Published May 6, 2024 | Version v2
Dataset Open

Maize management and yield of smallholder farmers in Sub-Saharan Africa between 2016 and 2022

Description

Yield and management practices data were collected from smallholders’ maize fields from 2016 to 2022. All fields corresponded to maize grown in pure stands (no intercropping). Data were collected from five maize producing regions in Sub-Saharan Africa: (i) north-central Nigeria (n = 115), (ii) Rwanda and Burundi (n = 2720), (iii) central Zambia (n = 861), (iv) southwest Tanzania (n = 3710), and (v) eastern Uganda and western Kenya (n = 7367). Data were collected by One Acre Fund (https://oneacrefund.org/), an NGO that provides smallholder farmers access to agricultural training, credit, crop insurance services, and farming supplies. About half of the fields in the database comprised farmers who subscribed to the One Acre Fund program and the other half farmers who did not. 

Maize grain yield, plant density, and row spacing were measured in two randomly placed boxes of 36 square meters at harvest, avoiding field edges. Field geolocation was recorded in 70% of the observations. When missing, the field geolocation was defined based on the nearby town (21%) or associated district (9%) location for the purpose of retrieving climate data. Management practices associated with each field were reported by farmers, including sowing and harvest dates, cultivar name, fertilizer inputs (types and total quantities for both organic and inorganic), fertilization method, liming, weeding, and pesticides (mainly insecticides to control fall armyworms). Farmers also reported the incidence of adversities (such as pests, diseases, Striga witchweed, hail, and excess water). Field size was reported by farmers and, in those cases in which farmers could not provide an accurate measure of their field size, or there was a strong indication of mistakes (e.g., nutrient fertilizer rates out of range), One Acre Fund personnel took in-situ measurements to determine field size. Input rates per hectare were calculated as the ratio of the farmer-reported input amount and field size. Data were subjected to quality control to remove unlikely values. Maize yield outliers were detected with a Bonferroni Outlier Test. Observations with plant densities and fertilizer rates higher than four standard deviations from the mean were excluded as well as those without geolocation, no N or P data, and atypical sowing dates. After quality control, the database contains a total of 14,773 field observations.

Inorganic fertilizer rates were converted to nutrient rates (in elemental nutrients) following typical fertilizer nutrient contents. Organic fertilizers were encoded separately in two binary variables and one continuous variable, indicating whether compost was used, if that compost contained manure, and compost application rate. Likewise, cultivars were classified into hybrids or open pollination varieties (OPVs), which included local varieties, retained seed, and improved OPVs. For hybrids, we retrieved the associated crop cycle maturity (short, medium, and long), disease tolerance traits, and year of release from companies’ seed catalogs. Reported incidence of diseases and insect pests (e.g., anthracnose, aphids, blight, cutworms, drought, fall armyworm, stemborer, termites, and stalk or kernel rot) were simplified to two binary variables indicating whether the crop was affected by pests and/or diseases. Infestation by parasitic witchweeds (Striga hermonthica and S. asiatica) was considered as a separate variable. Fertilization methods were also simplified to whether the fertilizer was applied inside a hole or broadcasted in the surface. Number of weeding operations was simplified to zero, one or two or more weeding per season. Sowing dates were expressed as a deviation from the estimated average sowing date for each climate zone-season combination. Fields were grouped based on their location using the climate zone scheme developed by the Global Yield Gap Atlas Project (www.yieldgap.org). Isolated observations (more than three standard deviations from the median distance across sites within the climate zone) were excluded from their group. In the case of climate zones with two maize seasons, each crop season was considered as a separate group. Field elevation was retrieved from the Amazon Web Services Terrain Tiles. Total precipitation during the growing season, as well as for early, flowering, and grain filling phases, was retrieved from CHIRP.  For observations with field-level coordinates data, root-zone plant-available water-holding capacity was retrieved from the World Soil Information database, and soil clay content, pH, organic carbon, and effective cation exchange capacity from iSDA. Lastly, the topography wetness index (TWI) was calculated from the elevation data. 

Table 1. List of survey-derived variables.

Name Type Unit Description
plant_date_dev discrete days sowing date deviation from cluster average
pl_m2 continuous # m2 plant density (plants per area)
row_spacing continuous cm distance between rows
hybrid binary - Was a commercial hybrid seed used?
hyb_mat ordinal - hybrid maturity (early, medium, late)
hyb_yor continuous - Year of release of the cultivar
hyb_tol_mln binary - Tolerance to maize lethal necrosis
hyb_tol_msv binary - Tolerance to maize streak virus
hyb_tol_gls binary - Tolerance to gray leaf spot
hyb_tol_nclb binary - Tolerance to northern corn leaf blight
hyb_tol_rust binary - Tolerance to rust
hyb_tol_ear_rot binary - Tolerance to ear rot
N_kg_ha continuous kg/ha N fertilization rate
P_kg_ha continuous kg/ha P fertilization rate
K_kg_ha continuous kg/ha K fertilization rate
compost binary - Was compost applied?
comp_t_ha continuous t/ha compost rate
manure binary - Did the compost contain manure?
fert_in_hole binary - Was the fertilizer applied in a hole?
lime_kg_ha continuous kg/ha lime rate
weeding discrete # number of times the plot was weeded
pesticide binary - Was any pesticide applied?
disease binary - Was yield affected by diseases?
pest binary - Was yield affected by pests?
striga binary - Was yield affected by the Striga weed?
water_excess binary - Was yield affected by water excess (heavy rain or flooding)?

 

Table 2. List of environmental variables. 

 
Name Unit Spatial resolution Description Source
GDD °C days 30 arc-sec (1km) Growing degree days www.worldclim.org
AI unitless 30 arc-sec (1km) Aridity Index (annual precipitation over potential evapotranspiration) www.worldclim.org
TS °C 30 arc-sec (1km) Temperature seasonality www.worldclim.org
season_prec mm 3 arc-min (5.6 km) Total rainfall during the maize season (10% of planting to 50% of the harvest) www.chc.ucsb.edu/data/chirps
season_prec_1 mm 3 arc-min (5.6 km) Rainfall during the first third of the season www.chc.ucsb.edu/data/chirps
season_prec_2 mm 3 arc-min (5.6 km) Rainfall during the second third of the season www.chc.ucsb.edu/data/chirps
season_prec_3 mm 3 arc-min (5.6 km) Rainfall during the last third of the season www.chc.ucsb.edu/data/chirps
elev m.a.s.l. 75 meters Elevation (altitude) above sea level registry.opend26ata.aws/terrain-tiles
soil_rzpawhc mm 1 km Root zone plant-available water holding capacity www.isric.org
soil_clay % 30 meters Clay content at 0-20cm soil depth www.isda-africa.com 
soil_pH - 30 meters pH (H2O) at 0-20cm soil depth www.isda-africa.com 
soil_orgC g/kg 30 meters Organic carbon at 0-20cm soil depth www.isda-africa.com 
soil_ECEC cmolc/kg 30 meters Effective cation exchange capacity at 0-20cm soil depth www.isda-africa.com 
twi unitless 75 meters Topographic Wetness Index calculated from elevation

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Additional details

Funding

Bill & Melinda Gates Foundation
Niche project INV-030103