected among farmers in AR districts with and w/out AR technologies!
  • Lieven. In general, it is ambitious, and it’s the assumption that we had to take. Since the big goal is upscaling, I thought it was a nice try to bring this to the national scaling with all the uncertain things, and there was an assumption we had to make. But I would be happy to exercise with the TARBES data. However, TARBES data is still limited to those two study areas (Babati & Kongwa Kiteto) right?. .
  • Mateete. Yes, Lieven. It is essential for AR project before you go wide nationally. So, if the TARBES data are used to do the second analysis, I think it would be interesting for the project. Because scaling out even within Babati, we work in fewer areas than the full descriptions or even in Kongwa Kiteto. So, the TARBES data would work out okay.
  • Carlo. Lieven, why is this study called ex-ante impact assessment? It looks like scenario modeling to me.
  • Lieven. I don’t know the entail difference Carlo, but we did scenarios regarding the realist yields. For the impact assessment, we refer to the actual adaption rates or predictive adoption rates.
  • Comment. Mateete. There is a need to put all the presentations together and stragetical plan on how we could produce a project legacy docuement from the PPT slides presented. It can be of interest, so there is a need to look at the presentations again and discuss further.
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