The rapid human population growth and increasing demand for agricultural products, including food and fodder, is putting pressure on agricultural production systems and environmental resources. Sustainable intensification aims to maximise primary production with effective resource use under consideration of ecological processes which contribute to regulate the productivity in agroecosystems (Tittonell and Giller, 2013).
Even if food production from smallholder farming systems is the backbone of global food production, large yield gaps are widespread, in particular in African smallholder farming systems. Moreover, many semi-arid farming systems are becoming less diverse, and consequently, less resilient and nutritionally secure (Lenné and Wood, 2011; Tscharntke et al., 2012). However, particularly resource-constrained agricultural systems strongly rely on biodiversity and associated ecological processes (e.g. stress-adapted crop types, integrated soil fertility management) (Jackson et al., 2007). A ´The paradox of scale` or the ´inverse farm size-productivity relationship` - concepts, which are controversially discussed among economist, agronomist and ecologist - further emphasise that small, diversified farms are more productive than large monocultures (Barrett et al., 2009;
Horlings and Marsden, 2011). To increase the agroecological capacity through a better integration of multiple crop types and varieties in smallholder farming systems is, therefore, a key strategy to fight the world’s food security and protect environmental resources. Grain legumes are valuable components in smallholder farming systems of semi-arid areas in Eastern Kenya as they contribute to food and nutrition security and help to manage and restore soil fertility. Increased climate variability however puts additional pressure on these vulnerable systems. Nevertheless, legumes have a great agro-morphological diversity, including varying drought and heat response and adaption mechanisms. In particular, short-season varieties offer new options for farming with increased rainfall variability and restricted growing periods as their adaption strategy of completing the life cycle before the onset of terminal drought seems to be advantageous for cropping with frequent dry spells in semi-arid areas (Loss and Siddique, 1994). The characterization of physiological and growth response to resources and management is, however, a fundamental first step in order to identify niches for new and exciting crop types with multi-purpose benefits for small-scale farming systems. Information on resource capture from field experiments, in particular the utilization of light and water of promising short-season grain legumes in semi-arid environments is, however, largely missing. The first part of this PhD thesis aims, therefore, to analyse the response of three short-season grain legumes to environmental conditions and different management interventions in semi-arid Eastern Kenya (Figure 4).
Two field trials, including a water response and plant density trial, which were conducted over two seasons in semi-arid Eastern Kenya, were designed to quantify the effect of plant population and water availability on crop growth and development to evaluate resource use and use-efficiency with special focus on RUE and WUE. Of particular focus in this thesis are the short-season varieties of two major grain legumes; common bean (Phaseolus vulgaris L.) and cowpea (Vigna unguiculata (L.) Walp.), which are widely utilized in Eastern Kenya. In addition, lablab (Lablab purpureus (L.) Sweet) was selected because of its potential adaption to the region and its local farming systems (Maass et al., 2010). In addition to the field experiments in semi-arid Eastern Kenya, the photoperiod sensitivity of promising short-season lablab accessions was evaluated in an exemplary analysis of combined field and controlled environment data. An improved physiological understanding of the photoperiod response can contribute to better estimate phenological events, such as flowering and maturity with the aim to assess the potential adaption of early-flowering lablab accessions to (sub)-tropical environments as a climate smart farming practice.
In order to explore the potential of certain crops and cropping strategies in diverse and dynamic smallholder farming systems under varying environmental conditions the development and application of crop growth simulation models has been proved to be an excellent tool (Whitbread et al. 2010). Combining field/crop simulation and farm level analysis is necessary to better understand the complexity of genotyp x environment interactions. One of the most applicable models to better understand the complexity of plant growth in response to the environment has been the Agricultural Production System sIMulator (APSIM) framework (Holzworth et al., 2014; Keating et al., 2003).
Roberstson et al. (2002) defined and estimated key physiological parameters necessary for modelling legumes growth and development.
Further, the conception of modules to simulate growth and development of further grain and forage legumes such as cowpea (Adiku et al., 1993), soybean (Robertson and Carberry, 1998), pigeonpea (Robertson et al., 2001), mungbean (Robertson et al., 2002) and fababean (Turpin et al., 2002; Turpin et al., 2003), and improvements to the overall module design made by Robertson et al. (2002) the model capability for the simulation of legume production and productivity was enhanced. Despite these efforts in model enhancement, there is very limited published research on the growth and development of short-season legumes, in particular for semi-arid environments. Model validation and testing has focused mainly on Australian production systems and the vegetative or forage types of cowpea and lablab.
The second part of the PhD thesis, therefore, focuses on the estimation of key physiological parameters necessary to parameterize and validate the crop growth model APSIM for the short-season legumes (Figure 4). Further, the objectives were to collect soil and weather information for semi-arid Eastern Kenya to be used in the simulations. If calibrated well, crop growth models can function as powerful tools to explore the potential impact of internal and external factors, including management strategies or the evaluation of climate change effects on growth and development of short-season grain and multi-purpose legumes (Carberry et al., 2002; Cooper et al., 2008; Tittonell and Giller, 2013). The ex-ante assessment through simulation models can help to better identify entry points for short-season grain legumes in existing farming systems of semi-arid Eastern Kenya. Consequently, the last part of the PhD thesis aims first to upscale results from field experiments and characterize possible responses of the short-season grain legumes to different management interventions and environmental conditions, including climate change (temperature and water stress) to estimate their agricultural production potential through multi-site simulations (Figure 4). Finally, the objective of the PhD thesis was to use experimental results together with the model outputs to better design strategies for climate smart agriculture in smallholder farming systems of Eastern Kenya to identify intervention opportunities and pathways towards the sustainable intensification of smallholder systems in sub-Saharan Africa and, thereby, increase food and nutrition security by minimizing the vulnerability to climate variability and change.
Re-assessment and evaluation
Key challenges:
Enhance soil fertility Manage climate risk Increase resource use efficiency
Design more resilient farming systems
Improve food and nutrition security
Can short-season grain legumes contribute to more resilient and productive farming systems in semi-arid Eastern Kenya?
I. Comparing the performance of potential legume species/ varieties
Contribution to:
1
system (re)-design discussion support research agenda
policy making II. Capturing the
physiological information in crop growth models
III. Delivering strategies to design lower risk farming
systems On-station
field trials
Simulation model output analysis Simulation model
calibration and validation
Climate variability and ex-ante assessment analysis
Figure 4: Conceptual framework of the PhD thesis including presentation of the research needs, major tasks and objectives and their trade-offs.
On-farm methods Computer-based