Mapping Water Stress Incidence and Intensity, Optimal Plant Populations, and Cultivar Duration for African Groundnut Productivity Enhancement

Abstract

Groundnut production is limited in Sub-Saharan Africa and water deficit or “drought,” is often considered as the main yield-limiting factor. However, no comprehensive study has assessed the extent and intensity of “drought”-related yield decreases, nor has it explored avenues to enhance productivity. Hence, crop simulation modeling with SSM (Simple Simulation Modeling) was used to address these issues. To palliate the lack of reliable weather data as input to the model, the validity of weather data generated by Marksim, a weather generator, was tested. Marksim provided good weather representation across a large gradient of rainfall, representative of the region, and although rainfall generated by Marksim was above observations, run-off from Marksim data was also higher, and consequently simulations using observed or Marksim weather agreed closely across this gradient of weather conditions (root mean square of error = 99 g m-2; R2 = 0.81 for pod yield). More importantly, simulation of yield changes upon agronomic or genetic alterations in the model were equally predicted with Marksim weather. A 1° × 1° grid of weather data was generated. “Drought”-related yield reduction were limited to latitudes above 12–13° North in West Central Africa (WCA) and to the Eastern fringes of Tanzania and Mozambique in East South Africa (ESA). Simulation and experimental trials also showed that doubling the sowing density of Spanish cultivars from 20 to 40 plants m-2 would increase yield dramatically in both WCA and ESA. However, increasing density would require growers to invest in more seeds and likely additional labor. If these trade-offs cannot be alleviated, genetic improvement would then need to re-focus on a plant type that is adapted to the current low sowing density, like a runner rather than a bush plant type, which currently receives most of the genetic attention. Genetic improvement targeting “drought” adaptation should also be restricted to areas where water is indeed an issue, i.e., above 12–13°N latitude in WCA and the Eastern fringes of Tanzania and Mozambique.

Agricultural Policy Environmental eXtender (APEX) Simulation of Spring Peanut Management in the North China Plain

“…These negative effects on pod yield could be due to greater competition for light and water among plants at high plant densities or the lower efficiency of resource use at extremely low plant density [23,62]. Consistent with previous observations [25,63,64], these results indicate that optimum plant density greatly improves the canopy structure and light interception, which enhance nutrient and water use efficiency, and finally help produce the highest pod yield.…”

Relevance of limited-transpiration trait for lentil ( Lens culinaris Medik.) in South Asia

“…Considering the breadth of geographical area and environments in which lentil is grown in South Asia (Erskine et al, 2009), an assessment of having a lower BP in lentil can only be done by using simulations done over the range of locations and weather conditions. Large scale assessment of this trait using a mechanistic model has been previously reported for soybean production in the USA (Sinclair et al, 2010), Africa (Sinclair et al, 2014) and groundnut in sub-Saharan Africa (Vadez et al, 2017).…”

Accelerating genetic gains in legumes for the development of prosperous smallholder agriculture: integrating genomics, phenotyping, systems modelling and agronomy

“…Crop models, with general genetic inputs, suggest how a given combination of alleles confers a positive or negative effect on plant performance in different locations and seasons (Hammer et al, 2002;Tardieu and Tuberosa, 2010;Messina et al, 2011). Modelling enables us to predict the effect of a change in the biological architecture of a plant type (for instance having faster growing roots, Vadez et al, 2012; for water use efficiency and stay green, Kholová et al, 2014), or of a change in the agronomic management (for instance increasing planting density, Vadez et al, 2017). As such, modelling should become an important resource-saving tool to guide the choice of breeding and agronomic management investment.…”

“…For legumes specifically, a model has been developed (Simple Simulation Modelling; SSM) that uses the same model architecture across a range of grain legumes and is easy to use . SSM has been used successfully to predict growth and yield in chickpea (Soltani and Sinclair, 2011;Vadez et al, 2012Vadez et al, , 2013b, lentil (Ghanem et al, 2015a,b), common bean , soybean , and groundnut (Vadez et al, 2017). In the case of soybean, a recent modelling study indicated that a faster rate of root growth has a negative impact on yield as available soil moisture was depleted faster by more vigorous root growth (Sinclair et al, 2010).…”