Macro-level interventions: B
byANH Academy
Academy Week Research Conference
 | Agriculture, Economics, Food Systems
Date and Time
From: 2 July 2020, 14:15
To: 2 July 2020, 15:15
Location
Country: United Kingdom
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Intro animation:

Macro-level interventions studies at ANH2020

 

Session recording:

ANH2020: Macro-level interventions B

 

Speakers and presentations:

  • Session chair: Kafui Adjaye-Gbewonyo, University College London
    @AdjayeKafui @ucl

  • Edward Joy, London School of Hygiene & Tropical Medicine
    @edward_joy1 @LSHTM
    First report of primary findings from the AHHA trial: can selenium deficiency in rural Malawi be addressed through consumption of maize flour biofortified with fertilizers?
    Presentation | Slides

  • Gregory Cooper, SOAS University of London 
    @SOAS
    Navigating the nutrition-based trade-offs arising from horticultural aggregation schemes: a system dynamics approach 
    Presentation | Slides

  • Molly Muleya, University of Nottingham
    @UniofNottingham
    Biofortification of staple crops with selenium fertilizers: speciation and bioaccessibility
    Presentation | Slides

  • Peter Mwangi, University of Nairobi
    @uonbi
    Effects of area yield index insurance on food security: Case study of Njoro sub-county, Kenya 
    Presentation | Slides

Abstracts:

First report of primary findings from the AHHA trial: can selenium deficiency in rural Malawi be addressed through consumption of maize flour biofortified with fertilizers?

Edward J. M. Joy1

Alexander A. Kalimbira2

Dawd Gashu3

Elaine L. Ferguson1

Joanna Sturgess1

Alan D. Dangour1

Leonard Banda2

Gabriella Chiutsi-Phiri4

Zione Kalumikiza2

Elizabeth H. Bailey5

Simon C. Langley-Evans5

R. Murray Lark5

Kate Millar5

Scott D. Young5

Limbanazo Matandika6

Joseph Mfutso-Bengo6

John C. Phuka6

Blessings H. Likoswe6

Felix P. Phiri5,7

Jellita Gondwe8

E. Louise Ander9

Nicola M. Lowe10

Patson C. Nalivata2

Martin R. Broadley5

Elizabeth Allen1

 

1Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine

2Lilongwe University of Agriculture and Natural Resources, Malawi

3Center for Food Science and Nutrition, Addis Ababa University, Addis Ababa, Ethiopia

4Lilongwe University of Agriculture and Natural Resources, Malawi

5School of Biosciences, University of Nottingham, UK

6School of Public Health and Family Medicine, College of Medicine, University of Malawi, Blantyre, Malawi

7Department of Nutrition, HIV and AIDS, Ministry of Health, Lilongwe, Malawi

8Public Health Institute of Malawi, Community Health Sciences Unit, National Nutrition Reference Lab, Lilongwe, Malawi

9Inorganic Geochemistry, Centre for Environmental Geochemistry, British Geological Survey, Nottingham, UK

10UCLan Research Centre for Global Development, University of Central Lancashire, UK

Introduction

Selenium (Se) deficiency is widespread in Malawi1 where most crops are grown on highly-weathered, tropical soils with low concentrations of plant-available Se. Concentrations of Se in maize, the staple cereal of Malawi, can be increased through application of Se fertilisers2 – a process known as agronomic biofortification – and this may contribute to alleviating deficiencies3. The Addressing Hidden Hunger with Agronomy (AHHA) trial was recently completed with the aim of establishing the efficacy of agronomic biofortification for improving Se status in rural Malawi4. The primary results of the AHHA trial will be presented here for the first time.

Methods

A double-blind, randomised, controlled trial was conducted in rural Kasungu District, Malawi. We hypothesised that consumption of maize flour agronomically-biofortified with Se would increase serum Se concentration. A study area of ~1200 households was defined in Wimbe Traditional Authority, where Se deficiency is widespread1. 180 women of reproductive age (20–45 years) and 180 school-age children (5–10 years) were recruited and randomly assigned in a 1:1 ratio to receive either maize flour enriched through agro-biofortification with Se or control flour not enriched with Se. Households received adequate flour to meet household requirements (330 g/capita/day) for 12 weeks, with distributions every 2 weeks. Non-participant households received control-equivalent flour to reduce chances of inter-household sharing. Blood samples were drawn from participants at baseline (prior to flour distribution) and end-line (after 8-9 weeks of flour distribution). Samples were centrifuged in the field to extract serum which was aliquoted and frozen. Dietary data for adult women was collected by interactive 24-hour recall. Serum Se concentrations will be measured by inductively coupled plasma mass spectrometry in February 2020. Data will be analysed on an ‘intention-to-treat’ basis, with analysis of covariance used to estimate a mean difference in Se status between the control and agronomically-biofortified trial arms.

Findings

The trial was successfully completed in September 2019 and the results will be known in March 2020 when the trial is unblinded. Primary findings will be presented here for the first time. A total of 360 participants were recruited and 345 (96%) completed the trial, defined as providing blood samples at baseline and endline. The low drop-out rate was largely thanks to intensive community sensitisation efforts led by the research team together with local government and community partners. Community fears and rumours arose, e.g. fears that blood sampling was linked to witchcraft, or that consumption of the maize flour would cause infertility. However, these were successfully addressed through community events such as visits to the maize production site and dish sharing, and managed communication strategies including regular meetings arranged by the Trial Manager and community representatives, and the development of a Frequently Asked Questions document which all Research Assistants were familiarised with before going to the field. In total, >200 tonnes of maize flour were distributed during the trial, with >99.5% accuracy in terms of correctly allocated flour. A total of 695 monitoring visits were conducted when adherence to treatment and adverse events such as diarrhoea were captured.

Conclusion

The AHHA trial will determine whether agronomic biofortification may be an effective approach to address Se deficiency in rural Malawi. Previous studies have suggested it may be a cost-effective strategy with the potential to reach poorer and marginalised populations who lack access to diverse diets or foods fortified at processing stage5. A national policy of agronomic biofortification with Se has been implemented in Finland since 1984, successfully addressing Se deficiency in the population. The AHHA trial will provide important evidence on the potential to alleviate Se deficiency through agronomic biofortification in the context of rural Malawi.

References

Phiri FP, Ander EL, Bailey EH, Chilima B, Chilimba AD, Gondwe J, Joy EJ, Kalimbira AA, Kumssa DB, Lark RM, Phuka JC. The risk of selenium deficiency in Malawi is large and varies over multiple spatial scales. Scientific reports. 2019 Apr 25;9(1):6566.

Chilimba AD, Young SD, Black CR, Meacham MC, Lammel J, Broadley MR. Agronomic biofortification of maize with selenium (Se) in Malawi. Field Crops Research. 2012 Jan 18;125:118-28.

Alfthan G, Eurola M, Ekholm P, Venäläinen ER, Root T, Korkalainen K, Hartikainen H, Salminen P, Hietaniemi V, Aspila P, Aro A. Effects of nationwide addition of selenium to fertilizers on foods, and animal and human health in Finland: From deficiency to optimal selenium status of the population. Journal of Trace Elements in Medicine and Biology. 2015 Jul 1;31:142-7.

Joy EJM, Kalimbira AA, Gashu D, Ferguson EL, Sturgess J, Dangour AD, Banda L, Chiutsi-Phiri G, Bailey EH, Langley-Evans SC, Lark RM. Can selenium deficiency in Malawi be alleviated through consumption of agro-biofortified maize flour? Study protocol for a randomised, double-blind, controlled trial. Trials. 2019 Dec;20(1):1-9.

Joy EJM, Kumssa DB, Broadley MR, Watts MJ, Young SD, Chilimba ADC, Ander EL. Dietary mineral supplies in Malawi: spatial and socioeconomic assessment. BMC nutrition. 2015 Dec;1(1):42.

Navigating the nutrition-based trade-offs arising from horticultural aggregation schemes: a system dynamics approach

Cooper, G.S1

Rich, K.M2

Shankar, B3

Kadiyala, S4

Ratna, N5

Rana, V6

Nadagouda, S.B6

Mohammad, A7

Choudhury, D. K7

Sadek, S6
 

1SOAS, University of London

2International Livestock Research Institute (ILRI)

3University of Sheffield 
4London School of Hygiene and Tropical Medicine (LSHTM)
5Lincoln University, Christchurch, New Zealand
6Digital Green, Patna, India

7Bangladesh Agricultural University (BAU), 

Introduction

Despite being India’s sixth largest state in terms of horticultural production, the average estimated consumption of fruits and vegetables in Bihar sits at ~132 grams/capita/day (NSSO, 2013). Rural demand is supressed by value chain fragmentation, publicly inaccessible cold storage facilities, price inflation from commission agents and the preference of farmers to supply urban markets. To overcome value chain fragmentation, the Non-Governmental Organisation ‘Digital Green’ has aggregated the fruit and vegetable production of over 28,000 farmers in Bihar since 2016. However, the ability of the ‘LOOP aggregation service’ to achieve win-win scenarios for livelihood and nutritional outcomes remains uncertain.

Methods

To this end, we develop a system dynamics modelling approach to (i) simulate the current implications of aggregation services on the availability and affordability of fruits and vegetables in small, rural horticultural markets in Bihar; (ii) explore future scenarios to make the scheme more nutritionally sensitive; and (iii) evaluate any trade-offs between improving F&V availability and affordability, and any potential feedbacks on the revenues and profits generated by farmers. We describe how the modelling framework integrates various sources of quantitative and qualitative data, including our initial value chains assessment, the quantitative analysis of the ‘LOOP dashboard’ datasets and a series of group model building (GMB) sessions conducted with stakeholders from across the horticultural value chain of Bihar. The overarching structures and processes of our model are then briefly introduced, before demonstrating that the model produces the ‘right’ quantitative behaviours for the ‘right’ qualitative and structural reasons (Barlas and Kanar 2000). Lastly, we describe the main modelling scenarios alongside the techniques used to visualise the nutritional implications and trade-offs along the value chain.

Findings

We compare the nutritional and livelihood implications of the baseline ‘LOOP’ evolution scenario against various internal (i.e. ‘LOOP’) and external (i.e. across the wider enabling environment) nutritionally sensitive scenarios. Whilst the baseline scenario is found to generate various producer-facing benefits, such as reduced transport costs and improved horticultural profits, the model suggests that more proactive scenarios are required to transition producers away from large urban markets and towards smaller, more nutritionally vulnerable markets. We find that scenarios such as improved cold storage utilisation and transport subsidies may help to improve the availability of fruits and vegetables in small rural markets, albeit involving trade-offs with other value chain metrics, including farmer profits and retail prices. The trade-off landscapes generated by the modelling ultimately pinpoint pathways and approaches to leverage aggregation systems towards futures that achieve win-win scenarios for both horticultural producers and consumers.

Conclusion

Building on more traditional value chain approaches, system dynamics modelling allows the effects of nutritionally sensitive value chain upgrades to be hypothesised and evaluated within a virtual environment. Ultimately, whilst aggregation schemes can be internally levered to improve the supplies of fruit and vegetable to rural markets, our simulations suggest that there is only so much that can be achieved from the inside. Therefore, the full nutritional potential of aggregation and the ability to overcome various producer-consumer trade-offs are maximised with complimentary changes to the wider enabling environment.

References

NSSO. (2013). Household Consumer Expenditure, NSS 68th Round Sch1.0 Type 1: July 2011—June 2012. New Delhi, India.
Barlas, Y. and Kanar, K. (2000). Structure-oriented behaviour tests in model validation, The 18th International System Dynamics Conference, p.19

Biofortification of staple crops with selenium fertilizers: speciation and bioaccessibility

Molly Muleya1

Scott Young1

Ivy Ligowe2

Martin Broadley1

Edward Joy3

Prosper Chopera4

Elizabeth Bailey1

1University of Nottingham, United Kingdom

2Department of Agricultural Research Services, Malawi

3London School of Hygiene and Tropical Medicine

4University of Zimbabwe

Introduction

Selenium is an essential micro-nutrient incorporated into at least 25 selenoproteins involved in vital metabolic processes in the human body. Sub-optimal dietary intake of Se is common in many parts of the world where people rely on agricultural products grown on Se deficient soils. Agronomic biofortification, through the application of Se-enriched fertilizers to soils and crops is an important strategy to introduce Se into the food system. To achieve the intended health effects, the applied Se must be biotransformed into bioaccessible Se compounds in the edible portions of the crops.

Methods

To resolve the speciation and bioaccessibility of applied Se in crops, it is important to discriminate between applied Se in fertilizer and native Se from soil. Isotopic labelling is an important way to trace the fate of an element subject to geochemical and biological processes. Isotopically labelled 77Se, as potassium selenate, was added to soils at a rate of 20 gha-1 in a field trial at Chitedze Research Centre in Malawi. Three crops of dietary importance in Malawi (maize, cowpea and groundnut) were planted in December 2016 and harvested in May 2017. Total Se concentrations were determined using a triple quadrupole ICP-MS operating in oxygen mode with mass shifting of 77Se to m/z93 (77Se16O) and 80Se to m/z96 (80Se16O) to minimise interferences from 76Ge hydride and 40Ar2. The standardised INFOGEST in-vitro digestion method was used to determine Se bioaccessibility. This method was developed by international consensus and designed to improve reproducibility and comparability of results across different laboratories. Se speciation was determined using anion exchange chromatography on an HPLC coupled to ICP-MS after samples were subjected to proteolytic and lipolytic digestion. An optimised gradient elution programme was used to detect Se species namely: selenite, selenate, selenomethionine(SeMet), selenocystine(SeCys) and Se-methyl-selenocysteine(SeMeSeCys).

Findings

The contribution of Se from fertilizer and soil (native Se) resulted in total Se concentrations ranging between 153 – 865 µgkg-1. The addition of Se fertilizer increased Se concentration by an order of magnitude such that Se from fertilizer contributed 88-97 % of the total Se. In general, total Se concentrations were in the order maize < cowpea, groundnut. As Se is mainly accumulated in proteins, legumes might be able to accumulate more Se in the grain than cereals due to their higher protein content. About 57.6–93.2 % of the applied Se was extracted for speciation and more than 90 % of the extracted Se was in organic form. The main form of applied Se was SeMet with an abundance of 92 % in maize, 63.7 % in cowpea and 85.2 % in groundnut. In addition to SeMet, cowpea also contained about 32.7 % of SeMeSeCys which is one of the most potent anticarcinogenic Se compounds. An average bioaccessibility of 72.3 % was observed with no significant differences among the different grain types. The formation of organic forms of Se after fertilization of staple crops is crucial as organic forms of Se are more bioaccessible than inorganic forms.

Conclusion

Se from soil and fertilizer were both biotransformed into bioaccessible organic Se species and an increase in total Se concentration was achieved by Se fertilization. Maize is an important staple crop in Malawi such that the inclusion of Se in fertilizers as a national strategy could sustainably improve Se status of the population. As plant-based protein is increasingly becoming important, Se biofortified legumes could offer an important source of Se with health benefits that go beyond improving Se status. The evidence from this study could be used as a foundation to establish dietary intakes of Se-biofortified crops for improved health.

References

Bevis LE. 2015. Soil-to-human mineral transmission with an emphasis on zinc, Se, and iodine. Springer Science Reviews 3:77-96.

Brodkorb A, et al. 2019. INFOGEST static in vitro simulation of gastrointestinal food digestion. Nature Protocols 14:991-1014.

Broadley MR, Alcock J, Alford J, Cartwright P, Foot I, Fairweather-Tait SJ, Hart DJ, Hurst R, Knott P, McGrath SP. 2010. Se biofortification of high-yielding winter wheat (Triticum aestivum L.) by liquid or granular Se fertilisation. Plant and Soil 332:5-18.

Chilimba AD, Young SD, Black CR, Meacham MC, Lammel J, Broadley MR. 2012. Agronomic biofortification of maize with Se (Se) in Malawi. Field Crops Research 125:118-128.

Combs Jr GF. 2000. Food system‐based approaches to improving micronutrient nutrition: The case for Se. Biofactors 12:39-43.

Kirby JK, Lyons G, Karkkainen MP. 2008. Se speciation and bioavailability in biofortified products using species-unspecific isotope dilution and reverse phase ion pairing− inductively coupled plasma− mass spectrometry. Journal of Agricultural and Food Chemistry 56:1772-1779.

Mathers AW, Young SD, McGrath SP, Zhao FJ, Crout NMJ, Bailey EH. 2017. Determining the fate of Se in wheat biofortification: an isotopically labelled field trial study. Plant and Soil 420:61-77.

Poblaciones MJ, Rodrigo S, Santamaria O, Chen Y, McGrath SP. 2014. Se accumulation and speciation in biofortified chickpea (Cicer arietinum L.) under Mediterranean conditions. Journal of the Science of Food and Agriculture 94:1101-1106.

Effects of area yield index insurance on food security: Case study of Njoro sub-county, Kenya

Peter Mwangi1

1University of Nairobi 

Introduction

Achieving food security in developing countries remains a challenge especially countries whose economy is largely agricultural dependent. Nevertheless, the escalating impacts of climate change are compromising the situation due to dwindling agricultural output. This has necessitated institution of a number of policy reforms and initiatives guide to effectively address food insecurity problems and ensure a healthy labour force. Lately, a number of pilot programs for index insurance that has increased massively. Though promoted as a prospective tool to suffice production risks associated with weather variability, its uptake evidence is limited with little empirical evidence on the effects and overall performance.

Methods

The study adopted a dynamic household model of likelihood and farm decision. Both descriptive and econometric analysis. Probit model was used to evaluate determinants of participation while Endogenous Switching Regression was used to estimate the effects of effect of AYII on food security. The study adopted a simple random sampling approach in selecting both the area of study and the category of farmers. The area was purposively selected for the study while a systematic random sampling method was employed to draw a pool of target households from a sampling frame where starting from a random point but with a fixed, periodic interval, a sampling interval of every nth household followed by establishing whether the insured (treatment) or not (control group). The same process was repeated until a sample of 233 was surveyed. Primary data at household level was collected using self-administered questionnaires where a sample of 233 households were interviewed out of which 47.64% were insured. Pilot testing was conducted to determine reliability and validity of the survey instrument. Finally, diagnostic tests for multicollinearity, heteroscedasticity, sample selection bias and falsification test was conducted to ensure that estimated are unbiased.

Findings

Although insurance was rolled out in 2017, the trend for uptake has been low and declining. For example, 2017 had 32% insured farmers while 2018 had 28% with only 11% reenrolling and 2019 had 29%. Decline was largely due to basis risk (non-compensation) and insurance not meeting farmers’ expectations or its unavailability. Results showed that education, non-farm income, land under maize, land tenure, access to agricultural credit, distance to motorable road, age and group membership significantly influenced farmers' decision to adopt crop insurance. Similarly, for food security, household size and distance to market had a significant positive effect while farming systems, irrigation and land size had a significant negative influence. In terms of the effect of participating in AYII and the vulnerability of being food insecure, the study revealed that participating in crop insurance reduces the food security index thus making households better off with insurance than without. Treatment effects of the treated indicated that if the adopter had not adopted, their food security index would have increased making the food insecure. Similarly, if non-adopters had adopted, their food insecurity index would have reduced making them more food secured. Therefore, insurance has a positive effect on improving food security.

Conclusion

Generally, the number of households insuring their crops is marginally small and declining. This trend is to a large extent worsen by the fact that most of the farmers find it hard to comprehend the concept of insurance, partly due to the complex form of insurance or due to insufficient knowledge on the potential benefits and opportunities associated with adoption. This indicates that despite awareness being vital, training is required to encourage adoption for sustainability of the scheme. In addition, there was minimal or little engagement by smallholders to offer responses concerning the type of insurance products offered and preference.

References

Adhikari, S., Knight, T. O., & Belasco, E. J. (2012). Evaluation of Crop Insurance Yield Guarantees and Producer Welfare with Upward-Trending Yields. Agricultural and Resource Economics Review, 41(3):367–376.

Ali, A. (2013). Farmers’ Willingness to Pay for Index-Based Crop Insurance in Pakistan: A Case Study on Food and Cash Crops of Rain-fed Areas. Agricultural Economics Research Review, 26(2):241–248.
Ashimwe, O. (2016). An economic analysis of the impact of weather index-based crop Insurance on household income in the Huye District of Rwanda (No. 634-2017-5863).

Carter, M., Janvry, A., Sadoulet, E., & Sarris, A. (2014). Index-Based Weather Insurance for Developing Countries: A Review of Evidence and a Set of Propositions for Up-Scaling. Development Policies Working Paper No. 111.
Cole, S., Bastian, G., Vyas, S., Wendel, C., & Stein, D. (2012). The effectiveness of index-based micro-insurance in helping smallholders manage weather-related risks. London: EPPI-Centre, Social Science Research Unit, Institute of Education, University of London, 59.
Collier, B., Skees, J., & Barnett, B. (2009). Weather Index Insurance and Climate Change: Opportunities and Challenges in Lower-Income Countries. The Geneva Papers, 34(3):401–424.
Government of Kenya (GoK). (2017), Medium Term Expenditure Framework, Government Printers, Nairobi.
Guo, W. (2015). Farmers’ Perception of Climate Change and Willingness to Pay for Weather Index Insurance in Bahunepati, Nepal. Himalayan Research Papers Archive, 9(1), 1–33.
Kinyua, J. (2004). Towards achieving food security in Kenya assuring food and nutrition security in Africa by 2020: Prioritizing action, strengthening actors, and facilitating partnerships April 1.3, 2004. Kampala, Uganda.
Kumar, D. S., Barah, B. C., Ranganathan, C. R., Venkatram, R., Gurunathan, S., & Thirumoorthy, S. (2011). An analysis of farmers’ perception and awareness towards crop insurance as a tool for risk management in Tamil Nadu. Agricultural Economics Research Review, 24(347-2016-16879), 37-46.
Lokshin M, Sajaia Z (2014). Maximum likelihood estimation of endogenous switching regression models. The Stata Journal.2004;4:282-289.
Ministry of Agriculture (2015), Kenya Agricultural Insurance and Risk Management Program.
Nahvi, A., Kohansal, M. R., Ghorbani, M., & Shahnoushi, N. (2014). Factors affecting rice farmers to participate in agricultural insurance. Journal of Applied science and Agriculture, 9(4), 1525-1529.
Njue, E., Kirimi, L., & Mathenge, M. (2018). Uptake of Crop Insurance among Smallholder Farmers: Insights from Maize Producers in Kenya.
Olila, D. O., & Pambo, K. O. (2014). Determinants of farmers’ awareness about crop insurance: Evidence from Trans-Nzoia County, Kenya (No. 138-2016-1985).
Panda, A., Sharma, U., Ninan, K. N., & Patt, A. (2013). Adaptive capacity contributing to improved agricultural productivity at the household level: Empirical findings highlighting the importance of crop insurance. Global Environmental Change, 23(4), 782-790.
Sundar, J., & Ramakrishnan, L. (2013). A study on farmers’ awareness, perception and willing to join and pay for crop insurance. International Journal of Business and Management Invention, 2(1), 48-54.
Thornton, P. K., Jones, P. G., Ericksen, P. J., & Challinor, A. J. (2011). Agriculture and food in sub-Saharan Africa in a 4°C+ world. Philosophical Transactions. Series A,Mathematical, Physical, and Engineering Sciences, 369(1934), 117–36.

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