Exploring Niches for Short-Season Grain Legumes in Semi-Arid Eastern Kenya — Coping with the Impacts of Climate Variability

Abstract

Climate variability is the major risk to agricultural production in semi-arid agroecosystems and the key challenge to sustain farm livelihoods for the 500 million people who inhabit these areas worldwide. Short-season grain legumes have great potential to address this challenge and help to design more resilient and productive farming systems. However, grain legumes display a great diversity and differ widely in growth, development, and resource use efficiency. Three contrasting short season grain legumes common bean (Phaseolus vulgaris L.), cowpea (Vigna unguiculata (L.) Walp.] and lablab [Lablab purpureus (L.) Sweet] were selected to assess their agricultural potential with respect to climate variability and change along the Machakos-Makueni transect in semi-arid Eastern Kenya. This was undertaken using measured data [a water response trial conducted during 2012/13 and 2013/14 in Machakos, Kenya] and simulated data using the Agricultural Production System sIMulator (APSIM). The APSIM crop model was calibrated and validated to simulate growth and development of short-season grain legumes in semi-arid environments. Water use efficiency (WUE) was used as indicator to quantify the production potential. The major traits of adaptation include early flowering and pod and seed set before the onset of terminal drought. Early phenology together with adapted canopy architecture allowed more optimal water use and greater partitioning of dry matter into seed (higher harvest index). While common bean followed a comparatively conservative strategy of minimizing water loss through crop transpiration, the very short development time and compact growth habit limited grain yield to rarely exceed 1,000 kg ha−1. An advantage of this strategy was relatively stable yields independent of in-crop rainfall or season length across the Machakos-Makueni transect. The growth habit of cowpea in contrast minimized water loss through soil evaporation with rapid ground cover and dry matter production, reaching very high grain yields at high potential sites (3,000 kg ha−1) but being highly susceptible to in-season drought. Lablab seemed to be best adapted to dry environments. Its canopy architecture appeared to be best in compromising between the investment in biomass as a prerequisite to accumulate grain yield by minimizing water loss through soil evaporation and crop transpiration. This lead to grain yields of up to 2,000 kg ha−1 at high potential sites and >1,000 kg ha−1 at low potential sites. The variance of observed and simulated WUE was high and no clear dependency on total rainfall alone was observed for all three short-season grain legumes, highlighting that pattern of water use is also important in determining final WUEbiomass and WUEgrain. Mean WUEgrain was lowest for cowpea (1.5–3.5 kggrain ha−1 mm−1) and highest for lablab (5–7 kggrain ha−1 mm−1) reflecting the high susceptibility to drought of cowpea and the good adaptation to dry environments of lablab. Results highlight that, based on specific morphological, phonological, and phys...

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

“…Teixeira et al (2015), using APSIM, demonstrated the importance of rotations for simulating climate impact assessments and Sennhenn et al (2017) found new niches for short season legumes in Kenya. APSIM has been used in simulation studies for yield gap assessment in legumes such as soybean, groundnut, pigeonpea, and chickpea in India (Bhatia et al, 2007;Chauhan et al, 2008), in simulation of soil temperature in the podding zone of groundnut (Chauhan et al, 2007), and in assessment of the impacts of fertilizers and legumes on N 2 O and CO 2 emissions from soils in subtropical agricultural systems (Huth et al, 2010).…”

“…As a result, the genetic gain achieved is about 1% per year in several species (Duvick, 2005;Cooper et al, 2009;Brisson et al, 2010;Lopes et al, 2012;Aisawi et al, 2015). The most promising option to design more drought resilient and sustainable production is to target the major traits of adaptation that include early flowering and seed set before the onset of terminal drought (Sennhenn et al, 2017).…”

“…Genomics-assisted breeding (GAB; Varshney et al, 2005) was proposed to integrate genomics in breeding and it has been quite successful for several traits in cereals (Septiningsih et al, 2013;Varshney et al, 2006) and legumes (Varshney, 2016;Pratap et al, 2017). Recently, Reynolds and Langridge (2016) have suggested crossing parents with different complex but complementary traits to achieve cumulative gene action for yield, while selecting progeny using remote sensing in combination with genomic selection.…”

Understanding the response of sorghum cultivars to nitrogen applications in the semi-arid Nigeria using the agricultural production systems simulator

“…This implies that yield losses for early to medium maturing sorghum cultivars may not be associated to erratic rainfall pattern but rather lack of nitrogen in the soil because the physiological potential of the cultivars falls within received in-season rainfall. It is, however, expected that the increase temperature might have a more severe impacts on sorghum productivity as it accelerates vegetative growth and maturity processes irrespective of whether rainfall increases or decreases in the semiarid area (Sultan et al 2014;Sennhenn et al 2017). Furthermore, the analysis of variance (ANOVA) indicates that simulated grain yield and total biomass were highly significant (p < .001) due to N-application rates and cultivars while significant interaction of N-rate and cultivar (N Â C) existed only for grain yield and total biomass in Kano and total biomass in Samaru (Table 5).…”

Intercropping in Zimbabwe conservation agriculture systems using a farmer-participatory research approach

“…Most of this crop production is characterized by limited application of inputs (due to the high cost and limited availability of agricultural inputs including seeds, fertilizers and agricultural chemicals), *Corresponding author. E-mail: M_entz@umanitoba.ca Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution License 4.0 International License deteriorating soil conditions (Vagen et al, 2005) and increasingly uncertain weather patterns (Sennhenn et al, 2017).…”