Global Environmental Change 70 (2021) 102325
Contents lists available at ScienceDirect 
Global Environmental Change 
journal homepage: www.elsevier.com/locate/gloenvcha 
How is organic farming performing agronomically and economically in 
sub-Saharan Africa? 
Christian Schader a,*, Anja Heidenreich a, Irene Kadzere a, Irene Egyir b, Anne Muriuki c, 
Joseph Bandanaa b,i, Joseph Clottey b, John Ndungu c,d, Christian Grovermann a, 
Gianna Lazzarini a, Johan Blockeel a, Christian Borgemeister d, Adrian Muller a,e, Fred Kabi f, 
Komi Fiaboe g,h, Noah Adamtey a, Beate Huber a, Urs Niggli a, Matthias Stolze a 
a Research Institute of Organic Agriculture (FiBL), Frick, Switzerland 
b University of Ghana, Department of Agriculture Economic and Agribusiness Legon, Accra, Ghana 
c Kenya Agricultural & Livestock Research Organization (KALRO), Kenya 
d University of Bonn, Center for Development Research (ZEF), Germany 
e Swiss Federal Institute of Technology Zurich (ETHZ), Institute for Environmental Decisions IED, Department of Environmental Systems Science D-USYS, Switzerland 
f Makerere University, Department of Agricultural Production, Kampala, Uganda 
g International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772-00100 Nairobi, Kenya 
h International Institute of Tropical Agriculture (IITA), Yaoundé, Cameroon 
i University of Ghana, Institute for Environment and Sanitation Studies, Legon, Accra, Ghana   
A R T I C L E  I N F O   A B S T R A C T   
Keywords: The potential of organic agriculture and agroecological approaches for improving food security in Africa is a 
Productivity controversial topic in global discussions. While there is a number of meta-analyses on the environmental, 
Profitability agronomic and financial performance of organic farming, most of the underlying data stems from on-station field 
Smallholder farmer trials from temperate regions. Data from sub-Sahara Africa in particular, as well as detailed real-farm data is 
Impact assessment 
Agroecology scarce. How organic farming is implemented in sub-Saharan Africa and how it performs in a smallholder context 
remains poorly understood. We applied a novel observational two-factorial research design, which allowed to 
evaluate the impacts of i) interventions for introducing organic agriculture and ii) specific organic management 
practices on 1,645 farms from five case studies in Ghana and Kenya, which we closely monitored for 24 months. 
Among the farmers who have been exposed to the interventions, we found heterogeneous adoption of organic 
agriculture principles, depending on the intervention. Furthermore, we found rather passive than active organic 
management among farmers. Most yields and gross margins under organic management remained at similar 
levels as the conventional values in four of the case studies. In one case study, however, coffee, maize and 
macadamia nut yields increased by 127–308% and farm-level gross margins over all analysed crops by 292%. 
Pooling our data across all case studies, we found significantly higher (+144%) farm-level gross margins on 
organically managed farms than on conventional farms. This indicates the potential of organic and agroeco-
logical approaches if implemented well. Based on our observations, we argue for improving the implementation 
of organic agriculture projects in settings with smallholder farmers. Limited capacities, lack of appropriate inputs 
and market access are major agronomic and institutional challenges to be addressed. Furthermore, we argue for 
supporting a differentiated debate about which types of organic farming are really desirable by classifying ap-
proaches to organic farming according to i) their intention to work organically and ii) the degree of following the 
organic principles. This will support the design and implementation of targeted policy interventions for stimu-
lating sustainability of farming systems and rural development.   
* Corresponding author. 
E-mail address: christian.schader@fibl.org (C. Schader).  
https://doi.org/10.1016/j.gloenvcha.2021.102325 
Received 11 February 2021; Received in revised form 30 June 2021; Accepted 12 July 2021   
Available online 30 August 2021
0959-3780/© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
C. Schader et al.                                                                                                                                                                                        G  l o  b  a l  E  n  v  i r o  n m   e  n t a  l  C  h  a  n  ge 70 (2021) 102325
1. Introduction clearly separate the effect of the agronomic system versus the accom-
panying training or further institutional measures (Bolwig and Gibbon, 
Organic agriculture (OA) is a globally-applicable environmentally- 2009; Meemken et al., 2017; Ssebunya et al., 2018). On-station field 
friendly alternative to conventional farming systems, which strives for trials tend to disregard the context of smallholder farming (Adamtey 
the principles of health, ecology, fairness and care (Luttikholt, 2007). Its et al., 2016; Crowder and Reganold, 2015; Ponisio et al., 2015; Seufert 
fundamental agronomic core characteristic is to aim at a circular system et al., 2012) by assuming the general availability of adequate agronomic 
by reducing external inputs, in particular through the ban of chemical knowledge, high-quality inputs and market access. This is despite the 
inputs such as synthetic pesticides and mineral fertilisers (Reganold and many studies showing that OA is highly knowledge and management- 
Wachter, 2016; Seufert et al., 2017). The environmental impacts are intensive and that access to both high-quality organic inputs and func-
linked to the lower chemical input use and the agronomic practices that tioning markets can be challenging (Meemken and Qaim, 2018). Addi-
need to be implemented for compensating these inputs, such as a wider tionally, studies often focus on export-oriented certified crops (Bolwig 
crop rotation, active nutrient management via compost, manure and and Gibbon, 2009; Ssebunya et al., 2018), disregarding the farming 
nitrogen-fixing legumes and an increased use of preventive and bio- systems they are embedded in and neglecting various other forms of OA 
logical pest management strategies. Therefore, many studies show clear and their great diversity of agronomic practices and economic perfor-
environmental benefits in terms of biodiversity promotion, soil organic mances (Bolwig and Gibbon, 2009; Ssebunya et al., 2019; Tittonell et al., 
matter, reduced energy use as well as less greenhouse gas emissions, and 2010; Tittonell et al., 2005). 
decreased pollution of water, soil, and air (Gattinger et al., 2012; Lori Therefore, there is little evidence on the agronomic and economic 
et al., 2017; Mäder et al., 2002; Meier et al., 2015; Schader et al., 2012). performance of OA as implemented by smallholder farmers in SSA, with 
By this, organic farming can contribute to addressing some of the global some evidence at times even biased (Porciello et al., 2020). Thus the 
environmental challenges related to food production (Muller et al., controversial debate regarding OA in SSA is often more ideological than 
2017; Rockström et al., 2009; Steffen et al., 2015; Willett et al., 2019). based on facts and empirical data (Meemken and Qaim, 2018; UNCTAD 
However, the main drawback for organic farms are the often lower and UNEP, 2008). Consequently, for understanding the potential of OA 
yields, which partly offset these benefits when environmental benefits in SSA as a basis for livelihoods of smallholder farmers and for achieving 
are evaluated from a per-output perspective (Meier et al., 2015; Seufert environmental and economic objectives, more evidence on the way OA 
and Ramankutty, 2017; Tuomisto et al., 2012; van der Werf et al., 2020). is actually implemented on real farms and the resulting yields and 
Agroecological practices are compatible with the principles of economic performance are needed. 
organic agriculture and can enhance the agronomic performance and This study fills this gap through analysing how OA is implemented by 
potential of the system to adapt to climate change (Sinclair et al., 2019). smallholders and assessing its agronomic and economic impacts, for a 
Unlike other agroecological approaches, the strict ban of chemical in- large number of smallholder farms in East and West Africa, covering 
puts as a clear minimum criterion, allows to evaluate whether farmers different agroecological zones and business contexts. The study’s spe-
are working organically or not (Fouilleux and Loconto, 2017; Pekdemir, cific targets were i) to analyse how OA is implemented in different ag-
2018; Seufert et al., 2017). From a food supply chain perspective, roecological contexts and market rationales; and ii) to analyse the effect 
standards allow to communicate such a compliance, via globally appli- of organic farming practices on productivity and profitability at crop 
cable third-party certification systems. Such certification allows to and farm level. 
generate financial benefits in terms of price premiums for farmers who To achieve these two objectives, we developed an observational two- 
comply with basic standards and contributed largely to the adoption of factorial evaluation framework and applied it to 1,645 farms in five case 
organic farming in many countries, particularly in Europe and the US studies with different approaches to OA. Each case study consisted of a 
(Crowder and Reganold, 2015; Willer et al., 2020). group of farmers that had been exposed to an intervention for changing 
In SSA, the uptake of conscious organic farming is still low with their management practices to organic farming prior to the study and a 
about 0.2% of the agricultural land (Willer et al., 2020). This is partic- control group that has not been exposed to this intervention. We clas-
ularly interesting, as most conventional smallholder farmers in SSA sified the farms in each case study to a) whether they participated in the 
often suffer from low productivity and marginal incomes from their respective organic interventions (Organic Intervention Group vs. Con-
farming activities (Collier and Dercon, 2014; McCullough, 2015). Afri- trol Group) and b) whether they were using mineral fertilisers and/or 
can farmers perceive organic farming often as a foreign farming system, synthetic pesticides (Organic Management Group vs. Non-organic 
as its rules are given by foreign organisations and crops are often pro- Management Group) (Fig. 1). This allowed for the evaluation of two 
duced for foreign consumers. This hampers adoption and ownership by effects:  
smallholder farmers (Jouzi et al., 2017; Kamau et al., 2018; Vaarst, 
2010). Currently, OA is implemented in SSA with diverse aims and ra- A. For evaluating the effect of the organic interventions on the 
tionales, which blur general statements that are often made about adoption of organic practices, we compare the Organic Interven-
organic systems (Bennett and Franzel, 2013; Seufert et al., 2017; Willer tion Group with the Control Group (Fig. 1, vertical comparison). 
et al., 2019). For instance, there are many non-certified OA projects in B. For evaluating the effect of organic farming practices on pro-
SSA, often established to supply healthy and safe food for local markets, ductivity and profitability at crop and farm level, we compared 
while third party certified OA projects primarily aim at export markets the Organic Management Group with the Conventional Management 
in Europe or North America (Ibanez and Blackman, 2016; Reganold and Group (Fig. 1, horizontal comparison). 
Wachter, 2016). For OA to be accepted by farmers and to diffuse in sub- 
Saharan Africa, it needs to be manageable, productive (De Ponti et al., 2. Materials and methods 
2012; Muller et al., 2017; Seufert et al., 2012) and profitable (Crowder 
and Reganold, 2015; Meemken and Qaim, 2018; Seufert and Ram- 2.1. Case study selection 
ankutty, 2017). 
The agronomic and economic performance of OA has been scruti- We chose a transdisciplinary approach to the design and imple-
nised by academics, with multiple shortcomings regarding methods and mentation of this study, which purposefully included the opinions of 
data availability. While meta studies have analysed comprehensive external stakeholders in the selection of case studies and development of 
global datasets, there is little empirical data available for SSA (Crowder research questions. The rationale behind selecting the case studies was 
and Reganold, 2015; Seufert et al., 2012). Impact assessments of single to cover the relevant agroecological (i.e. humid and semi-arid), agro-
projects in SSA have often studied success stories qualitatively, without nomic (i.e. predominantly arable and predominantly perennial systems) 
providing a clear baseline or counterfactuals and without being able to and commercial contexts (i.e. focus on non-certified production for local 
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Fig. 1. Generic analytical framework for the evaluation of the effect of A) organic interventions on the adoption of organic management practices and B) the effect of 
organic practices on productivity and profitability of farms. 
Fig. 2. Overview of the locations and main characteristics of the five case studies in Ghana and Kenya and the interventions for introducing organic agriculture 
to farmers. 
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Table 1 
A) Number of organic and conventional the intervention and control groups of each case study. B) share of farmers not using any inputs prohibited according to organic 
standards (based on EU Regulation EU Regulations 834/2007, 889/2008 and 1235/2008). C) Estimated average treatment effect on the treated regarding adoption of 
organic farming practices.   
GH-C KE-C GH-NC KE-NC1 KE-NC2  
A) Number of farms in organic and conventional farms in intervention and control groups 
Organic Intervention / Organic management (Organic intended) 59 83 38 47 15 
Organic Intervention / Conventional management 134 7 194 66 39 
Organic Intervention Group - Total 193 90 232 113 54 
Control / Organic Management (Organic-by-default) 12 0 2 35 25 
Control / Conventional management 193 182 164 134 216 
Control Group - Total 205 182 166 169 241 
Total number of farms 398 272 398 282 295  
B) Proportion of farmers not using any inputs prohibited according to organic standards 
Organic Intervention group 30.6% 92.2% 16.4% 41.6% 27.8% 
Control group 5.9% 0.0% 1.2% 20.7% 10.4%  
C) Adoption of organic practices (Average effect of the treatment on the treated) 
Non-use of conventional inputs 
Non-use of mineral fertilisers 0.12** 0.41*** 0.09 0.09** 0.07 
Non-use of chemical pesticides (excl. Herbicides) 0.27*** 0.52*** 0.01*** 0.12** 0.18*** 
Non-use of chemical herbicides 0.03 0.79*** 0.05 1.00*** 0.02  
Substitution of conventional weed, pest and disease management 
Application of non-chemical pesticides and fungicides 0.08 − 0.03 − 0.01 − 0.02 0 
Application of mechanical or manual weed control − 0.06 − 0.03 0.01 − 0.02 − 0.08  
Substitution of mineral fertilisers 
Application of organic fertilisers − 0.04 0.12* 0.11** − 0.04 − 0.09** 
Use of cover crops 0.01 − 0.05 − 0.01 − 0.02 0.02 
Mulching 0.02 0.01 − 0.10 0.00 − 0.13 
Incorporation of crop residues 0.00 0.01 0.03* − 0.02 − 0.08*  
Further agroecological and preventive practices 
Reduced tillage 0.07 − 0.07 − 0.01 − 0.01 0 
Diverse crop rotation − 0.04 − 0.24** 0.01** 0.13* − 0.22** 
Agroforestry − 0.07 − 0.07 − 0.02 0.00 0.05 
Intercropping 0.04 0.06 − 0.03* 0.00 0.05* 
Application of measures to prevent soil erosion 0.01 − 0.01 0.04 0.00 0.03 
Significance levels: *** = p<0.001, ** = p<0.01, * = p<0.05. 
markets, or on certified production for export markets) in which organic minimum farm size (KE-C: 5 macadamia trees, KE-NC1: maximum of 3 
agriculture is implemented in SSA. Kenya and Ghana were selected as ha of farm land, KE-NC2: 5 mango trees; for the case studies in Ghana, 
focal countries for the following reasons: i) in both countries, there is a the maximum farm size was 10 ha). 
substantial share of area under organic agriculture, ii) the existence of In a second step, we stratified the organic farms according to village, 
organic crop production for export, and iii) the existence of local sci- and randomly selected organic farms in each stratum. In a third step, we 
entific partners with whom prior experiences in collaboration existed randomly selected similar conventional farms in each stratum. These 
and who could implement the study. In both countries, relevant organic conventional farms needed to meet the same size criteria as the organic 
farming initiatives (eight in Kenya, five in Ghana) were mapped, visited farms. 
and evaluated according to the following criteria: a) a sufficient number In total, the local research partners randomly selected 300 farms for 
of individual smallholder farms, which complied with the farm selection each of the three case studies in Kenya and 400 farms for each of the two 
criteria (see below), b) the willingness of the organic initiative operators case studies in Ghana (see Table 1A for final numbers) and sensitized the 
to cooperate with the research team, and c) coverage of a wide range of farmers, in a series of workshops, on the study’s objectives and the 
agroecological, agronomic and commercial contexts. Out of these 13 intensive data collection involved. 
organic initiatives, we selected five, referred to as case studies hereafter. 
The selected case studies (two in Ghana, with one of them being certified 2.3. Data collection 
(GH-C), and one non-certified (GH-NC), and three in Kenya, one of 
which was certified (KE-C) and two non-certified (KE-NC1, KE-NC2)) are In each case study, the research team assigned a data collection team 
described in Section 3.1 and Fig. 2. with a site manager and a group of 10–20 enumerators. The enumerators 
were trained and monitored extensively in order to ensure homogenous, 
2.2. Farm selection comparable and high-quality data. Data was collected for five cropping 
seasons from August 2014 to March 2017. Season 1 (August 2014 – 
As a first step, we characterized the population of organic farms in March 2015) was used as a pre-test for training the enumerators and for 
each case study area, according to the socio-demographic and agro- tailoring the questionnaire contents and procedures to the research ne-
nomic data collected by the organic interventions and defined criteria cessities. Farmers who were literate entered all relevant information into 
for selection: farms had a) to be located not more than 50 km away from farmers’ field books designed by the researchers. Less literate farmers 
each other, b) to be exposed to the intervention, which aimed at the were supported in keeping regular records of their farming activities by 
adoption of organic agriculture, at least three years prior to the start of literate family members or farmers’ secretaries (literate people from the 
the data collection period in July 2014, and c) to meet or exceed the village who were paid by the project). In Ghana, about 200 visits of an 
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average of one hour each were paid to each farmer by the enumerators 2.6. Data management and verification 
or farmers’ secretaries. In Kenya, the enumerators transferred the in-
formation from farmers’ field books fortnightly (about 100 times in Achieving high data quality standards with survey data from small-
total) to the electronic questionnaire for 2–3 h per visit over two years holder farmers is challenging. Therefore, we implemented a complex 
(four seasons). iterative data verification and correction process alongside the data 
The questionnaire was an electronic Excel file with an automatic collection to ensure complete, valid and consistently high-quality data 
upload function to a database in which all data was stored. The ques- (Figure S6). To minimize possible response errors, participating farmers 
tionnaire contained 20 sheets comprising all the relevant information and field secretaries were trained in record keeping prior to and during 
about each farm concerning inputs, outputs and processes. For each the data collection. Several other measures were applied to reduce 
farm, fields were identified and marked on a sketch map. We measured respondent/farmer fatigue: interviews were kept brief (max 1.5 to 2 h), 
the size of all fields on a farm using handheld GPS devices. Fields were but were performed on a regular basis. To maintain farmers’ motivation 
subdivided into plots if several crops or intercropping patterns were over the entire course of the project, all the participants received small 
found in one field. For each plot, all crops were documented. For each yearly tokens of appreciation, which did not influence their farming 
crop on each field, we documented inputs and outputs as well as which practices. To reduce data entry errors, ongoing training of enumerators, 
agronomic activities were performed. Finally, all inputs and outputs together with support and supervision, were established in all five case 
were documented in physical and monetary units (Figure S5). Physical study sites, including seasonal workshops, video tutorials and peer re-
quantities of yields were determined by using standardized measuring view sessions among the enumerators as well as regular checks of enu-
containers and calibrating the farmers’ own containers (e.g. baskets, merators’ performances. Carefully designed questionnaires, including 
bags, wheelbarrows, buckets, tins, etc.) accordingly. This allowed the instant validity checks were used to ease the data entry and reduce 
farmers to measure the quantities used and harvested with their own mistakes. Through this, enumerators were enabled to directly identify 
containers, yet allowed a standardization of these containers for and correct errors. After data collection, the completed questionnaires 
comparability. were uploaded to the central Microsoft Access database and passed 
through multiple procedures for data quality, checking to detect syn-
2.4. Characterization and analysis of farm management practices tactical, semantic and coverage anomalies within each questionnaire. 
Automated database queries were set up, resulting in enumerator- 
To understand how farmers practiced OA, we analysed the rate of specific data quality reports, each encompassing greater than 50 val-
adoption of organic practices at two levels: first, we looked at the idity checks. To also ensure the consistency between different ques-
number of farms which did not use any conventional inputs (inputs tionnaires and identify enumerator biases, agronomic parameters such 
prohibited in OA based on EU Regulations 834/2007, 889/2008 and as yields, inputs and labour hours were calculated and compared be-
1235/2008) on any of their plots during any of the seasons over the two tween and within case studies as well as between enumerators. 
years, referred to from here on as “Passive Organic Management We further established processes for identifying outliers for mone-
(POM)”. Second, we investigated how farmers substituted these inputs tary parameters (output, labour and input prices) as well as for physical 
by means of preventive measures and/or productivity-enhancing inputs inputs and outputs (labour hours per ha, inputs per ha, yield per ha). 
which are permitted in OA or by agroecological practices, referred to as Outliers were identified by calculating lower and upper fences (Q1-/Q3 
“Active Organic Management (AOM)”. To understand the extent to + 3*IQR). Monetary outliers were replaced with the case study median. 
which farmers implemented AOM, proxies were defined based on 23 For the data entries that caused outliers in physical inputs and outputs, 
farm-level indicators from the sustainability assessment method we followed a multiple imputation approach, applying the multivariate 
SMART-Farm Tool (RRID:SCR_018197) (Schader et al., 2016; Schader imputation by chained equations (MICE) method for replacing outliers 
et al., 2019) implemented on the same farms as the productivity and (Royston, 2004) through the R-package “mice” (van Buuren and 
profitability study (Table S7). The performance of each farm with Groothuis-Oudshoorn, 2011). Predictive mean matching (PMM) was 
respect to each indicator was evaluated according to a function defined used as imputation model and 22 variables were included as predictors 
within the SMART-Farm Tool. Indicator scores were aggregated and for output, labour and input quantity outliers. Five imputed datasets 
normalized using an equal-weight approach. A score of 0% means that a were generated through this method and analysed for differences 
farmer did not use any practices associated with AOM at all, while a through a MANOVA test. In a sensitivity analysis, the results proved to 
score of 100% means that a farmer fully implemented all AOM options. be stable (p greater than 0.993) and, consequently, the initial imputed 
data set was used for further analysis. The datasets and source code 
2.5. Analysis of productivity and profitability generated during and/or analysed during the current study are available 
from the corresponding author on request. 
We used R (https://www.R-project.org/) for computing the param-
eters relevant for assigning productivity and profitability at farm, crop, 2.7. Statistical analysis 
field, and plot level. All physical flows into and out of the farm were 
allocated to crops, fields, plots or livestock activities. Additionally, we We used an entropy balancing approach (Hainmueller, 2012; 
allocated all labour activities to specific fields, plots and crops or live- Meemken and Qaim, 2018), to correct for potential selection bias in each 
stock activities. We collected farm-specific price data for labour, land, case study with regards to participation in the OA interventions. The 
inputs and sales. For labour prices, based on the median of the data exact adjustment of covariate moments make it an appealing alternative 
received and in order to ensure comparability between the farms, we to standard matching or reweighting methods when estimating causal 
fixed labour prices at the level for non-permanent employees above 18 effects from observational studies (Zhao and Percival, 2015). Farm- 
years at 3.00 Ghanaian cedis (GHS) (GH-C), 37.50 Kenyan shillings specific weights were generated in STATA (StataCorp. 2017. Stata Sta-
(KHS) (KE-C), 1.67 GHS (GH-NC), 37.50 KHS (KE-NC1) and 46.43 KHS tistical Software: Release 15) using a large range of covariates covering 
(KE-NC2). This set-up allowed calculating farm, crop, field and plot- the characteristics farms and farmers (Table S8). Unobservable charac-
specific key performance indicators such as quantities of physical in- teristics, such as motivation or risk aversion, were assumed to be 
puts, labour, total input costs, yields, revenues, gross margins, land and implicitly captured through family labour, gender, experience and other 
labour productivity. covariates. 
Based on the entropy weights, key performance indicators reflecting 
immediate and intermediary outcomes were used to compare farms in 
the intervention groups with farms in the control groups at the crop and 
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farm levels. More immediate outcome variables include compliance premiums for further improving farmers’ livelihoods. 
with minimum requirements for OA and the uptake of AOM practices. Each case study consisted of 280–398 smallholder farmers over the 
More intermediate outcomes include changes in yield and economic period April 2015 to May 2017. A proportion of these farmers had been 
performance in terms of gross margins. As the weights were only exposed to interventions that introduced them to organic farming 
assigned to untreated units in the control group through the data pre- practices at least three years prior to data collection (organic interven-
processing, the entropy balancing produced estimates of the average tion group), while the remaining farmers (control group) were selected 
treatment effect on the treated (ATT) (Meemken and Qaim, 2018). The from similar socio-ecological contexts (Fig. 2). Average farm sizes were 
effects were estimated using a probit regression for the binary compli- 2–3 ha in Ghana and around or below 1 ha in Kenya. The most labour- 
ance outcome. A generalized linear model (GLM) with a binomial family intensive case study was KE-NC1, which was dominated by vegetable 
for the error distribution and a logit link for the dependent variable was production (1,450–1,660 h/(ha*a)) and KE-C (517–583 h/(ha*a)). In 
used to estimate the effect for the AOM scores, as recommended for the other case studies labour hours were between 55 and 147 h/ha*a. In 
dependent variables scaled as proportions (Papke and Wooldridge, GH-NC family labour was dominating (around 80%) compared to other 
1996). For the gross margin estimations, standard ordinary least square case studies. 
(OLS) regression was employed. For farm-level estimations of economic The main cash crops were cocoa in GH-C, coffee, macadamia nuts 
performance effects, the robustness of the method was tested by and bananas in KE-C, maize, millet and beans in GH-NC tea, maize and 
comparing the results generated by entropy balancing with the results brassicas in KE-NC1 and maize, peas and beans in KE-NC. Fertiliser in-
produced through propensity score matching. This confirmed the puts were very low in GH-C (0.4 kg N/(ha*a)), GH-NC (32 kg N/(ha*a)) 
findings. and KE-NC2 (8 kg N/(ha*a)), but very high in KE-C (169 kg N/(ha*a)) 
For the impact analysis at crop level, we concentrated on four crops/ and KE-NC1 (240 kg N/(ha*a)) (Table S9). 
crop categories in each case study, which were most commonly grown Conventional farmers did not report using any herbicides in KE-NC1 
by farmers. Crops were aggregated to crop categories according to while between 0.5 kg/(ha*a) (GH-C) and 2.4 kg/(ha*a) (KE-C) was re-
Table S9. The organic to non-organic yield ratio, input cost ratio, labour ported in the other case studies. Synthetic fungicides were used in all 
cost ratio, and gross margin ratio were calculated using a bootstrap case studies, but in GH-NC the use was very low, averaging 0.01 kg// 
procedure to estimate a single confidence interval on the ratio in me- (ha*a). The highest quantities of fungicides were used in KE-C (0.45 
dians. The systems were deemed significantly different from each other, kg//(ha*a)). A large amount of organic fungicides (e.g. copper) was 
if the 95% confidence interval of the ratio did not overlap one another. used by both conventional and organic farmers in KE-C. In terms of 
The analysis was implemented using the R-package boot and figures synthetic insecticide use, farms in KE-C were managed most intensively 
were produced using the R-package ggplot2. Gross margins are not with about 1.1 kg/(ha*a), too among the conventional farmers, while 
displayed for those crops with different mathematical operator signs. the other case studies ranged between 0.03 (GH-NC) to 0.44 kg/(ha*a) 
The sensitivity analysis, with an assumed general price premium of 20% on the vegetable plots in KE-NC1. Botanical insecticides (e.g. neem) 
as a conservative estimate, was based on data from a meta-study which were allowed in organic were only reported from certified organic 
(Crowder and Reganold, 2015). We, however, deducted estimated cost systems KE-C (2.4 kg/(ha*a)) and GH-C (0.1 kg/(ha*a)) (Table S9). 
for maintaining a functioning internal control system and covering cost In all case studies, interventions that introduced OA provided 
for external certification, as we did not want to overestimate potential training and organic inputs to encourage farmers to adopt organic 
profitability of the smallholder systems. practices. The training schedules covered crop rotation, compost mak-
The productivity effects of AOM were assessed across all five case ing, preventive and natural pest and disease management and general 
studies using a production function framework. In all cases, a Cobb- farm management. However, the training concepts and the governance 
Douglas specification was used. Due to the large number of zero for fostering the uptake of OA, such as the availability of organic inputs, 
values for mineral fertiliser and synthetic pesticide. inputs, dummy varied by case study (Table S11). While GH-C suffered from discontent 
variables associated with the incidence of zero observations were of farmers because their organic cocoa could not generate appropriate 
included in the analysis (Battese and Broca, 1997). In this article, we use price premiums during the course of our study, KE-C was equipped with 
the term “pesticide” as an umbrella term for fungicides, insecticides, a well-managed internal control system. Among the non-certified in-
herbicides and other plant protection substances, unless specified terventions, KE-NC1 had a well-functioning, long-term advisory service 
differently. The endogeneity of AOM was tested in all five cases and the with staff committed to the principles of OA and able to communicate 
significant correlation of error terms required the use of a regression the potential benefits of organic farming to farmers, while KE-NC2 and 
model in order to treat this covariate as endogenous. Program partici- GH-NC invested less efforts in capacity development (Table S10). 
pation and experience with organic management were employed as in-
struments in the first stage equation. 3.2. Implementation of organic farming practices 
3. Results and discussion To understand how farmers practice OA, we analysed the rate of 
adoption of organic practices distinguishing between a) Passive 
3.1. Case study descriptions Organic Management (POM), i.e. farmers not using any conventional 
inputs and b) Active Organic Management (AOM), i.e. farmers 
We applied the evaluation framework to a broad set of case studies substituting conventional inputs by means of preventive measures and/ 
covering different agroecological and market contexts, as well as various or productivity-enhancing inputs, which are permitted in OA. 
types of interventions that had introduced OA to African smallholder 
farmers. Three of the selected case studies were in Kenya (KE) and two in 3.2.1. Effect of the interventions on passive organic management 
Ghana (GH) (Fig. 2). In three of these case studies (KE-NC1, KE-NC2, All five organic interventions significantly reduced the number of 
GH-NC) the interventions aimed at implementing non-certified (NC) farmers using conventional inputs, including synthetic pesticides 
OA and in the other two, certified (C) OA was introduced (KE-C, GH-C). (including herbicides) and/or mineral fertilisers, compared to the con-
The implicit assumption behind the non-certified organic interventions trol groups (Table 1C). However, the share of farmers not using any 
is that they lead to a healthier environment and that farmers benefit conventional inputs differed substantially between case studies. In KE-C, 
from applying agroecological principles and technologies, avoiding the 92% of the farmers who were exposed to the interventions did not use 
use of synthetic pesticides and mineral fertiliser (Altieri, 2018; Jensen any conventional inputs, while in the control groups the share of farmers 
et al., 2015). The certified organic interventions combine the capacity not using conventional inputs was low. The farmers in GH-C had low 
development efforts with a formal certification for securing price compliance rates with organic standards as the business partners of the 
6
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cooperative failed to sell their produce on the organic market and thus (at least POM), while non-financial reasons (personal health related and 
did not receive the expected organic premium price. This contributed to a general conviction for organic farming) were less apparent. In the non- 
a large number of farmers in the intervention group continuing to use certified case studies, the primary motivation to practice organic 
mineral fertilisers and/or synthetic pesticides. In the non-certified case farming was non-financial with the exception of GH-NC, where 54% of 
studies, rather small percentages of farms fully followed the rules of OA. the responding farmers had primarily financial reasons. The differences 
In KE-NC1, the case study with the most promising approach, up to 42% in motivations and expectations are partly driven by the implementation 
of farmers worked organically, without any certification, while in GH- approach of the intervention. For instance, in KE-NC1, much time was 
NC and KE-NC2 only 16% and 28% did, respectively. prior invested to make farmers aware of the non-financial benefits of 
Remarkably, in four case studies, the share of smallholder farmers in organic farming such as human and environmental health. While in 
the control group that did not use conventional inputs (i.e. mineral most case studies, little difference between the responses of adopters and 
fertilisers or synthetic pesticides) was below or around 10%. In KE-NC1, non-adopters could be observed, farmers adopting organic management 
the rate was about 21% (Table 1B). This is contrary to literature, where practices in KE-NC2 and GH-NC had a higher share of financial moti-
organic-by-default is often indicated to be common among smallholder vations (Table S3). 
farmers in SSA (Jouzi et al., 2017; Sheahan and Barrett, 2017). This The most prominent challenges that the organic intervention farmers 
indicates the increasing availability and usage of conventional inputs as faced were: pest and disease damage during crop cultivation and post-
found by De Bon et al. (2014) and Andersson and Isgren (2021). Espe- harvest stages (74% of all farmers in the intervention groups perceived 
cially pesticide use among smallholders is far more widespread than this as an important challenge), lack of stable markets (67%), inade-
commonly assumed (Bakker et al., 2021). Getting accustomed to con- quate training and extension services (57%), unavailability of inputs 
ventional input use may lead to a decreased willingness of smallholder (54%) and additional labour required due to weeding (52%). The 
farmers to convert to an entirely organic system and agroecological importance of the challenges was perceived differently in the various 
principles (Sapbamrer and Thammachai, 2021). However, input use in case studies. Generally, the Ghanaian farmers perceived the agronomic 
SSA varies substantially between countries (Sheahan and Barrett, 2017), challenges as more important than the Kenyan ones. Furthermore, our 
therefore these results cannot be extrapolated to other countries. assessment of uptake of farmers is reflected in severity of the challenges, 
as farmers in KE-C and KE-NC1 who were exposed to these interventions 
3.2.2. Effect of the different interventions on active organic management perceive the challenges as overall less severe (Table S10). 
Farmers replacing conventional inputs with either preventive or While there is only little empirical evidence reported in literature 
curative agroecological practices can address nutrient and pest man- about motivations and challenges of organic farmers in SSA (Sapbamrer 
agement issues under organic management. These practices include, and Thammachai, 2021), the technical challenges, such as weed infes-
among others, applications of botanical pesticides, such as neem; pre- tation and damage by pests and diseases, are similar to those found in 
ventive practices can involve a more diverse rotation, agroforestry or Switzerland by Home et al. (2019) although the Swiss farmers in their 
intercropping systems (Altieri, 2018; Sinclair et al., 2019). We specif- study reported that these barriers were less severe than they had esti-
ically analysed whether the interventions led to an increased uptake of mated before conversion to organic. 
a) practices substituting conventional inputs for pest, disease and weed 
management, b) practices for substituting mineral fertilisers, and c) 3.3. Productivity and profitability of organic agriculture 
further agroecological practices. 
Looking at the effect of the interventions on the uptake of AOM 3.3.1. Productivity and profitability of organic farming at crop level 
practices, our data shows no widespread systematic adoption in any of Following the analysis of the effects of the interventions on the 
the case studies (Table 1C). Organic fertiliser application was slightly adoption of practices, we analysed how OA, as a production system, 
increased in KE-C and GH-NC while it was reduced in KE-NC2. Among performed. For this, we compared all farms in each case study, those 
the agroecological practices, only the diversity of crop rotations was who worked organically with those who did not, regardless of whether 
affected positively in GH-NC and KE-NC1 while it was even less diverse they were part of the intervention group or not (Fig. 1 – horizontal 
in KE-C and KE-NC2. For the remaining components of AOM, we did not comparison). We analysed the differences in yields, inputs, labour and 
find consistent differences between the intervention and control groups. gross margins of the four most widely grown crops in each of the five 
While at least one of the interventions led farmers to adopt POM, case studies, using an entropy balancing approach for estimating a 
AOM was not adopted widely in any of the case studies, despite that all sound counterfactual (Fig. 3). 
of the interventions aimed at such an adoption. This shows the impor- Among the total of 20 crops analysed from the five case studies, we 
tance of considering innovation dynamics and transition timeframes found four organically managed crops with significantly higher and four 
when introducing organic agriculture to smallholder farmers, as their crops (bananas in KE-C, baize and brassica in KE-NC1 and millet in GH- 
decision-making is dynamic, multi-dimensional and contextual (Her- NC1) with significantly lower yields (Fig. 3A). Input cost was signifi-
mans et al., 2021). Transferring information and skills to famers via cantly higher for three crops each, while inputs were significantly lower 
group trainings is an important component of capacity development, but for eight crops (Fig. 3B) and labour was lower for six crops (Fig. 3C). This 
needs to be embedded in a long-term process and governance structure, resulted in higher gross margins for four organically managed crops, 
which allows a group of smallholder farmers to learn and explore while only one crop (bananas in KE-C) had significantly lower gross 
practices on their own farms and identify ways of combining practices margins under organic management (Fig. 3D). 
that fit into their specific production system. Compared to applying Comparing the two certified case studies, farmers practicing OA 
mineral fertilisers, herbicides and pesticides, agroecological practices performed very differently in their productivity and profitability. Except 
are usually knowledge-intensive and require understanding of complex for reduced cocoa input costs (-100%), no significant differences in 
ecological principles (Sinclair et al., 2019). yields and gross margins between organically and conventionally grown 
crops could be observed in GH-C. Contrary, we observed higher yields 
3.2.3. Motivations and challenges for implementing organic agriculture for the economically most relevant crops (macadamia nut + 172%, 
In order to understand the low uptake rates of organic farming coffee + 308%, maize + 127%), while banana yields were lower (-61%) 
practices by farmers in the organic intervention groups, we analysed a) in KE-C. Input cost was reduced (macadamia nut, bananas) or stayed 
motivations to convert to organic agriculture and b) the implementation similar, while labour cost was increased for coffee (+87%) and mac-
challenges as perceived by the farmers. adamia nut (+248%) in KE-C. Despite labour cost was higher, the gross 
In the two certified organic case studies (KE-C and GH-C), high po- margins of coffee and macadamia nut increased by 336% and 185%, 
tential economic returns motivated farmers to practice organic farming respectively. 
7
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Fig. 3. Ratios of organic to non-organic A) yield (t/ha*a), B) input cost ($/ha*a), C) labour cost ($/ha*a), D) gross margin ($/ha*a) based on observed output prices 
and E) gross margin with an assumed price premium of + 20% for all crops grown organically. Data is weighted using entropy balancing to allow comparability. Dots 
indicate the ratio median estimates; bars represent the 95% confidence limits for the ratios. The systems were deemed significantly different from each other if the 
95% confidence interval of the ratio did not overlap one (highlighted in orange). The number of observations in each group is shown in parentheses [organic system/ 
non-organic system]. When no median input cost is displayed this is because it was zero for both conventional and organic crops. When gross margins are not 
displayed, gross margins for conventional and organic have different signs. (For interpretation of the references to colour in this figure legend, the reader is referred 
to the web version of this article.) 
Table 2 
Estimated effect of organic management on total gross margins ($/ha) in the case studies and for the pooled dataset as average treatment effect on the treated (ATT) 
using entropy balancing with the dataset as observed and an assumed 20% price premium for all organic products as sensitivity analysis.    
POOLED GH-C KE-C GH-NC KE-NC1 KE-NC2 
Potential outcome mean for conventional management 1095.7 403.7 2153.1 125.2 1595.4 208.2 
as observed Organic management 2644.8 401.0 8432.5 147.1 1474.4 238.3 
ATT 1549.1 -2.7 6279.4 21.9 -121.1 30.1 
Relative difference 141%*** -1% 292%*** 17% -8% 14% 
20% premium for all organic products Organic management 3159.3 520.6 9646.4 189.8 2092.2 350.3 
ATT 2063.3 116.9 7493.3 63.2 496.0 142.3 
Relative difference 188%*** 29%** 348%*** 52%** 31% 68%** 
Significance levels: *** = p<0.001, ** = p<0.01, * = p<0.05, ns=not significant. 
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Less pronounced differences were found in the non-certified case are mainly based on data from high-income countries (Badgley et al., 
studies: in GH-NC, yields of organic farms were similar to conventional 2007; De Ponti et al., 2012; Ponisio et al., 2015; Seufert et al., 2012). The 
farms for the four crops, almost no purchased inputs were used and la- methodological difficulties of comparing organic smallholder producers 
bour was reduced for maize (-29%), groundnuts (-40%) and millet in low-income countries and the resulting uncertainty resulting from the 
(-46%). Gross margins of organically managed crops were at similar definition of a sound counterfactual led even to a higher uncertainty of 
levels to their conventional counterparts, except in the case of maize impacts of organic agriculture on smallholder yield and profits. Some 
(+72%). In KE-NC1, however, organically managed brassica and maize authors identify strong yield increases due to organic agriculture 
yields were lower than for conventional farmers (-35%, − 60% and quite (Badgley et al., 2007; UNCTAD and UNEP, 2008), while others criticise 
consistent trends with findings from on-station research (Adamtey et al. methodological flaws (Meemken and Qaim, 2018). 
2016) but other crops were not significantly affected. Maize input cost 
was significantly lower while labour costs were lower for beans, maize 3.3.2. Profitability of organic farming at farm level 
and roots. Overall, no significant differences in gross margins were We observed positive effects of organic farming practices at farm 
observed in KE-NC1. On the other hand, in KE-NC2, pea (aggregate of level productivity in one of the five case studies (KE-C + 292%). Higher 
pigeon pea and cowpea) yields were higher, while the other crops farm level gross margins could neither be achieved in the other certified 
remained unaffected by organic management. In this case study, organic case study (GH-C) nor in the three non-certified case studies (Table 2). 
farmers significantly reduced input cost for mango while labour cost for As, at least in GH-C, organic price premiums were originally supposed to 
peas was higher. In terms of gross margins, there were no significant be realised, the farm-level gross margins were analysed under the 
differences, except for peas (+49%) (Fig. 3D). assumption that at least 20% of price premium could be realised due to 
Except for macadamia nut in KE-C, the organically grown crops in the organic management. For the pooled sample over all five case 
our case studies were not sold with/did not generate a price premium. studies, OA had significantly positive impacts on gross margins 
To assess the importance of local and international markets for organic (+141%). 
produce, we therefore tested the impacts of a general 20% price pre- Under such an assumption, the organic farmers in four of the five 
mium for the organic farmers for sensitivity analysis (see Fig. 3). case studies would have realised higher gross margins than the farmers 
Assuming an organic price premium, POM gross margins would be who managed their farms conventionally. Besides KE-C, GH-C (+29%), 
higher than the conventional counterpart for bananas (+131%) in GH-C, GH-NC (+52%) and KE-NC1 (+68%) would also have performed better, 
coffee (+429%) in KE-C, beans (+100%) and maize (+117%) in GH-NC, while for KE-NC1, there was no significant difference observed. For the 
roots (+344%) in KE-NC1, and beans (+77%) and peas (+140%) in the pooled sample, OA had significantly positive impacts on gross margins 
case of KE-NC2. (+188% instead of + 141%). 
The high variability of organic yields and gross margins through Our results show that there is no one silver bullet for increasing 
organic farming is mostly consistent with findings of meta-studies that profitability among smallholder farmers (Table S4). Profitability- 
Fig. 4. Graphical classification of African smallholder farmers according to their organic management practices and their intention to work organically based on the 
findings from our study. 
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increasing effects observed over all the five case studies can be associ- inputs from time to time, even though some used them only in small 
ated with labour input in general and specifically with the number of quantities. Both groups practice organic farming intentionally and can 
hours spent on pruning as one of the key specific good agricultural be further distinguished as farmers who manage their farm only 
practices for the perennial crops such as macadamia nut, mango and passively and those who manage their farm organically in an active way. 
cocoa. When used, the application of organic fertilisers had significantly Among the latter group, we can further distinguish between farmers 
positive impacts, while conventional fertilisers and pesticides affected who merely substitute conventional inputs by organic ones and those 
the revenues rather negatively. Organic insecticides (pyrethrum, neem) who actively follow agroecological principles and design their farm 
did not have significant yield effects while copper did. Contrary to accordingly for a sound organic nutrient and pest management. While 
findings from field trials (Adamtey et al., 2016; Altieri, 2018), further the latter group can be considered closest to implementing the principles 
organic and agroecological management practices resulted mostly in no of OA, according to our data it is the absolute minority among small-
remarkable economic benefits assessed for the farmers in our study holder farmers in SSA. This emphasises the necessity to view organic 
(Table S5). This could signify that the levels of inputs and practices agriculture as a farming system that requires a systemic shift beyond the 
applied by farmers in our study were still low and not optimal as sup- view of single practices that is increasingly taken up by agroecology or 
ported by on-station long-term trial findings in Kenya (Adamtey et al., regenerative agriculture (Altieri, 2018; Gosnell et al., 2019; Loconto and 
2016). Contrary to results from field studies and meta-studies, which Fouilleux, 2019). 
report the productivity of organic agriculture crop-specifically (Ponisio 
et al., 2015; Seufert et al., 2012), our study shows that contextual factors 4. Conclusions 
such as the governance and capacities of smallholder cooperatives are 
important factors determining the agronomic and economic perfor- This study feeds empirical facts into a long-term debate with greatly 
mance of OA, too. diverging opinions about the potential of organic agriculture for sus-
Many authors suggest that capacity development measures, which tainable intensification and food security in SSA. Using a large-scale 
are implemented alongside organic projects, are responsible for a large dataset with a two-factorial observational research design, we were 
share of the revenue increases that were observed in other studies able to consistently analyse a) how different organic interventions 
(Bolwig and Gibbon, 2009; Meemken and Qaim, 2018; UNCTAD and enable farmers to practice organic agriculture and b) how organic 
UNEP, 2008). In our study, we therefore, controlled for the number of agriculture comparatively performs in terms of productivity and 
training events from both government and non-governmental (NGO) profitability. 
organisers. Overall, organic farms had similar numbers of training Given the large heterogeneity of organic farming systems, it is 
events and extension visits by NGO and government agents in all case necessary to classify them into intended organic management and 
studies, except in KE-C, where the number of governmental and total organic-by-default and according to the degree they follow the princi-
trainings was even lower than for conventional farms (Table S6). Gov- ples of OA. While passive organic management (POM) (i.e. just omitting 
ernment trainings were generally rated lower by organic farmers than by prohibited inputs) is prevalent among all organic farmers, active organic 
conventional farmers in KE-NC1 and KE-C. On average, NGO trainings management (AOM) is only present among farmers who intentionally 
were rated better in terms of effectiveness compared to the practice OA and can be divided into a) the mere substitution of con-
governmental-based trainings. This indicates a potential for improve- ventional inputs and b) substitution of conventional inputs plus an ag-
ment and strengthening of trainings and extension services offered by roecological system design. 
governmental agents. This further indicates that there are large differ- While OA aims at such an agroecological system design, our study 
ences in the perceived quality of NGO training provided to the farmers. shows that the reality in SSA looks very different. After being exposed to 
an intervention for introducing OA, most farmers do not fully adopt even 
3.4. A management-based typology of organic farms in SSA POM and are even further away from sound AOM. We attribute this to, 
a) the limited knowledge and lack of capacities to manage the organic 
There is a great diversity of smallholder farmers in Africa. Much of production system, b) the lack of suitable organic biomass and other 
the on-going controversial discussion about OA is due to a lack of a clear organic inputs for soil fertility management, and effective plant pro-
classification and the very heterogeneous characteristics and perfor- tection inputs and c) the lack of markets which are sufficiently stable and 
mances that one can realise on farms that may all be called “organic” on allow for generating organic price premiums as additional incentives. 
a superficial level (Seufert and Ramankutty, 2017; Seufert et al., 2017). The farmers who managed their farms organically were mostly 
Therefore, based on the results from our study, we propose a terminol- performing not substantially different from conventional farmers in 
ogy for organic farms that can bring more transparency in the debate terms of yields and profitability. The exception farmers in an organic 
and can be used to assess the current situation and design tailored public certified case, in which an intervention introduced effective capacity 
and private policy interventions. development, including the provision of necessary organic inputs and an 
Fig. 4 distinguishes organic farming systems according to a) the intensive and functioning monitoring and control system to allow for 
degree they follow the principles of OA (health, ecology, fairness, care) organic premium prices. Uptake of organic practices as well as most 
(Luttikholt, 2007) (horizontal axis) and b) the intention to work physical yields and profits of organic farmers were substantially higher 
organically (vertical axis). The degree that farming systems follow the than their conventional counterparts in that case study. However, even 
principles of OA can be represented as continuous scale, however, a in this system, farmers did not substantially adopt AOM practices, 
clear line can be drawn between farmers who complied with the mini- neither could those who did derive significant economic benefits from it. 
mum requirements of organic standards (organic farmers) and those OA is operationalised by farmers mostly as a restriction in manage-
who do not (conventional farmers). On this scale, also conventional ment. However, if the aim is to make farmers work according to organic 
farmers can be by the extent to which they come close to the boundary of or agroecological principles, organic farming needs to be reframed from 
organic compliance, based on the amount and frequency of chemical a “do not use specific inputs” to a “do better agroecosystem manage-
inputs they use (Fig. 4). ment” approach and to be better adapted to a smallholder context in 
Furthermore, organic farmers can be grouped according to whether SSA. From field trials, we know that generally agroecological practices 
they practice organic farming because they do not have access to are effective in increasing yields (Altieri, 2018; Trabelsi et al., 2016). 
chemical inputs (organic-by-default) or whether they practice organic This could not be observed in any of our case studies and one likely 
farming intentionally (organic intended). In our case studies, organic- explanation relates to poor implementation of the measures due to 
by-default farmers, which were in the control and not in the interven- lacking capacities. Therefore, if the policy goal is to make farmers work 
tion groups, were rather uncommon, as most farmers used chemical increasingly according to the principles of OA and agroecology, the 
10
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abovementioned agronomic and institutional challenges need to be administration. Joseph Clottey: Methodology, Validation, Formal 
addressed, not only by private initiatives. This would support idea of analysis, Investigation, Writing - review & editing, Project administra-
payments for ecosystem services and large-scale public investments into tion. John Ndungu: Methodology, Validation, Formal analysis, Inves-
resilience of ecosystems and combating desertification in sub-Saharan tigation, Writing - review & editing, Project administration. Christian 
Africa, such as the Great Green Wall. Grovermann: Methodology, Validation, Formal analysis, Investigation, 
The excellent agronomic and economic performance of organic Writing - original draft, Writing - review & editing, Supervision. Gianna 
agriculture in one of the five case studies with more than 290% increase Lazzarini: Methodology, Validation, Formal analysis, Investigation, 
in gross margins at farm level, indicates the potential that organic Writing - review & editing. Johan Blockeel: Methodology, Validation, 
agriculture in SSA can have if the main challenges are addressed and the Formal analysis, Writing - review & editing. Christian Borgemeister: 
smallholder systems are managed well. Both governmental and non- Methodology, Validation, Writing - review & editing, Supervision. 
governmental capacity development needs to be targeted to the main Adrian Muller: Methodology, Validation, Writing - original draft, 
challenges of input availability, farmers’ capacity development for Writing - review & editing. Fred Kabi: Validation, Writing - review & 
agroecosystem management, and access to local and international editing. Komi Fiaboe: Validation, Writing - review & editing. Noah 
markets with price premiums. Due to the partly poor governmental Adamtey: Validation, Writing - review & editing. Beate Huber: Fund-
capacity development institutions, the role of private initiatives and ing acquisition, Writing - review & editing, Project administration. Urs 
standards is important to not only address issues specific to organic Niggli: Funding acquisition, Writing - review & editing, Project 
agriculture but also promote general good agricultural practices administration. Matthias Stolze: Funding acquisition, Writing - review 
(Schoneveld et al., 2019; Thorlakson et al., 2018). & editing, Project administration. 
Contrary to previous studies, which were based mostly on field trial 
data (Crowder and Reganold, 2015; Seufert and Ramankutty, 2017; Declaration of Competing Interest 
Seufert et al., 2012), we did find only sporadic indications for signifi-
cantly lower yields and profitability in organic systems in SSA compared The authors declare that they have no known competing financial 
to current systems. Within the SSA context, improved agronomic man- interests or personal relationships that could have appeared to influence 
agement through organic interventions, e.g. the use of organic inputs the work reported in this paper. 
which help to add organic matter to the soil, and tree pruning, can help 
to offset potential yield reductions as commonly reported for organic in Acknowledgments 
developed regions of the world. It is, however, likely that smallholder 
conventional high-input systems would yield much higher returns if The authors of this article and all project partners are grateful to the 
implemented well. The fact that particularly maize, millet and brassica Mercator Foundation Switzerland, the Swiss Agency for Development 
were among the few crops performing lower under organic management Cooperation, HIVOS and the Gerling Foundation for funding this study. 
in a few cases, draws attention to the importance of staple crops under FiBL thanks the Swiss National Science Foundation for providing addi-
organic management. Future research should therefore address the tional funds via the Project Enhancing Supply Chain Sustainability in the 
agronomic performance specifically of staple crops under organic National Research Programme 73 “Sustainable Economy” to produce 
management. this article. The authors furthermore thank David Amudavi, Hippolyte 
Finally, from a societal perspective, it should be considered that Affognon, Markus Arbenz, Richard Asare, Simone Bissig, Danny Coyne, 
organic farming induces less external costs to society (e.g. for clean Willy Douma, Bo van Elzakker, Robert Home, Jordan A. Gama, Adrian 
water, biodiversity protection and workers health), while delivering Haas, Juliana Jaramillo, Ofosu-Budu Kwabena, Wolfgang Langhans, 
more expensive food to consumers (Seufert and Ramankutty, 2017). Annette Massmann, Gian Nicolay, Nelson Ojijo, Kurt Riedi, Bernhard 
From a resource-economic perspective, the cost for providing public Schlatter, Monika Schneider, Willem-Albert Toose, Helga Willer, Bar-
goods to society should not be borne by consumers as is currently the bara Zilly for contributing to the success of this study. We also thank all 
case for certified OA in SSA (von Braun and Birner, 2017). This is a members of the data collection teams for their hard work and the 
typical free-rider problem - as only a limited number of consumers are members of the National Advisory Committees for their valuable inputs. 
ultimately likely to accept the higher costs for organic products, OA will Last but not least, we thank all farmers and their intervention partners 
globally not develop into a dominant system. Therefore, governments for participating in this study and investing their time in documentation. 
should either make efforts to internalise these external effects, and thus 
improve the relative competitiveness of organic farming practices, or Appendix A. Supplementary data 
facilitate and finance such capacity development and economic per-
spectives for the implementation of OA and enable smallholders to Supplementary data to this article can be found online at https://doi. 
practice it. org/10.1016/j.gloenvcha.2021.102325. 
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