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Maize production in Zambia must increase with a view towards improved food security and reduced food imports whilst avoiding cropland expansion. To achieve this, it is important to understand the causes behind the large maize yield gaps observed in smallholder farming systems across the country. This is the first study providing a yield gap decomposition for maize in Zambia, and combining it with farm typology delineation, to identify the key limiting factors to maize yield gaps across the diversity of farms in the country. The analysis builds upon a nationally representative household survey covering three growing seasons and crop model simulations to benchmark on-farm maize yields and N application rates. Three farm types were delineated, including households for which maize is a marginal crop, households who are net buyers of maize, and households who are market-oriented maize producers. Yield gap closure was about 20% of the water-limited yield, −1 corresponding to an actual yield of 2.4 t ha . Market-oriented maize farms yielded slightly more than the other farm types, yet the drivers of yield variability were largely consistent across farm types. The large yield gap was mostly attributed to the technology yield gap indicating that more efficient production methods are needed to raise maize yields beyond the levels observed in highest yielding fields. Yet, narrowing efficiency and resource yield gaps through improved crop management (i.e., sowing time, plant population, fertilizer inputs, and weed control) could more than double current yields. Creating a conducive environment to increase maize production should focus on the dissemination of technologies that conserve soil moisture in semi-arid areas and improve soil health in humid areas. Recommendations of sustainable intensification practices need to consider profitability, risk, and other non-information constraints to improved crop management and must be geographically targeted to the diversity of farming systems across the country. Keywords Food security · Sustainable intensification · Farm typology · Global Yield Gap Atlas · Fertilizer input subsidy program 1 Introduction of farming systems (IAPRI 2020). Approximately 75% of the population rely on smallholder farming for their Economic development in Zambia is strongly linked to pro- livelihoods (MoA/CSO 2019). Maize (Zea mays L.) is the ductivity growth in agriculture and sustainable management main staple food crop in the country, as in other South- ern African countries (Smale 1995), with a harvested area Joao ˜ Vasco Silva of approximately 1 Mha and providing 50–90% of the email@example.com; firstname.lastname@example.org caloric intake of the national population. Maize produc- tion in Zambia is associated with low use of mineral fertilizers and low adoption of other sustainable intensifica- Sustainable Agrifood Systems Program, CIMMYT-Zimbabwe, Harare, Zimbabwe tion practices (e.g., conservation agriculture and improved maize legume cropping systems; Arslan et al. 2014). Plant Production Systems Group, Wageningen University & Research, Wageningen, The Netherlands Poor soil fertility and adverse effects of increased cli- mate variability reduce farmers’ financial resource base Sustainable Agrifood Systems Program, CIMMYT-Zambia, Lusaka, Zambia (Komarek et al. 2019) and contribute to low adaptive capacity of maize-based farming systems in the country Food and Agriculture Organization of the United Nations, FAO-Zambia, Lusaka, Zambia (Cairns et al. 2013). 26 Page 2 of 16 J. Vasco Silva et al. Smallholder farming systems in sub-Saharan Africa are highly diverse and farm typologies have proven useful to identify farms with different levels of resource endowments and livelihood strategies (Tittonell et al. 2010). The same is true in Zambia where approximately 1.6 million farmers are considered small scale with 70% having farm sizes below 2 ha, 25% having farm sizes between 2 and 5 ha, and 5% having farm sizes between 5 and 20 ha (Ngoma et al. 2019), and where poor subsistence farming co-exists with more market-oriented emerging commercial farming (Alvarez et al. 2018). Grain legumes are often produced alongside maize (Mwila et al. 2021) and livestock is kept in dry land areas of Southern and Western provinces characterized by Fig. 1 Maize yield gaps in Eastern Zambia. Maize plants on the left refer to an on-farm baby trial under good agronomic management (i.e., low and erratic rainfall. Identifying different farm types timely sowing, high plant population, hybrid maize variety, and proper is a means to consider farmers’ socio-economic context fertilizer inputs). Maize plants on the right show crop performance and resource endowment when promoting agricultural under actual farm management. Credits: J.V. Silva, February 2022. technologies (e.g., Jayne et al. 2019) and an important first step to target technologies for different farm types (Berre et al. 2017). Yield gaps of rain-fed crops are defined as the difference Zambia, and to identify the key limiting factors to maize between the water-limited yield (Yw) and the actual yield yield gaps across the diversity of farms in the country. The (Ya) observed in farmers’ fields (van Ittersum et al. 2013). analyses built upon a nationally representative household Yw is defined as the maximum yield that can be obtained survey covering the 2011/12, 2014/15 and 2017/18 growing under rain-fed conditions in a well-defined biophysical seasons (Figure 2;IAPRI 2012, 2015, 2019). Multivariate environment and without nutrient limitations or yield statistical techniques were used to construct the farm typol- reductions due to pests, diseases, or weeds. Currently, Ya for ogy (Alvarez et al. 2018) and yield gaps were decomposed −1 maize in Zambia ranges between 1.4 and 3.0 t ha ,which using a combination of frontier analysis and crop model- −1 is considerably lower than a Yw of 8–15 t ha that could ing (Silva et al. 2017). The latter was used to simulate Yw be achieved with best agronomic practices (Figure 1;van and estimate the nitrogen (N) rates needed to reach it, which Ittersum et al. 2016). Yield gap decomposition is a means were then used to benchmark maize yields and N rates to unpack the causes behind yield gaps as it identifies the observedinfarmers’fields. key crop management factors limiting or reducing Ya (Silva et al. 2017). The resource yield gap indicates the scope to increase Ya through higher amounts of inputs, whereas 2 Materials and methods the efficiency yield gap indicates the scope to increase Ya through fine tuning current management practices and 2.1 Rural Agricultural Livelihoods Survey (RALS) technologies in terms of the time, space, and application form of these inputs. The technology yield gap indicates Data from the Rural Agricultural Livelihoods Survey the possible yield increases beyond current best performing (RALS) was used to identify the main farm types engaged technologies on-farm. This decomposition is important in maize production and to determine the drivers of maize to derive policy recommendations and prioritize research yield variability in Zambia. The RALS comprises a panel and development interventions towards increasing maize of households interviewed over three different periods yields in existing cropland as food security and biodiversity and is statistically representative of the rural population conservation are dependent on such improvements. at the province and national levels. The surveys were This is the first study providing a yield gap decompo- conducted by the Indaba Agricultural Policy Research sition for maize in Southern Africa and combining it with Institute (IAPRI) in collaboration with the Ministry of farm typology delineation to identify what interventions are Agriculture and the Zambia Statistics Agency. The first needed, where, and for which farm types to narrow exist- round of RALS was conducted in May/June 2012, the ing yield gaps. We hypothesized that the magnitude and the second in June/July 2015, and the third in June/July 2019. determinants of the yield gap differ across farm types with The months when the RALS were conducted coincide with different production orientations and resource endowments. the harvesting period of the previous agricultural production The main objective of this study was thus to character- season and with the agricultural marketing season. A total ize farm diversity across maize-based farming systems in of 8839, 7934, and 7241 households were surveyed in 2012, Narrowing maize yield gaps across smallholder farming systems in Zambia: what interventions, where, and for whom? Page 3 of 16 26 Fig. 2 Spatial distribution of the Number of surveyed households per district households included in the Source: IAPRI 2012, 2015, 2019 Rural Agricultural Livelihoods 8°S Survey (RALS) across Zambia. Background layer displays the total annual rainfall (in mm) Rainfall average over the period 10°S 2000–2019. Source: Climate Hazards Group Infra-Red Precipitation with Station data 12°S 1000 (CHIRPS; Funk et al. 2015). 14°S Households (#) 16°S 0km 200km 400km 18°S 22°E24°E26°E28°E30°E32°E34°E Longitude 2015, and 2019, respectively, with 6531 panel households 25mm between the months of September and December interviewed in all three waves. (Hachigonta et al. 2008). The spatial distribution of households included in the RALS is provided in Figure 2. The survey requested 2.2 Farm typology delineation information on farm(er) characteristics and on field-specific crop management practices, thus meeting the requirements The farm typology was constructed using principal com- for yield gap decomposition (Beza et al. 2017). A unimodal ponent analysis (PCA) followed by hierarchical clustering rainfall regime with one wet season lasting from November (HC; Alvarez et al. 2018) on the pooled data. PCA is a tech- to April in each year was observed across the country nique used to reduce the number of dimensions in a dataset (Herrmann and Mohr 2011). Yet, annual rainfall was lowest to a few synthetic and uncorrelated variables called principal in the Southern and Western regions of Zambia, with an components. The principal components are linear combina- average between 600 and 800 mm per year, intermediate in tions of the original variables, which can be conceptualized the central regions, with an average between 800 and 1200 as the directions of high-dimensional data that capture the mm per year, and highest in the Northern regions, with an maximum amount of variance and project it onto a smaller average above 1200 mm per year (Figure 2). dimensional subspace. The principal components retained Secondary data were retrieved from spatial products for analysis were those with an eigenvalue greater than one. using the GPS coordinates of the individual households. PCA was conducted in R using the dudi.pca() function of Climatic data were retrieved from the climate zone scheme the ade4 package (Dray and Dufour 2007). HC refers to of the Global Yield Gap Atlas (GYGA) and comprised three the hierarchical decomposition of the data based on group variables: growing degrees days, temperature seasonality, similarities and was then applied to a distance matrix cal- and aridity index (Van Wart et al. 2013). Soil data on culated for the principal components selected following the clay, silt and sand contents, pH in water and exchangeable PCA. Similarities between clusters were calculated using acidity were retrieved from SoilGrids at 250m resolution the Ward method. The final number of clusters was identi- (Hengl et al. 2017) and on rooting depth and soil available fied through visual inspection of the resulting dendrogram water from AfSIS-GYGA (Leenaars et al. 2015). Simulated aiming to reach not less than three and not more than five water-limited yields for maize were retrieved from GYGA. clusters. HC was conducted with the hclust() function of the Rainfall data were obtained from Climate Hazards Group R stats package (R Core Team 2013). InfraRed Precipitation with Station data (CHIRPS, Funk et Thirteen variables aggregated at the farm level were al. 2015) and used to determine the dekad corresponding used to construct the farm typology, seven of which to the onset of the rains for each of the growing seasons were structural variables (i.e., describing the structure surveyed. The onset of the rains was defined as the first of the household, variables that tend to remain constant dekad with a cumulative rainfall equal to or greater than from one season to the next) and six of which were Latitude 26 Page 4 of 16 J. Vasco Silva et al. functional variables (i.e., describing the performance of 2.3.2 Stochastic frontier analysis the household). The farm(er) characteristics included in the typology were the age of the household head (years), Stochastic frontiers account for two random errors, v it household size (#), and area of owned cultivated land (ha) (random noise) and u (technical inefficiency), assumed it at the time of the surveys. Resource endowments were to be independently distributed from each other when captured with variables referring to the cash available to estimating production functions (Kumbhakar and Lovell each household (ZMW), farm assets calculated as the sum 2000). A Cobb-Douglas functional form (Equation 1), of the assets owned by each household multiplied by their comprising only first-order terms in the production frontier, respective economic value (in Zambian Kwacha, ZMW), was used to describe the relationship between maize yield total cultivated land in ha, and livestock ownership in and a vector of agronomic relevant variables defined tropical livestock units (TLU; Jahnke 1982) for each survey according to principles of production ecology (van Ittersum year. The total amount of maize produced, sold and bought and Rabbinge 1997). A translog functional form was per farm (all in kg) and the area cultivated with maize and also fitted to test the effect of second-order terms (i.e., legumes (both in ha) were included to assess the level of squared and interactions) on maize yield. The results of the engagement of each farm in maize and legume production, translog functional form are presented in Supplementary whereas the total fertilizer use at farm level (in kg) was Material given the large number of estimated parameters included to assess the level of agricultural intensification (Supplementary Table 3). Inefficiency effects, i.e., the of each farm. Variables were screened for outliers and drivers of the efficiency yield gap, were also estimated standardized using the scale() function in R to avoid the through a one-step estimation of the production frontier and influence of different levels of variation due to the unit the second-stage regression (Equation 2; Battese and Coelli of measurement of each variable. The mean value of each 1995), as follows: variable was compared for each farm type and the number of households per farm type were summarized per province ln y = α + β ln x + v − u (1) it 0 k kit it it and per year. u = δ ln z + (2) it j jit it 2.3 Yield gap decomposition v ∼ N(0,σ ) (3) it + 2 2.3.1 Concepts and deﬁnitions u ∼ N δ ln z ,σ (4) it j jit Eff. Yg = 1 − exp(−u ) (5) Yield gap decomposition (Silva et al. 2017) relies on it it −1 four yield levels to diagnose agronomic constraints in Y = y × exp(−u ) (6) TEx it it it cropping systems at regional level (Doree ´ tal. 1997). In addition to Yw and Ya (van Ittersum et al. 2013), the where y represents the maize yield in field i and in year it highest farmers’ yield (Y ) is defined as the average top t, x is a vector of agronomic inputs k used on field i HF kit 10th percentile of farmers’ yields whereas the technically and year t and, α and β are parameters to be estimated. 0 k efficient yield (Y ) is defined as the maximum yield that The vector z comprises the j crop management drivers TEx jit can be achieved for a given input level in a well-defined of the efficiency yield gap in field i and in year t.Y TEx biophysical environment. The efficiency yield gap refers and Yf were estimated for each field using the Cobb- to the difference between Y and Ya and is explained Douglas model described earlier (Equations 1 and 6), but TEx by suboptimal crop management in relation to time, space without considering inefficiency effects. Model parameters and form of inputs applied. The resource yield gap refers were estimated for the pooled data and for each farm type to the difference between Y and Y and is explained HF TEx with maximum likelihood using the sfa() function of the R by suboptimal amounts of inputs applied. The technology package frontier (Coelli and Henningsen 2013). Continuous yield gap refers to the difference between Yw and Y HF variables were ln-transformed prior to the analysis and and is explained by low input use and the lack of use data were used as a cross-section rather than as a panel, of specific technologies. The feasible yield (Yf) was also hence technological change and time-(in)variant technical considered to unpack the contribution of suboptimal input efficiency were not assessed. use (i.e., resource yield gaps) and variety choice to the The vector of inputs x was designed to capture the kit technology yield gap. Yf is defined as the maximum yield effect of growth-defining, growth-limiting, and growth- with available technology and best-practice management reducing factors on maize yield (Silva et al. 2017). but with no economic constraints (van Dijk et al. 2017). Growth-defining factors were controlled for with the Narrowing maize yield gaps across smallholder farming systems in Zambia: what interventions, where, and for whom? Page 5 of 16 26 following variables: growing degrees day considering a base field classification was specific to each of three variety types temperature of 0 C(VanWartetal. 2013), temperature and to each unique climate zone (Van Wart et al. 2013)and seasonality defined as the standard deviation of average soil type (Hengl et al. 2015), so genotype and biophysical monthly temperatures (Van Wart et al. 2013), seed rates factors were controlled for when comparing maize yields −1 (kg ha ), replanting (yes or no), and variety type (open- and management practices across the different fields. pollinated, hybrid, or unknown). Growth-limiting factors related to water included variety classification according 2.3.4 Global Yield Gap Atlas (GYGA) to drought tolerance (yes, no, or unknown), aridity index defined as the ratio between total annual precipitation and Yw for rain-fed maize across Zambia was obtained annual total potential evapotranspiration (Van Wart et al. from GYGA. Maize Yw in Zambia was simulated with 2013), soil rooting depth and soil available water (Leenaars the HybridMaize crop model (Yang et al. 2004)for et al. 2015), soil texture class constructed based on spatial the period 2001–2010 (see www.yieldgap.org/Zambia for predictions of clay, silt, and sand contents (Hengl et al. further details). The average Yw data over the period 2001– 2017), location of the field in a wetland (yes or no), and 2010 for a given climate zone was used here to benchmark presence of erosion or flood control practices (yes or no). Ya in farmers’ fields and the technology yield gap was Growth-limiting factors related to nutrients included the rate then calculated as the difference between Yw and Y for HF −1 of N applied (kg N ha ), pH in water, and exchangeable unique climate zone x soil type x variety combinations. acidity (Hengl et al. 2017). Finally, growth-reducing factors It was not possible to make use of year-specific Yw were captured with the number of weeding operations (none data for the same growing seasons in which the surveys or one, two, and three or more), herbicide use (yes or no), were conducted due to lack of Yw data for the growing and insecticide use (yes or no). Sowing date, expressed in seasons surveyed, which introduces uncertainties in the weeks after the onset of the rains, and date of the first magnitude of the overall yield gap estimated, particularly weeding operation, expressed in weeks after sowing, were in regions with erratic rainfall. Therefore, coefficients of included in the model as inefficiency effects. The variance variation of maize Yw were computed to better characterize inflation factors indicated no multicollinearity between the inter-annual yield variability across Zambia. The N rates considered variables. needed to reach 80% of Yw were also retrieved from The Cobb-Douglas frontier model without inefficiency GYGA (ten Berge et al. 2019) to benchmark N used in effects was used to predict Yf for specific values of some of farmers’ fields. −1 the input variables. To do so, seed rate was set at 25 kg ha , which is the recommended seed rate for maize in Zambia. −1 N application rate was set at 350 kg N ha ,which is 3 Results the minimum N requirement for a target of 80% of Yw in the high rainfall areas of Zambia (www.yieldgap.org). 3.1 Maize-based farming systems in Zambia It was further assumed that drought tolerant hybrid maize varieties were used in combination with replanting of maize Rural agricultural households across Zambia cultivate on seedlings, herbicides, and insecticides. The estimation of Yf average 2.2 ha of land and own 4.5 tropical livestock units further assumed that fields with a pH in water below 6.5 (TLU; Figure 3A and B). Yet, the median values were were corrected to a pH in water of 6.5 and that fields with considerably lower with 50% of the surveyed households −1 exchangeable acidity above 0.2 cmol+ kg were corrected cultivating less than 1.6 ha and owning less than 1.1 to that level in fields with pH below 6.5. TLU. Maize was cultivated throughout the country with an average and median maize area share of 67% of the 2.3.3 Distribution of actual yields total cultivated (Figure 3C). This corresponds to an average maize area per farm of about 1.4 ha. Fertilizer use across −1 Farmers’ fields were categorized as highest, average, and the country was on average 140 kg ha of cultivated land, lowest yielding fields based on the distribution of Ya with 50% of the surveyed farms using less than 110 kg of observed for a given variety type and climate zone x soil fertilizer per ha of cultivated land across the three survey type combination. Highest yielding fields were identified as periods (Figure 3D). those with Ya above the 90th percentile. Average yielding There were wide variations in total cultivated land, fields were identified as those with Ya between the 10th livestock ownership, maize share of cultivated cropland, and and the 90th percentiles and lowest yielding fields as those total fertilizer use across the different provinces (Figure 3 with Ya below the 10th percentile. Highest (Y ), average and Supplementary Table 1). The average total cultivated HF (Y ) and lowest farmers’ yields (Y ) were calculated as land was larger than the national average in the Southern AF LF the average Ya for the fields in each respective group. The (3.4 ha), Central (2.8 ha), and Eastern provinces (2.4 ha), 26 Page 6 of 16 J. Vasco Silva et al. Cultivated land (ha) Tropical livestock units A) B) 0 0 Maize area share (% of cultivated land) Fertilizer use (kg per ha of cultivated land) C) D) 0 0 Fig. 3 Main characteristics of farming systems in Zambia and their per ha of cultivated land. Data for the entire country are highlighted variability at national level and per province: (A) cultivated land in in dark gray. Asterisks show the mean value across the farm-year ha, (B) livestock ownership in tropical livestock units, (C) proportion combinations of each province. of the cultivated land occupied by maize in %, and (D) fertilizer used and lower in all other provinces (1.4–2.1 ha; Figure 3A). of Lusaka and Copperbelt, between 70 and 75% in the The same was true for livestock ownership which was on Southern, Northwestern, and Central provinces, and about average 11.9, 5.5, and 4.4 TLU in the Southern, Central, 60% in the Eastern, Muchinga, and Luapula provinces. and Eastern provinces, respectively, and much lower in all The Northern province was where the maize share of other provinces, notably those in the Northern part of the cultivated cropland was lowest, ca. 55% of the total country (Figure 3B). Maize represented more than 50% cultivated land. Finally, fertilizer use was below the national of the cultivated land for at least 50% the surveyed farms average in the Southern, Eastern, and Western provinces −1 in all provinces (Figure 3C). The average maize share (50–100 kg ha ), and slightly above the national average of cultivated cropland was above 80% in the provinces in the other provinces (Figure 3D). Southern Central Eastern Zambia Northern Muchinga Western Lusaka NorthWestern Copperbelt Luapula Southern Eastern Lusaka Central Copperbelt Zambia Southern Lusaka NorthWestern Northern Central Muchinga Zambia NorthWestern Western Western Eastern Copperbelt Muchinga Luapula Luapula Northern Lusaka Copperbelt Central Luapula Muchinga Northern NorthWestern Zambia Southern Eastern Western Narrowing maize yield gaps across smallholder farming systems in Zambia: what interventions, where, and for whom? Page 7 of 16 26 Table 1 Maize yield potential (Yp) and water-limited yield (Yw) for each location, averaged over the same period as the yield ceilings. eight weather stations located across Zambia. Means and coefficients Source: www.yieldgap.org; van Ittersum et al. (2016); ten Berge et al. of variation (CV) are provided for the years 2000–2010. ‘N require- (2019). ments’ refer to the minimum N rates needed to reach 80% of Yw in Province Weather station Mean Yp CV Yp Mean Yw CV Yw N requirements −1 −1 −1 (t ha)(%) (tha)(%) (kgNha ) Northern Kasama 18.71 0.05 18.59 0.05 325.1 Luapula Mansa 18.34 0.07 17.36 0.09 303.6 Muchinga Mpika 16.78 0.05 14.48 0.21 253.1 Eastern Chipata 16.56 0.06 13.29 0.31 232.3 Central Kabwe 16.79 0.05 12.63 0.35 220.7 Central Mumbwa 16.25 0.08 10.03 0.57 175.4 Western Mongu 16.80 0.07 9.79 0.47 171.2 Southern Choma 13.28 0.06 9.38 0.40 164.0 3.2 Farm types and importance of maize the large number of livestock kept and large amount of fertilizer used (Figure 4 and Supplementary Table 2). The farm typology was constructed using principal com- The age of the household head did not vary significantly ponent analysis (PCA) followed by hierarchical clustering across farm types (Figure 4) whereas household size was (HC). Four principal components had an eigenvalue greater lower for FT1 (5.5 individuals), intermediate for FT2 (7.2 than one and were retained for further analysis. These four individuals), and higher for FT3 (8.2 individuals). FT1 principal components explained approximately 60% of the owned 1.5 TLU and cultivated a total of 1.4 ha, 0.8 ha of cumulative variance in the data. Three clusters were iden- which were allocated to maize and 0.3 ha to legumes, and tified in the dissimilarity dendrogram of the HC analysis, used 140 kg of fertilizer per farm per year. FT1 produced corresponding to three distinct farm types. In short, Farm an average of 1500 kg of maize, sold 600 kg of maize, and Type 1 (FT1) exhibited a low dependency on maize produc- bought 50 kg of maize per farm per year. FT2 had access to tion and consumption, Farm Type 2 (FT2) were net buyers 2.7 TLU and cultivated a total of 1.3 ha, of which 0.8 and of maize and exhibited low levels of maize area and produc- 0.1 ha were cultivated with maize and legumes, respectively. tion, and Farm Type 3 (FT3) were market-oriented maize Fertilizer use was lower in FT2 than in FT1 (Figure 4) with producers engaged in agricultural activities, as indicated by a rate of 90 kg fertilizer per farm per year, and so was maize A) Farm type 1 B) Farm type 2 C) Farm type 3 Age HH Age HH Age HH TLU TLU TLU head head head Own cult. Available Own cult. Available Own cult. Available 2.5 2.5 2.5 land cash land cash land cash 2.0 2.0 2.0 1.5 1.5 1.5 Maize Cultivated Maize Cultivated Maize Cultivated 1.0 1.0 sold land sold land sold land Maize Farm Maize Farm Maize Farm produced assets produced assets produced assets Maize Fertiliser Maize Fertiliser Maize Fertiliser bought use bought use bought use Maize HH Maize HH Maize HH area size area size area size Legume Legume Legume area area area Fig. 4 Radar charts represent all studied quantitative variables on indi- variable for all farm types (cf. Supplementary Table 2). The spatial vidual axes starting from the same central point for each farm type. and temporal distribution of the farm types is provided in Supplemen- The variables displayed were used in the principal component analy- tary Figures 1 and 2, respectively. Abbreviations: ‘HH’ = household, sis followed by hierarchical clustering to delineate the farm typology ‘TLU’ = tropical livestock units. for the pooled data. Data are scaled with the average value of each 26 Page 8 of 16 J. Vasco Silva et al. −1 production and maize sold (Figure 4), with an average of and 9.5 t ha in the Western and Southern provinces. 1000 kg and 250 kg per farm per year, respectively. FT3 The respective CV for Yw was 5, 30, and 45% for the used 600 kg of fertilizer, produced 6500 kg of maize, sold Northern, Eastern, and Western and Southern provinces, 1600 kg of maize, and purchased 80 kg of maize per farm respectively (Table 1). The difference between Yp and Yw per year. indicates the yield gap due to water limitations, whose There were slight differences in the spatial distribution of magnitude increased along a North-South gradient (Table 1) the three farm types (Supplementary Figure 1). In Western characterized by lower and more erratic rainfall (Figure 2). province, nearly 70% of the farms were classified as FT2 N rates needed to reach 80% of Yw were greater than −1 and only 10% of the farms were classified as FT3. By 250 kg N ha in the Northern, Luapula, and Muchinga −1 contrast, in Southern and Central provinces as much as 50% provinces, ca. 230 kg N ha in the Eastern province, −1 of the farms were classified as FT3 whereas 20% and 30% and about 170 kg N ha in the Western and Southern were classified as FT1 and FT2, respectively. In Luapula, provinces (Table 1). Muchinga, Northern, and Northwestern provinces, 35–40% Yield gap closure (i.e., the ratio between Ya and Yw) of the farms were classified as either FT1 or FT3. Farms was on average 21% of Yw and varied with agro-ecological were evenly distributed amongst farm types (ca. 30% per zone, province, and farm type (Figure 6). Yield gap closure farm type), in the Eastern and Copperbelt provinces. There was greatest in agro-ecology I (35% of Yw), intermediate were no major changes in farm type classification for single in agro-ecology IIa (23% of Yw), and smallest in agro- farms over time (Supplementary Figure 2): out of 5238 ecologies IIb and III (15% of Yw; Figure 6Aand B). farm-year combinations, 715 were classified as FT3, 412 as Yield gap closure per province was similar to that per agro- FT2, and 209 as FT1 in the three rounds of the survey. Other ecology (Figure 6B and E) because most of the Southern changes in farm type classification were not consistent province is in agro-ecology I, the Central and Eastern and were likely to reflect fluctuations in farm performance provinces are in agro-ecology IIa, the Western province is in over time. agro-ecology IIb, and the Northern, Northwestern, Luapula, Muchinga and Copperbelt provinces are in agro-ecology III. 3.3 Yields and yield gaps of rain-fed maize Finally, yield gap closure was on average 30% of Yw for FT3, 20% of Yw for FT1, and only 15% of Yw for FT2 Maize Ya across all farm-year combinations analyzed (Figure 6Cand F). −1 ranged between nil and 9.0 t ha (Figure 5). Ya was smaller Most of the yield gap was attributed to the technology −1 and more variable in 2019 than in 2012 and 2015 harvest yield gap, which accounted for 7.2 t ha (50% of Yw) on years (Figure 5A), with average values of 2.6, 2.4, and average, yet narrowing efficiency and resource yield gaps −1 2.2 t ha and a coefficient of variation (CV) of 67, 67, could more than double Ya for maize in Zambia (Figure 6). −1 and 77% during the 2012, 2015 and 2019 harvest years, The efficiency yield gap was on average 1.6 t ha (14% of −1 respectively (Figure 5A). There were also clear differences Yw) and the resource yield gap was on average 1.7 t ha in the distribution of Ya across agro-ecological zones, farm (16% of Yw), which means that fine tuning current crop types, and variety types. Ya was smallest and most variable management practices and increasing input use to the level −1 in agro-ecology IIb (mean = 1.3 t ha ,CV = 82%) and of highest yielding fields can increase yields from the −1 −1 −1 greatest and least variable in agro-ecology III (2.7 t ha , current 2.4 t ha to 5.7 t ha . The resource yield gap 61%), with intermediate values observed in agro-ecology considering the feasible yield (i.e., maximum yield with IIa and I (Figure 5B). Ya was also smallest and most variable available technology and best-practice management but −1 −1 for FT2 (1.8 t ha , 76%), intermediate for FT1 (2.4 t ha , with no economic constraints) as ceiling was small with −1 −1 66%), and greatest and least variable for FT3 (2.9 t ha , an average of 1.0 t ha (7% of Yw). This means that 61%; Figure 5C). Finally, Ya was on average 1.9 and 2.9 resource-use efficiency in farmers’ fields is low and must be −1 tha , with a CV of 61 and 73%, for open-pollinated and improved to realize the yield gains associated with increased hybrid maize varieties, respectively (Figure 5D). input use and better technology. The large technology yield Simulated yield potential (Yp) ranged between 13 gap is thus a result of suboptimal input use compared −1 and 19 t ha in the Southern and Northern provinces, to what is needed to reach Yw and of low resource-use respectively, without a clear spatial distribution across the efficiency of current farm practices. country (Table 1). Conversely, Yw was greatest and least There were slight differences between agro-ecological variable in the Northern, Luapula, and Muchinga provinces, zones and provinces in the relative contribution of each yield gap to the overall yield gap (Figure 6). For instance, the intermediate in the Eastern and Central provinces, and smallest and most variable in the Southern and Western relative contribution of the technology yield gap to the total −1 provinces (Table 1). Yw was on average 18 t ha in yield gap was less than 10% of Yw in the Southern province −1 the Northern province, 13 t ha in the Eastern province, (which is part of agro-ecological zone I; Figure 6Dand Narrowing maize yield gaps across smallholder farming systems in Zambia: what interventions, where, and for whom? Page 9 of 16 26 Cumulative probability (%) Cumulative probability (%) 100 100 A) B) 90 90 80 80 2015 AEZ I AEZ IIa 2019 AEZ IIb 70 70 60 60 AEZ III 50 50 40 40 30 30 20 20 10 10 0 0 01234567 01234567 Cumulative probability (%) Cumulative probability (%) 100 100 C) D) 90 90 Open−pollinated Farm 80 80 varieties (OPV) type 1 Farm 70 70 type 2 60 60 Farm Hybrid 50 type 3 50 varieties 40 40 30 30 20 20 10 10 0 0 01234567 01234567 Maize actual yield (t/ha) Maize actual yield (t/ha) 9 9 E) F) 8 8 Highest Highest 7 7 yielding fields yielding fields 6 6 5 5 Average Average 4 4 yielding fields yielding fields 3 3 2 2 Lowest 1 1 yielding fields Lowest yielding fields 0 0 010 20 30 40 0 20 40 60 80 100 120 140 160 180 Seed rate (kg/ha) N rate (kg N/ha) −1 −1 Fig. 5 Maize actual yield variability across years (A), agro-ecology (66.2%) for farm type 1; 1.8 t ha (76.2%) for farm type 2; 2.9 t ha −1 zones (AEZ, B), farm types (C), and variety types (D), and maize yield (61.3%) for farm type 3; 2.9 t ha (60.9%) for hybrid varieties; 1.9 −1 response to seed rate (E) and N applied (F). Lines in (A)–(D) display tha (73.3%) for open-pollinated varieties. Data in (E) and (F) are empirical cumulative distribution functions. Mean values (and coeffi- aggregated per household × field type, and lines display statistically −1 cients of variation) are as follows: 2.6 t ha (67.0%) for year 2012; significant ordinary-least square regressions fitted to highest (Y ), HF −1 −1 2.4 t ha (66.6%) for year 2015; 2.2 t ha (76.8%) for year 2019; average (Y ), and lowest yielding fields (Y , quadratic for seed rate AF HF −1 −1 2.1 t ha (74.0%) for AEZ I; 2.4 t ha (70.3%) for AEZ IIa; 1.1 t and linear for N). −1 −1 −1 ha (82.0%) for AEZ IIb; 2.7 t ha (61.4%) for AEZ III; 2.4 t ha 26 Page 10 of 16 J. Vasco Silva et al. Fig. 6 Maize yields and yield gaps in Zambia disaggregated by agro- yield gap, ‘Resource Yg ’ = resource yield gap considering the YHF ecological zones (A-D), provinces (B-E), and farm types (C-F). Panels highest farmers’ yields (Y ) as benchmark, ‘Resource Yg ’ = HF Yf −1 in the top row display data in absolute terms (t ha ) and panels in the resource yield gap considering the feasible yield (Yf) as benchmark, bottom row display data in relative terms (% of Yw). Codes: ‘AE’ = ‘Technology Yg’ = technology yield gap. agro-ecological zone, ‘FT’ = farm type, ‘Efficiency Yg’ = efficiency E), whereas the relative contribution of the efficiency and available water, and herbicide use were the key drivers resource yield gaps were ca. 20% and 30% of Yw. In Lusaka of maize yield variability (Table 2). The seed rate had a province (with areas also part of agro-ecological zone I), significant positive effect on Ya with a 1% increase in seed each of the three intermediate yield gaps accounted for ca. rate resulting in 0.33% increase in Ya. There was also a 20% of the total yield gap. The differences in the relative significant effect of variety on Ya, with hybrid varieties of contribution of the efficiency, resource, and technology yielding ca. 13% more than open-pollinated varieties. The yield gaps to the overall yield gap between these two effects of temperature seasonality and replanting on Ya provinces (Southern and Lusaka) and the other provinces were also statistically significant, but the effect was small. is likely attributed to the low water-limited yield simulated, Aridity index and soil available water had a significant and hence small technology yield gap in absolute terms, for positive effect on Ya with a 1% increase in these variables the Southern and Lusaka provinces (and respective agro- resulting into 0.50 and 0.20% increase in Ya. Ya in loamy ecological zone, Figure 6A and B). There were also slightly sand soils were significantly lower (135%) than in clay differences in the causes of yield gaps for the different soils and adoption of erosion and flood control practices farm types (Figure 6C and F): the efficiency yield gap was increased Ya by 5%. N applied had a significant positive slightly greater for FT3 (i.e., market-oriented maize farms) effect on Ya whereas exchangeable acidity had a significant than for FT1 and FT2, whereas the opposite was true for the negative effect on Ya, but in both cases the effect was resource yield gap (Figure 6Cand F). small. Herbicide use had a significant positive effect on Ya, resulting in 12.5% greater Ya compared to fields where 3.4 Determinants of maize yield variability herbicides were not used. Finally, Ya was significantly lower in 2015 and in 2019 than in 2012 (cf. Figure 5A). The The stochastic frontier model fitted to the pooled data time of the first weeding, measured in number of days after revealed that seed rate, variety type, aridity index, soil sowing, had a significant negative effect on the efficiency Narrowing maize yield gaps across smallholder farming systems in Zambia: what interventions, where, and for whom? Page 11 of 16 26 Table 2 Parameter estimates of ZambiaFarmtype1 Farm type2 Farm type3 the stochastic frontier model fitted for maize yield in Production frontier Zambia during the growing seasons of 2010/11, 2013/14, Intercept 2.196 −6.079 # 10.016* 4.120 and 2017/18. The same model Year 2015 −0.080*** −0.049 −0.102** −0.087*** was fitted to the pooled sample Year 2019 −0.271*** −0.193*** −0.280*** −0.265*** (Zambia) and each of the farm types identified (Figure 4). Defining factors Reference values: Year = Growing degrees day −0.108 0.538 # −0.647 # −0.101 ‘2012’, Replant = ‘No’, Temperature seasonality 0.093** 0.277*** −0.127 # −0.045 Variety = ‘OPV’, Drought −1 Seed rate (kg ha ) 0.333*** 0.328*** 0.307*** 0.462*** tolerant = ‘No’, Soil = ‘Clay’, Wetland = ‘No’, Erosion/Flood Replant Yes −0.064*** −0.023 −0.009 −0.112*** = ‘No’, Weeding = ‘One or Variety Hybrid 0.128*** 0.134** 0.084 0.105* none’, Herbicide use = ‘No’, Variety Unknown 0.065*** 0.050 0.028 0.109** Insecticide use = ‘No’. Units: Limiting factors (water) WFO = week from onset of rains; WAS = week after Drought tolerant Yes 0.028 −0.014 0.043 0.045 # sowing. Significance is Drought tolerant Unknown −0.120*** −0.058 −0.139** −0.105** indicated by the codes: ‘***’ Aridity index 0.502*** 0.613*** 0.242* 0.443*** 0.1%, ‘**’ 1%, ‘*’ 5%, ‘#’ 10%. n.a. = not applicable. Rooting depth 0.021 −0.003 0.011 0.019 Soil available water 0.214*** 0.162* 0.261*** 0.164*** Soil Clay loam −0.014 0.084 0.092 −0.117 Soil Loam 0.234 0.032 1.265*** −0.510* Soil Loamy sand −1.353* −1.161 # Soil Sandy clay −0.026 −0.028 0.161 −0.099 Soil Sandy clay loam 0.096 0.114 0.218 # 0.001 Soil Sandy loam 0.110 0.150 0.164 0.037 Wetland Yes −0.035 −0.023 −0.073 # 0.013 Erosion/Flood Yes 0.052** −0.048 0.106** 0.043 # Limiting factors (nutrients) −1 N applied (kg N ha ) 0.026*** 0.021*** 0.016*** 0.024*** pH in H O (unitless) 0.176 0.312 0.618 −0.134 −1 Exch. acidity (cmol+ kg ) −0.018*** −0.010 −0.016 # −0.003 Reducing factors Weeding 2 0.021 0.006 0.103*** −0.012 Weeding 3+ 0.018 0.153** 0.057 −0.085* Herbicide Yes 0.126*** 0.156 # 0.034 0.076* Insecticide Yes 0.087 # 0.181 # 0.055 0.018 Inefficiency effects Sowing date (WFO) 0.008 # 0.018* 0.016 # 0.000 Weeding timing (WAS) −0.052*** −0.096** −0.163*** −0.006 Model evaluation 2 2 2 σ = σ + σ 0.964*** 0.885*** 1.363*** 0.642*** v u 2 2 γ = σ / σ 0.820*** 0.819*** 0.869*** 0.735*** Sample size (n) Field x year combinations (#) 30765 8245 10896 11335 yield gap, meaning that smaller efficiency yield gaps were The significance level and magnitude of the first-order observed when the first weeding was done at later dates, but terms derived from the survey data were comparable in again the effect was small. both the Cobb-Douglas and translog stochastic frontier 26 Page 12 of 16 J. Vasco Silva et al. models (Supplementary Table 3). Yet, variables derived and N applied had a significant positive effect of maize, from secondary sources (temperature seasonality, aridity and a similar effect size, independently of the province index, rooting depth, soil available water, pH in water, (Supplementary Table 4) and the effect of biophysical and exchangeable acidity) showed contrasting signs and variables (e.g., aridity index and soil available water) was different effect sizes (Supplementary Table 3). Quadratic not significant when the model was fitted per province terms were statistically significant for all continuous (Supplementary Table 4). variables, except soil available water (Supplementary Table 3), indicating a quadratic effect of seed rate on Ya and a quadratic positive effect of N applied on Ya (cf. Figure 5E 4 Discussion and F). There were negative interactions between seed rate and growing degree days, aridity index and N applied, Agricultural productivity must increase in sub-Saharan and positive interactions between seed rate and temperature Africa with a view towards improved food security and seasonality and pH in water. N applied showed a negative reduced food imports with minimum crop expansion in interaction with growing degree days, seed rate, rooting biodiversity and carbon-rich natural habitats (e.g., Giller depth and soil available water, meaning that maize yield et al. 2021a; Jayne and Sanchez 2021; Giller 2020; Keating response to N decreased with increases in these variables. et al. 2014). Zambia is no exception to this narrative The effect of seed rate and N applied on maize yield was (Figure 1), where narrowing yield gaps up to 80% of Yw further investigated for highest, average, and lowest yielding is needed for the country to reach cereal self-sufficiency by −1 fields. Maize yield ranged between 0 and 1.5 t ha ,1.5 2050 with cropland expansion (van Ittersum et al. 2016). −1 −1 and 4.0 t ha , and 4.0 and 9.0 t ha for lowest, average, Yield gap closure for rain-fed maize across Zambia is only and highest yielding fields (Figure 5E and F). Seed and ca. 20% of Yw (Figure 6), which is similar for other crops −1 N rates were lowest in lowest yielding fields (16 kg ha in other countries across sub-Saharan Africa (van Ittersum −1 and54kgNha ), intermediate for average yielding fields et al. 2016; Tittonell and Giller 2013). The large yield −1 −1 (23kgha and84kgNha ), and greatest for highest gap of rain-fed maize in Zambia is mostly attributed to −1 −1 yielding fields (25 kg ha and 100 kg N ha ). There were the technology yield gap (Figure 6) indicating that more no major differences in yield and input use for the different efficient production methods are needed to narrow maize farm types across highest, average, and lowest yielding yield gaps. Yet, narrowing efficiency and resource yield fields (data not shown). The quadratic effect of seed rate on gaps through fine tuning current farm practices could more yield was significant for highest and average yielding fields, than double current yields (Figure 6). The latter can be but not for lowest yielding fields (Figure 5E), whereas the achieved through improved timeliness and precision of effect of N applied on yield was linear and positive for management operations and through increases in input use lowest, average, and highest yielding fields (Figure 5F). to levels observed in highest yielding fields (Figures 5E Yield response to N was greatest, intermediate, and smallest and 5F). Similar findings regarding the relative importance for average, highest, and lowest yielding fields, respectively. of efficiency, resource, and technology yield gaps were The drivers of maize yield variability for each farm reported for cereal farming systems in Eastern Africa (Silva type were largely comparable to those observed for the et al. 2019, 2021; Assefa et al. 2020; van Dijk et al. 2017), pooled data (Table 2), as opposed to the results obtained for pointing to the need for making inputs available to farmers Northern, Eastern, and Southern provinces (Supplementary at the right amount, cost, and time, and of targeting and Table 4). For all farm types, seed rate, aridity index, soil packaging technologies in ways that increase adoption at available water, and N applied had a significant positive farm level. effect on Ya and Ya was significantly smaller in 2019 than Seed and N rates, variety, weed control, and sowing date in 2012. Variety type and herbicide use had a positive effect were the most important management drivers of maize yield on Ya for FT1 and FT3, and fields weeded three or more variability in Zambia (Table 2). All these are well-known times yielded 15% more for FT1, and 9% less for FT3, than drivers of maize yield variability in Eastern and Southern fields weeded once or not weeded. Increasing temperature Africa (e.g., Burke et al. 2020; Assefa et al. 2020). First, seasonality by 1% translated into increases in Ya of 28% for seed rate and variety type had a large impact on maize FT1, replanted fields yielded 11% less than non-replanted yield, with a 1% increase in seed rate resulting ca. 0.35% fields for FT3, and fields where erosion or flood control increase in maize yield and hybrid varieties yielding 12% practices were adopted for FT2 had 11% greater Ya than more than traditional OPVs (Table 2). Seed rate might well fields where these practices were not adopted. Also for FT2, be a proxy for plant population, a key factor controlling fields weeded twice yielded 10% more than fields with maize productivity in Southern Africa (Nyagumbo et al. one or no weeding operations. The effects of soil type on under review). Second, the timing of the first weeding Ya were not consistent across farm types. The seed rate operation was an important driver of the efficiency yield Narrowing maize yield gaps across smallholder farming systems in Zambia: what interventions, where, and for whom? Page 13 of 16 26 gap (Table 2), reflecting the importance of timely weeding Ngoma et al. 2021) whereas soil acidity is known to be at the start of the growing season for maize productivity. a major constraint to agricultural production in the humid Third, N fertilizer rate had a linear positive effect on maize areas of Northern Zambia (Pelletier et al. 2020;Burke yield (Figure 5F; Table 2), but the effect size was small due et al. 2017;Pauw 1994). These biophysical constraints to the low amounts of N applied by farmers. In fact, the may impact the adoption of mineral fertilizers to narrow range of N application rates observed in farmers’ fields was resource yield gaps due to the risks involved in areas with considerably lower than that needed to reach 80% of Yw low and erratic rainfall and the low nutrient-use efficiency −1 (i.e., 170–320 kg N ha ;Table 1). Such large N application in areas with acid soils, both with implications beyond rates are out of reach for most smallholders in the country, maize farming in Zambia. Erratic rainfall is widespread and may well not be profitable or desirable under prevailing across much of Eastern and Southern Africa (Muthoni conditions (e.g., input-output markets, infrastructure, and et al. 2019) whereas soil acidity (defined here as low pH soil acidity). Lastly, the effect of timely sowing on maize areas with high levels of exchangeable acidity) affects over productivity was very much related to the onset of the rains half of all countries in sub-Saharan Africa (Silva et al., (Supplementary Figures 3 and 4), and appropriate-scale in preparation). These results support the revision of the mechanization can contribute to timely and more precise subsidy program by the Government of Zambia (Morgan sowing across the region (Baudron et al. 2015). et al. 2019) to make it possible for farmers to access The drivers of maize yield variability were largely mechanized services and inputs (e.g., seeds, fertilizers, and consistent across farm types (Table 2), but the importance lime) and to strengthen extension systems to deliver timely of maize for rural livelihoods across Zambia was farm-type and site-specific agronomic recommendations (Jayne et al. specific (Figure 4). This means that interventions aiming to 2018). This is crucial to improve soil health and sustainably narrow maize yield gaps will likely benefit the different farm intensify maize production in the country. types differently. For instance, boosting maize productivity Further research is needed to understand how fertilizer can be a suitable ‘stepping up’ strategy for market-oriented use is influenced by climate variability and to identify maize farms (FT3), who achieve the highest maize yields profitable soil water conservation technologies for semi-arid in Zambia (Figure 6C). Targeting interventions to this type areas. A range of new technologies building on previous of farm might well be the most effective way to increase conservation agriculture research (e.g., improved legume maize production at national level. Conversely, farms with systems with strip-, double, relay and intercropping, green low levels of assets (FT1 and FT2, Figure 4), for whom manure cover crops, and agroforestry species) are currently ‘stepping out’ of maize production through investments in being tested on-farm in Zambia to address these challenges. new on-farm activities or off-farm activities is likely more For humid areas, it is crucial to revisit past research on suitable, do not seem to have the productive capacity to soil acidity to assess the returns-on-investment associated intensify maize production in the short-term. Yet, increasing with liming or acid soil management strategies (CIMMYT maize yields would be more beneficial for FT2 than for FT1 2021; Burke et al. 2017). Simulated yield ceilings across given the large dependency on bought maize of the former the continent, and respective N rates needed to reach (Figure 4). Clearly, strategies aiming to narrow maize yield such yields (Table 1; van Ittersum et al., 2016), should gaps must thus be complemented with a suite of pro-poor also be thoroughly tested against empirical data as they policies and investments tailored to specific farm types. are well above maximum yields reported in agronomic This will be crucial to stimulate and embed smallholder experiments under controlled conditions (see Masuka et al. agriculture into a broader rural development program that 2017; Mupangwa et al. 2017 for examples in Zambia). can provide social safety nets in the absence of livelihood High rainfall variability makes rain-fed farming across options off-farm (Giller et al. 2021a). Eastern and Southern Africa a risky activity for small- Maize production in Zambia takes place across a holders. Site-specific recommendations must thus consider gradient of agro-ecological conditions, which in turn have year-to-year variation in profitability and smallholders’ risk a considerable impact on yield gaps and their causes profile to cope with uncertain yield response to inputs throughout the country (Figure 6; Supplementary Table 4). (Descheemaeker et al. 2016), as these are known to con- For instance, our analysis indicates that a 1% increase strain farmers’ willingness to investment in technologies. in soil available water translates into ca. 0.20% greater More attention must be paid to incorporate the effects of maize yield and that a 1% decrease in exchangeable acidity rainfall variability and soil properties on yield response to results into a 0.02% increase in maize yield across the inputs to better explain the adoption of technologies (Cham- pooled sample (Supplementary Table 4). Water is indeed berlin et al. 2021; Burke et al. 2017), which appear to be a key limiting factor to production in the semi-arid areas profitable on average, but have high variance in outcomes of Southern and Western Zambia (Table 1, Figure 2; over time. The role of non-information constraints, such as 26 Page 14 of 16 J. Vasco Silva et al. alternative uses of labor at critical periods (Silva et al. 2019; to inter-annual rainfall variability. Blanket, one-size-fits- Kamanga et al. 2014), to the adoption of improved crop all, recommendations should be avoided when promoting management practices also needs to be explored as these sustainable intensification practices aiming to increase can limit the timely management needed to narrow yield yields in the country. gaps. Small farm sizes are another important constraint to Supplementary Information The online version contains supple- technology adoption and intensification of crop production mentary material available at https://doi.org/10.1007/s13593-023- in African smallholder farming systems (Harris and Orr 00872-1. 2014), as narrowing yield gaps on small farms is often not Acknowledgments We thank the Indaba Agricultural Policy Research enough to ensure food self-sufficiency or a living income at Institute (IAPRI) for having availed the three waves of panel data household level (Giller et al. 2021b). to be used to conduct this research and dr. Antony Chapoto for his constructive suggestions in an earlier version of the manuscript. The manuscript reflects the opinions of the authors but do not necessary represent the institutional policies, opinions and strategies of the 5 Conclusion European Union, FAO and CIMMYT. Maize is the dominant crop in Zambian farming systems, Authors’ contributions Conceptualization: JVS, FB, CT. Software: which range from mixed-crop livestock systems in semi- JVS, FB, HN. Validation: HN, IN, ES, KK. Formal analysis: JVS. Investigation: JVS. Resources: FB, HN, IN, MH, MM, CT. arid areas of the Southern and Western provinces to mixed Data curation: JVS, HN. Writing—original draft preparation: JVS. maize systems in the rest of the country. This study Writing—review and editing: JVS, FB, HN, IN, CT. Supervision: combined for the first time a farm typology delineation FB, CT. Project administration: MH, ES, KK, MM, CT. Funding with yield gap decomposition to gain insights on what acquisition: MM, CT. interventions are needed, where, and for which farm types, Funding This research was conducted under the European Union- to increase maize production in Zambia. Three farm types funded “Sustainable Intensification of Smallholder Farming Systems were identified, including households for which maize in Zambia project” (SIFAZ, Grant No FED/2019/400-893), jointly is a marginal crop, households which are net buyers of implemented by the Ministry of Agriculture of Zambia, the Food and Agriculture Organization of the United Nation (FAO), and the maize, and households which are market-oriented maize International Maize and Wheat Improvement Center (CIMMYT). producers. Maize yield gap closure across the country was Financial and logistical support is gratefully acknowledged. only 20% of the water-limited yield (Yw), corresponding −1 to 2.4 t ha , and was slightly larger for market- Data availability The datasets generated during and/or analyzed during the current study are not publicly available due privacy reasons oriented maize farms. For nearly all agro-ecological regions, but are available from the corresponding author on reasonable request. provinces, and farm types, about half of the yield gap was attributed to current technologies used by farmers Code availability The R and Python scripts used in the analysis are not reaching their full agronomic potential. Yet, improving available upon request at the GitHub repository https://github.com/ jvasco323/SIFAZ Yg Decomposition. current technologies in terms of timeliness and precision of operations and increasing input use, particularly mineral Declarations fertilizers, could more than double current yields. Doing so requires targeted approaches for technology intervention, Ethics approval Not applicable e.g., by focusing on market-oriented maize producers, accompanied by carefully designed policy interventions Consent to participate Not applicable ensuring other households benefit from other value chains or off-farm opportunities. If profitable, adoption of practices Consent for publication Not applicable that increase soil moisture in semi-arid areas, such as Conﬂict of interest The authors declare no competing interests. conservation agriculture, and management of soil acidity in humid areas are key to improve yield response to mineral Open Access This article is licensed under a Creative Commons fertilizers. Two avenues can facilitate the foregoing policy Attribution 4.0 International License, which permits use, sharing, levers. First, the current national subsidy program needs adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the to be flexible enough to make it possible for farmers source, provide a link to the Creative Commons licence, and indicate to access mechanized services and inputs. Second, the if changes were made. The images or other third party material in this extension systems need to be strengthened to help farmers article are included in the article’s Creative Commons licence, unless cope with risk and uncertain crop yield response to inputs indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended in areas with high rainfall variability. Further research use is not permitted by statutory regulation or exceeds the permitted is needed to better understand the profitability of maize use, you will need to obtain permission directly from the copyright production under rain-fed conditions and to disseminate holder. To view a copy of this licence, visit http://creativecommons. technologies that can reduce the vulnerability of farmers org/licenses/by/4.0/. Narrowing maize yield gaps across smallholder farming systems in Zambia: what interventions, where, and for whom? 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Agronomy for Sustainable Development – Springer Journals
Published: Apr 1, 2023
Keywords: Food security; Sustainable intensification; Farm typology; Global Yield Gap Atlas; Fertilizer input subsidy program
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