Overview

Introduction

The effects of COVID-19 go well beyond the initial acute health impacts into prolonged repercussions on the economy, employment, poverty levels, safe water access, food security status, crop production, and much more. In this analysis, we looked at whether projections of COVID-19 case totals in low-and-middle-income countries (LMICs) overlapped with key phases in the crop production cycle to understand where, when, and on which important crops COVID-19 could take a toll. From this work, we can better inform our thinking on where to focus on Feed the Future programming, estimate which areas will be hit hardest next, and whether COVID-19 impacts will affect crops that are significant food sources, income generators, or both. Combined with data on the demographics of value chain actors involved in each affected crop, we could also see whether the impacts are projected to disproportionately impact women, smallholder farmers, small businesses, and other already-marginalized groups.

Methodology

We began with time series projections of COVID-19 case totals from a model developed by the Centre for the Mathematical Modelling of Infectious Diseases (CMMID) at the London School of Hygiene & Tropical Medicine (LSHTM), and overlaid those data with the calendar/timing of phases of the crop cycles in various countries from the Crop Monitor initiative of the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) to observe if peaks in the number of cases for a country were projected to occur at the same time as a key phase in the cycle of production for crops important to that country.

The LSHTM model

  • This model provides simulation-based estimates for COVID-19 epidemic scenarios in many low-and-middle-income countries. The model starts with the date a country reached 50 active infections (i.e. “Day 0”), and then projects the totals of several variables (cases, deaths, hospitalizations, etc.) each day over time for an ‘unmitigated’ scenario (i.e. the country takes no action to contain COVID-19 spread), as well as in ten different COVID-19 containment strategies involving social distancing, shielding, lockdowns, or combined approaches. The projections estimate how different containment strategies could have different outcomes on the spread and intensity of the disease in a country.

  • While you can see the full results and more granularity of the LSHTM research for each country here, the line graphs in this document utilize a loess smoothing function over a 10% window to plot the model’s projections for each country’s case totals across time for the eleven scenarios (unmitigated plus ten containment strategies) for all age groups combined.

  • ‘Day 0’ in the LSHTM model is defined as the date the country has 50 active infections. Note that this is different from the date the country reaches 50 total (cumulative) infections. While the true date of ‘Day 0’ is unknown, the model estimates this date for each country by looking at reported cases and adjusting that number based on a few factors, including:

    • An estimate of the difference between the number of actual infections and the number of reported cases (not all infections are reported);
    • An estimate of what portion of cases are asymptomatic (since many cases, especially in young people, may be asymptomatic and thus not ever reported/tested/known to the medical community);
    • An estimate of the delay between initial infection to onset of symptoms, and then the lag between onset of symptoms to accessing a test and getting results to be counted as a ‘confirmed case’.
    • Note: some countries do not yet meet the criteria of having had a ‘Day 0’ (i.e., a day with 50 active infections).

Refer to the LSHTM link below for more details on the model, including the definition of the different scenarios and methodologies for determining transmission rates, etc.

The GEOGLAM crop calendars

  • For each country, the timing of crop production phases was plotted for significant staple crops, and for each sub-national area where production occurs in a country. In the case of multiple crop seasons such as maize 1 and maize 2, the more significant season in terms of total production will be labeled as the first crop, e.g, maize 1.

  • The phases of the crop production cycle reflected in this analysis were defined as:

    • Planting: Start of Planting through the Early Vegetative stage
    • Idle (represents the dormancy stage of winter wheat where applicable)
    • Vegetative: Start of Vegetative period through the Reproductive period (Period of peak weeding)
    • Harvest: Ripening through Harvest

  • For the crop calendars displayed in this document, each sub-national area is represented where the timing of the crop production cycle differs. In cases where crop cycles followed the same timing throughout the country, only one crop calendar is shown for that country.

Country specific information

Visit the following pages to explore the data for specific countries.

Countries with multiple, sub-national crop calendars

For countries with multiple, sub-national crop calendars, you can view the information here.

* Argentina

* Benin

* Brazil

* Burkina Faso

* Cameroon

* Central African Republic

* Chad

* Chile

* Congo (Kinshasa)

* Cote d’Ivoire

* Ethiopia

* Ghana

* Guinea

* Honduras

* India

* Kenya

* Liberia

* Madagascar

* Mali

* Mexico

* Morocco

* Mozambique

* Nigeria

* Paraguay

* Senegal

* South Sudan

* Tanzania

* Togo

* Turkey

* Uganda

* Zambia

Countries with national level crop calendars

For countries with national level crop calendars you can view the information here.

* Afghanistan

* Algeria

* Bangladesh

* Bolivia

* Cabo Verde

* Cambodia

* Colombia

* Cuba

* Djibouti

* Ecuador

* Egypt

* El Salvador

* Eswatini

* Guatemala

* Guinea-Bissau

* Haiti

* Iran

* Iraq

* Lebanon

* Libya

* Malawi

* Nepal

* Niger

* Pakistan

* Peru

* Rwanda

* Sierra Leone

* Somalia

* South Africa

* Sri Lanka

* Sudan

* Tajikistan

* Tunisia

* Uruguay

* Yemen

Data sources

Citation and license

To cite this work, please use:

  • USAID / Bureau for Resilience and Food Security, Data, Monitoring, and Analytics Team. “COVID-19 Modeling and the Crop Cycle”. 2020. With data from the London School of Hygiene & Tropical Medicine Centre for the Mathematical Modelling of Infectious Diseases (LSHTM/CMMID) and from the Crop Monitor Initiative of the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM). https://paultanger.github.io/COVID_LIC/. Accessed 23 June 2020, 15:55:13 MDT.

This work is licensed under a Creative Commons Attribution 4.0 International License.