## 1 Introduction  
Rainwater capture and its storage in the soil are important for the success of semi-arid cropping systems in sub-Saharan Africa (SSA) [1, 2]. Rain-fed smallholder farming systems are projected to continue contributing about 40% of growth in cereal production [3]. Various rainwater harvesting (RWH) and soil water conservation techniques have been developed and adapted for smallholder farming systems in SSA. Rainwater harvesting is the practice of collecting rainwater and storing it for future use on the farm. _Zai_ pits, dead level contours, infiltration pits, _Fanya juns_, tied ridging, and potholing are some of the RWH techniques adapted to the smallholder systems of SSA [2, 4, 5, 6]. In addition, conservation tillage and conservation agriculture practices have been developed for rain and soil water management in cropping systems on smallholder farms [7, 8, 9, 10]. Agricultural water management in rain-fed farming systems of sub-Saharan Africa and similar environments is now paramount in the face of increasing drought and heat stresses [11]. RWH technologies, such as planting basins and ripped furrows, increase the residence time of rainwater on the soil surface, thereby promoting water infiltration into the soil. The increased water infiltration reduces surface runoff generation and promotes soil water recharge [12; 13]. However, the runoff potential of the promoted RWH techniques has not been evaluated in most parts of SSA, including southern Africa. The runoff potential of these RWH techniques can be evaluated by hydrologic models and the curve number (_CN_) approach, which use measured runoff and daily rainfall [14; 15; 16]. When rainfall and runoff data are available, the runoff potential of RWH techniques can be assessed using the _CN_ approach [15]. The _CN_ method is a procedure used to determine the potential runoff from a rainfall event in a given land area [17]. A _CN_ can be defined as the proportion of rainfall that gets converted into runoff in a given surface area, and its value ranges from 0 to 100 [18; 19]. The soil's hydrological conditions, soil type, antecedent soil moisture content, land use, and the treatment applied (e.g., tillage or mulching) to a given area of interest, influence the _CN_[20; 21]. The importance of _CN_s in agricultural research includes the fact that _CN_s are an important input parameter for crop models and can be used for water-logging forecasting in cropping systems, the modification of soil water conservation techniques for increased infiltration, and a better understanding of water balance at plot or field level [15; 20; 22; 23]. In agricultural land-use systems, _CN_s of 60-90 have been reported in studies conducted by the [24]. In SSA, cropping systems modelling studies used _CN_s of 50-90, but these were largely derived from United States Department of Agriculture (USDA)-based systems, researchers' experience of the tillage systems, and simulated physical environments [25; 26; 27; 28; 29]. In addition, some of the _CN_s reported in the literature are determined from annual rainfall totals [30], which might be different from values estimated from event rainfall recorded at a given location. Curve numbers of 61-91 in row crops such as legumes, 58-88 in small grains like sorghum (_Sorghum bicolor_ L.), and 51-89 in closely planted legumes have been reported [24]. In conventionally plobined soils without surface cover, _CN_s ranging from 50 to 90 have been applied in previous crop systems modelling studies using the Agricultural Production Systems Simulator (APSIM) model [25; 26; 29]. In no-till and conservation-agriculture-based cropping systems, _CN_s for bare soil ranging from 75 to 80 have been used for simulations using the APSIM model [31; 32].