NRI (NUTRI-P-LOSS)

Natural Resources Institute (NRI), University of Greenwich 

NUTRI-P-LOSS: Nutritional Postharvest Loss Estimation Methodology

 

PI: Aurelie Bechoff, Keith Tomlins
Partners: Diego Naziri (International Potato Centre (CIP), Uganda), Eria Simba (National Agricultural Research Laboratories of the National Agricultural Research Organisation (NARO), Uganda), Brighton Mvumi (University of Zimbabwe), Suzanne Nielsen (Purdue University), Zhengyuan Zhu (Iowa State University), Bruno Tran (NRI, AfricaRice, Benin)
Start date: 5 December 2016
Duration: 24 months
Value: £249,920
Countries of research: Zimbabwe, Uganda, USA, and UK

Project summary

Postharvest losses and food security are important concerns in low and middle income countries (LMICs) and research in these areas has become a global priority.

Through APHLIS (African Postharvest sLosses Information System), methodologies and tools have been developed to estimate weight losses along postharvest value chains (VCs), and to a lesser extent economic values of the losses. However, no established methodologies exist for estimating postharvest nutritional losses.

Measuring nutritional losses along the VC will deepen quantitative and qualitative understanding of these losses, which is critical for understanding the contribution of agricultural interventions to nutrition improvement. Nutritional loss estimate methods and metrics can provide crucial information for improving food security strategies at local and global levels.

NUTRI-P-LOSS will develop a methodology to estimate nutritional postharvest losses (NPHLs) throughout the VCs of key staple food crops (maize, sweetpotato, cowpea) in LMICs. Using a combination of literature review, laboratory studies and field verification in Uganda and Zimbabwe, the project willl generate an estimation model to predict NPHLs. This predictive tool presented as an algorithm will be linked into the APHLIS online platform, enabling LMIC government policy makers, researchers and development partners to obtain estimates of the nutritional, physical and economic value of postharvest losses in their focal country and VCs.

Working alongside APHLIS, the project willl develop an open-access tool to predict NPHLs occurring at different VC activity stages. It will cover estimation of nutritional losses related to:

(1) physical weight losses (building on existing weight loss methodologies)

(2) other changes not associated with weight loss (i.e. quality losses).

The development of this predictive tool requires prior establishment of a novel and robust methodology to estimate NPHLs:

• Initially, physical weight losses will be estimated using literature data, and quality losses using both literature data and laboratory experiments.
• Secondly, the data will be converted into nutritional loss and fed into a predictive model based on an algorithm that will be used for NPHL estimates. Laboratory nutritional analysis where appropriate on food losses will be also carried out on selected samples.
• Field trials will be undertaken to validate the data obtained with the predictive model.
• Finally, the model will be fine-tuned and made open-access to users.

NUTRI-P-LOSS will focus on key-nutrient loss: macronutrients (protein, lipid, carbohydrate) and micronutrients considered the most important in terms of deficiencies (vitamin A, zinc, and iron).  

Generation of policy recommendations regarding nutritional implications of PHL reduction at different VC stages based on case studies with the tool.

The projects aims to develop a methodological approach for model dissemination to other countries/commodities (other cereals, pulses, and roots and tubers) in a cost-effective and sustainable way through the APHLIS network.

The multi-disciplinary team is composed of international experts from research institutes in the global North and South: Natural Resources Institute, UK; University of Zimbabwe; National Agricultural Research Organisation, Uganda; International Potato Center, and Purdue University, USA.

The NUTRI-P-LOSS methodology will create a predictive tool for estimating postharvest nutritional loss, bringing reliable and critical missing food security information to agriculture practitioners, development agents and policy makers.