Improving methods for estimating post-harvest losses

An overview of proposed methods

Carola FabiStatistics Division, FAO

19 September 2016

Outline

1) Global Strategy programme components

2) The problem – Proposed coverage of PHL surveys

3) An array of methods – Measuring PHL

4) Organising the work – Narrowing the survey focus

5) Next steps – Aligning data collection with existing

survey programme and policy frameworks

10-year global statistical capacity development program1) To address developing countries’ lack of capacity

to provide reliable statistical data on food and agriculture

more reliable, timely data

2) To provide a blueprint for long-term sustainable agricultural statistical systems in developing countries

sustainability, ownership

Why the Global Strategy

How does it work?

Country assessment

s

Sectors Plans

Technical Assistance Training

Addressing urgent needs

Research: cost-effective methods; guidelines

www.gsars.org

Why is research on statistical methods for PHL needed?

• Strong request from Sub-Saharan countries at the AFCAS (2011)• Malabo Declaration of African Union countries in 2014 • Global commitment to reduce food losses and waste - SDG 12.3

• Lack of availability and quality of PHL data• Solutions to methodological challenges need to be provided. Examples:

– Scope: where does post-harvest start and where does it end? Are we measuring quantitative, qualitative, nutritional losses?

– High level of expertise needed to assess damage– Multiple dimensions of PHL: commodities, technology, value chain, actors, ..– Statistical approach: sampling vs. case studies / value chain– Cost-effectiveness

Purposes of the research

• Main research question: Which methods for collecting data and measuring grain post-harvest

losses deliver the best results for the lowest cost?

• “Best results” :o Acceptable accuracy and precision ; o Statistical representativity (extrapolate results to region/country level)o Sustainability: countries understand the method and have the resources to carry-

out their own assessments regularly

• Benefits from ongoing initiatives on PHL or connected to it

• Final objective: produce guidelines for developing countries on cost-effective methods for estimating post harvest losses

The problem: spillage, breakage, etc.

The problem: spillage, breakage, etc.

The problem: biodeterioration, pests...

Desired end result of PHL measurements

Farm level

Processor level

Wholesale level

Retail level

VC/FSC Analysis

Chain of Actors, statistical units, surveys

An array of methods (1/3)

Harvesting

Stacking / stooking

Threshing/shelling, drying, transport, winnowing, cleaningStorage

Milling

Drying

Transportation

Storage

FARMERS PROCESSORS TRADERSRETAILERS

CONSUMERS

For each stage x activity, many possible designs and measurement methods:

Study designs• Probabilistic surveys• Experimental design• Econometric modelling• Rapid appraisal FAO-AGS /

GIZ method

Measurement• Crop-cutting• Laboratory analysis• Weigh-in/out• Visual scales

An array of methods (2/3)

The choice of the method ultimately depends on:• The stage at which losses should be measured: on-farm/off-farm storage,

farm operations, transport, etc.• The characteristics of the target population:

o variability + multiplicity of units => probabilistic surveys are more appropriateo Small number of large units: in-depth technical studies (lab analysis, experimental

designs) are appropriate

• The desired properties of the PHL indicatorso Statistical representativity: national, regional, none, etc.o Precision / accuracyo Reproducibility / updatingo Frequency

An array of methods (3/3)

…

• The level of technical, financial and human resources available in the country to carry-out the assessmentso Are there enough resources to carry out regular surveyso Can FLW be integrated into ongoing surveys

• Country needso What will be the use of PHL indicators: improving production

estimates, identifying food waste reduction strategies, etc.o Scope: which crops ? which stage ?o Desired frequency, level of representativity, precision, etc.

• At the harvesting level, villages are commonly the primary sampling units (PSU); holders are the secondary sampling units (SSU), and fields the ultimate (tertiary) sampling units.

• PSU’s may be clustered prior to sampling. Simple or stratified random sampling may be used to select the holders; for each chosen holder, a simple random sample of subplots within fields can then be selected.

• For threshing, cleaning, drying, transportation, and processing, villages are PSU’s and holders the SSU’s. For these levels, additional sampling stages will be necessary; for instance selecting a random sample of produce (maize cobs, etc.) to be threshed and observed for lost or damaged grain.

• For storage, villages are PSU’s, holders are the SSU’s, and the storage units (if there are more than one) within the holding are the ultimate sampling units. The villages (PSU’s) may be selected via simple random sampling, stratified random sampling, or may be sampled with probability proportional to size.

Sampling Methods: Farm level

• government distribution agencies, mills, marketing cooperatives, wholesale and retail traders.

• losses are to be estimated at the stage of transport, storage, processing, packing and distribution.

• For transport, storage and handling by market handlers, a sample of such handlers is to be selected and the required information collected.

• This stage calls for a two-stage stratified random sampling design with the market as the PSU, and the intermediary as the SSU.

• In the same way, random sample of mills/processing factories, may be chosen and the data collected.

• A single stage sampling design will suffice in the case of mills and similar units.

• For each sampled intermediary, different kinds of percentage losses are computed and then grossed-up to stratum/regional levels using the same techniques as in the threshing/shelling stage.

Sampling Methods: Intermediary level

• These agencies and other public distribution agencies should maintain detailed administrative records of grains received and dispatched.

• Food technology specialists working in these agencies are expected to collect samples of grains periodically, and record pertinent information, such as moisture content, insect and pest infestation and other causes of damage.

• Hence these agencies should therefore have readily available comprehensive data on levels of losses and their causes.

Sampling Methods: Government warehouses level

Testing cost-effectiveness. Field tests: overview

• General objective: Assess the relevance, quality and feasibility in developing countries of some of the main PHL data collection and measurement methods proposed in the Methodology Report

• Scope: farm and post-farm (processors, traders) level

• Partner institutions: National Statistical Offices and Ministries of Agriculture

• Countries: Ghana, Zimbabwe (or Zambia) and Malawi • Timing: Now-November 2016

Testing cost-effectiveness: methods 1/2

Ghana • Objective: Assess post-harvest on-farm losses for grains using a

survey-based approach, compare declarative method and objective measurements

• Method: – Small-scale crop-cutting survey based on the annual production survey for grains

– Losses measured by farm operation and for on-farm storage

– By inquiry (declarative) and by observation (measurement)

• Specificities: grain harvest from September to December

• Partner institution: National Statistical Office (NSO)

Testing cost-effectiveness: methods 2/2

Malawi • Objective: Propose and test a method to provide quick-estimates of PHL in-

between two survey rounds• Method:

– Desk-study based on past survey microdata (2009 and 2011)

– Quantification of the impact of key variables (humidity, temperature type of storage facility, etc.) on PHL

– Econometrics (linear modelling)

• Specificities: Malawi has experience in PHL assessments using probabilistic surveys. Two rounds (2009 and 2011) of farm-level data are available for a large sample of farms

• Partner institution: Ministry of agriculture

PHL guidelines: Workflow and process

2015

Feb

Gaps analysis

Apr

Literature review

Methodological report

June Mar

Field Test Protocols

Field Tests Report

Nov

2016

2017

Mar

Draft Guidelines

June

Final Guidelines

Narrowing the survey scope

Define the needed data:• Review the policy information needs• Select key commodities:

– maize, beans, lemons and tomatoes?– 35 commodities in the ENA?

• Identify the critical loss points• Set desired frequency, level of representativeness,

accuracy, etc.

Narrowing the survey scope

Analyse and use available resources: • Previous FLW surveys and report• Existing value chain analysis and reports

– Assessment from the Programa Nacional de Agrologistica– Analysis of ENA results– Diagnostico de la capacidad productive de los hogares Rurales y

perdidas post-cosecha CONEVAL– Reports of the Grupo tecnico de perdidas y mermas de alimentos en

México – Report of the Sistema Nacional de Abasto Alimentario - IPD

Narrowing the survey scope

Operate within the country statistical system The population census The census of agriculture The annual agriculture production survey. The post harvest loss survey? The food consumption & nutrition survey The household income/expenditure survey

The farm management survey Other special agriculture surveys (livestock) Price statistics surveys Administrative records International statistical publications & databases Technological research information.

Next steps

Define the data needs:• Inventory of policy and programmes, related information

need and monitoring frameworksIdentify the necessary surveys:• What actors? What statistical units? What type of

survey?Analyse data sources: data collection frequency, survey coverage, CV’s, etc.• identify the gaps

Next steps

Plan the integration of PHL into existing surveys. For example, at farm level:

• WCA: march to May 2017, ENA: data collection in Oct-Nov, new round of EnChor?

Ensure inter-institutional framework and coordination mechanism:• INEGI, SAGARPA, SODESOL, CONEVAL• Agronomic research, University (laboratory

analysis, experimental designs/trials).

Thank Youcarola.fabi@fao.org