Performance of elite maize genotypes under selected sustainable intensification options in Kenya.

Combining different cropping and tillage systems with different genotypes across several cropping seasons can reveal opportunities for sustainable intensification (SI). The objective of this study was to assess the performance of six maize genotypes under intercropping with conservation tillage (no-till) - two promising options for SI. The experiment was carried out over three years (or six cropping seasons) at Kiboko Research Station, Kenya with sole cropping and mouldboard ploughing as baseline production systems. Results showed that maize genotypes and cropping systems had a significant effect on yield, but the effect of tillage was not significant. Moreover, there was no significant interactive effects of the tested factors on maize yield. The maize genotype CKH10085 had the highest yield of 7.7 t ha-1 under sole cropping yet it also recorded the largest yield penalty due to intercropping of 1.1 t ha-1. On the other hand, genotype CKH10717 maintained the same average yield of 7.1 t ha-1 in both conventional and conservation tillage systems. The commercial genotype genotype CKH10080 and CKH08051 were more stable than the other experimental genotypes under the variable growing and management conditions. These two genotypes are of intermediate maturity and drought tolerance, two critical attributes to improved maize production. Intercropping reduced maize yields due to increased competition, for example the overall yield of sole cropping was 7.1 t ha-1 compared with 6.4 t ha-1 under intercropping; representing an overall yield penalty of 0.7 t ha-1. The differences in performance of maize genotypes revealed opportunities to deploy genotypes to reduce risk or maximize yield, depending on the biophysical circumstances and the production objective of the farmer.

Assessing adoption potential in a risky environment: The case of perennial pigeonpea.

Perennial crops offer the opportunity to harvest from the same plant many times over several years while reducing labor and seed costs, reducing emissions and increasing biomass input into the soil. We use system dynamics modeling to combine data from field experiments, crop modeling and choice experiments to explore the potential for adoption and diffusion of a sustainable agriculture technology in a risky environment with high variability in annual rainfall: the perennial management of pigeonpea in maize-based systems of Malawi. Production estimates from a crop model for the annual intercrop system and data from field experiments on ratooning for the perennial system provided the information to create a stochastic production model. Data from choice experiments posed by a farmer survey conducted in three Malawi districts provide the information for parameters on farmers' preferences for the attributes of the perennial system. The perennial pigeonpea technology appeared clearly superior in scenarios where average values for maize yield and pigeonpea biomass production were held constant. Adoption was fastest in scenarios where relatively dry growing seasons showcased the benefits of the perennial system, suggesting that perennial management may be appropriate in marginal locations. The potential for adoption was reduced greatly when stochasticity in yields and seasons combine with significant social pressure to conform. The mechanism for this is that low yields suppress adoption and increase disadoption due to the dynamics of trust in the technology. This finding is not unique to perennial pigeonpea, but suggests that a critical factor in explaining low adoption rates of any new agricultural technology is the stochasticity in a technology's performance. Understanding how that stochasticity interacts with the social dynamics of learning skills and communicating trust is a critical feature for the successful deployment of sustainable agricultural technologies, and a novel finding of our study.

DOES SIZE MATTER? A CRITICAL REVIEW OF META-ANALYSIS IN AGRONOMY.

Intended to test broad hypotheses and arrive at unifying conclusions, meta-analysis is the process of extracting, assembling, and analyzing large quantities of data from multiple publications to increase statistical power and uncover explanatory patterns. This paper describes the ways in which meta-analysis has been applied to support claims and counter-claims regarding two topics widely debated in agricultural research, namely organic agriculture (OA) and conservation agriculture (CA). We describe the origins of debate for each topic and assess prominent meta-analyses considering data-selection criteria, research question framing, and the interpretation and extrapolation of meta-analytical results. Meta-analyses of OA and CA are also examined in the context of the political economy of development-oriented agricultural research. Does size matter? We suggest that it does, although somewhat ironically. While meta-analysis aims to pool all relevant studies and generate comprehensive databases from which broad insights can be drawn, our case studies suggest that the organization of many meta-analyses may affect the generalizability and usefulness of research results. The politicized nature of debates over OA and CA also appear to affect the divergent ways in which meta-analytical results may be interpreted and extrapolated in struggles over the legitimacy of both practices. Rather than resolving scientific contestation, these factors appear to contribute to the ongoing debate. Meta-analysis is nonetheless becoming increasingly popular with agricultural researchers attracted by the power for the statistical inference offered by large datasets. This paper consequently offers three suggestions for how scientists and readers of scientific literature can more carefully evaluate meta-analyses. First, the ways in which papers and data are collected should be critically assessed. Second, the justification of research questions, framing of farming systems, and the scales at which research results are extrapolated and discussed should be carefully evaluated. Third, when applied to strongly politicized topics situated in an arena of scientific debate, as is the case with OA and CA, more conservative interpretations of meta-analytical results that recognize the socially and politically embedded nature of agricultural research is are needed.