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. 2021 Nov 19;7(11):e08428.
doi: 10.1016/j.heliyon.2021.e08428. eCollection 2021 Nov.

Investigating false start of the main growing season: A case of Uganda in East Africa

Emmanuel Ocen  1 , C A J M de Bie  1 , Charles Onyutha  2
Affiliations

Investigating false start of the main growing season: A case of Uganda in East Africa

Emmanuel Ocen et al. Heliyon. .

Abstract

False start of the growing season (Fsos) is a component of the onset variability related to agronomic drought that adversely impact on agricultural production and productivity. In the sub-Saharan Africa (SSA) where agriculture heavily depends on rainfall, the Fsos tends to create confusion among farmers on when to start planting crops thereby affecting seed germination and normal growth after emergence. In this paper, we focus on the Fsos and the occurrence of dry spell especially before the Start of growing Season (SoS). We take advantage of the existing rainfall estimates (CHIRPS) and remotely sensed data for vegetation performance (NDVI) over the period 1999-2017 in combination with local knowledge derived from farmers to map out areas at risk of (i) dry spell at the SoS, and (ii) false timing of SoS or high probability of occurrence of the Fsos. We found that the North Eastern part of Uganda (8.8% of arable area) were at risk of dry spell throughout each year. However, the greater North (58.1% of arable area) was prone to dry spell during the onset of the March-May season. Areas in the South Western (3.7%) region were at risk during the onset of the September-November season. The probability that a location in Uganda experiences an Fsos falls between 0-53%. The findings in this study are vital for planning of predictive adaptation to the impacts of climate variability on agriculture amid struggle aimed at tackling food insecurity challenge in the SSA.

Keywords: Agronomic drought; False start of growing season; Precipitation variability.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The characteristic of the growing season within the study area in relation to the climatic zones (Source: Majaliwa et al., 2015).
Figure 2
Figure 2
Flow diagram showing the preparation of NDVI dataset over 19-year period, in which the statistical parameters 10th, 50th, 90th & SD were extracted and used in the mapping of areas prone to dry spell during the start of the growing season.
Figure 3
Figure 3
Framework for identification of areas at risk of dry spell at the onset of the growing season adopted from de Bie et al. (2008).
Figure 4
Figure 4
Spatial variability of Uganda land cover for the year 1999–2017 as depicted by the unsupervised classification results. The classes contain mixed land cover types with different spatial temporal characteristics.
Figure 5
Figure 5
Spatial coverage of distinct categories of areas at risk of a dry spell during start of season.
Figure 6
Figure 6
NDVI profile for (a) areas at the risk of dry spell of MAM growing season, (b) the entire year, (c) not at risk, and (d) areas at the risk in SON growing season.
Figure 7
Figure 7
a) Long term farmer recall of onset variability and b) the false start of the season.
Figure 8
Figure 8
Spatial extent of the occurrence of the false start of the main growing season from 1999 to 2017.
Figure 9
Figure 9
(a) the probability of false start occurrence, the probability of false start of the season increases from green to red. (b) its associated rainy days, (c) dates in dekad compared to onset of the season (d) the arable cropping areas in Uganda as an indication of vulnerability and impact of risk to farmers.
Figure A 1
Figure A 1
Temporal profile pattern for each of the classes generated from the unsupervised classification, describing the 10th, 50th, 90th percentiles and standard deviation as indicator of variability during the growing season in Uganda from the year 1999 to 2017.
Figure B1
Figure B1
Section A of the interview question focusing on seasonality and its variability.
Figure B2
Figure B2
Section B of the interview questions focusing of existing agricultural practices and underlying factors informing such decisions.
Figure C1
Figure C1
false start occurrence probability map (result from our study) and generic drought risk map during the overall growing season for Uganda as developed under the SUM-Africa project supported by the G4AW program of Netherlands Space Office (NSO).
See this image and copyright information in PMC

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