A Spatial Autoregressive Graphical Model with Applications in Intercropping

Within the statistical literature, there is a lack of methods that allow for asymmetric multivariate spatial effects to model relations underlying complex spatial phenomena. Intercropping is one such phenomenon. In this ancient agricultural practice multiple crop species or varieties are cultivated together in close proximity and are subject to mutual competition. To properly analyse such a system, it is necessary to account for both within- and between-plot effects, where between-plot effects are asymmetric. Building on the multivariate spatial autoregressive model and the Gaussian graphical model, the proposed method takes asymmetric spatial relations into account, thereby removing some of the limiting factors of spatial analyses and giving researchers a better indication of the existence and extend of spatial relationships. Using a Bayesian-estimation framework, the model shows promising results in the simulation study. The model is applied on intercropping data consisting of Belgian endive and beetroot, illustrating the usage of the proposed methodology. An R package containing the proposed methodology can be found on https:// CRAN.R-project.org/package=SAGM

Quantifying the prevalence of (non)-response to fertilizers in sub-Saharan Africa using on-farm trial data

Open Access Article; Published online: 21 Oct 2021Poor and variable crop responses to fertilizer applications constitute a production risk and may pose a barrier to fertilizer adoption in sub-Saharan Africa (SSA). Attempts to measure response variability and quantify the prevalence of non-response empirically are complicated by the fact that data from on-farm fertilizer trials generally include diverse nutrients and do not include on-site replications. The first aspect limits the extent to which different studies can be combined and compared, while the second does not allow to distinguish actual field-level response variability from experimental error and other residual variations. In this study, we assembled datasets from 41 on-farm fertilizer response trials on cereals and legumes across 11 countries, representing different nutrient applications, to assess response variability and quantify the frequency of occurrence of non-response to fertilizers. Using two approaches to account for residual variation, we estimated non-response, defined here as a zero agronomic response to fertilizer in a given year, to be relatively rare, affecting 0–1 and 7–16% of fields on average for cereals and legumes respectively. The magnitude of response could not be explained by climatic and selected topsoil variables, suggesting that much of the observed variation may relate to unpredictable seasonal and/or local conditions. This implies that, despite demonstrable spatial bias in our sample of trials, the estimated proportion of non-response may be representative for other agro-ecologies across SSA. Under the latter assumption, we estimated that roughly 260,000 ha of cereals and 3,240,000 ha of legumes could be expected to be non-responsive in any particular year

What works where and for whom? Farm Household Strategies for Food Security across Uganda

East Africa's smallholder agriculture is expected to be strongly affected by climate change, which, together with a growing population and pressure on natural resources, will result in an increasing challenge to achieve food security for households and regions. Policy makers need information on what works where for which farmers in order to guide their decision making and prioritise investment for agricultural interventions to increase food security. For this, we must better understand how smallholder farm strategies for achieving food security differ across regions and farm types and what drives these strategies. In this study we present new analyses at country and farm household level that quantify drivers of productivity and food security, and that can be used to prioritise agricultural interventions. Uganda was chosen as a case study because of data availability but the approach can be applied to other countries in sub-Saharan Africa. First, we quantified how food security and farm types varied across Uganda, and which key factors drive this variability. We used household level data from the Living Standard Measurement Study – Integrated Survey on Agriculture (LSMS-ISA) of the World Bank and developed an approach to map and quantitatively explain food security and agricultural land use across Uganda. The resulting maps showed where which crops and livestock activities are important for which types of farm households. Subsequently, the effects of agricultural interventions on food security of different farm types were assessed. Second, we used this information to select contrasting sites and farm households for detailed interviews, which aimed at identifying drivers of farmers' decision making, assessing farmers' vulnerability to climate change and how proposed interventions match with the farmers' socio-ecological niche. The spatial approach we developed is a novel way to use farm household level information to generate country-wide patterns in farming systems and their productivity. It generates useful information for a quantitative assessment of what might happen to the food security of smallholder farmers in Uganda under climate change and for a country-wide targeting of agricultural interventions that aim at mitigating the effects of climate change

Big data, small explanatory and predictive power: Lessons from random forest modeling of on-farm yield variability and implications for data-driven agronomy

Context Collection and analysis of large volumes of on-farm production data are widely seen as key to understanding yield variability among farmers and improving resource-use efficiency. Objective The aim of this study was to assess the performance of statistical and machine learning methods to explain and predict crop yield across thousands of farmers’ fields in contrasting farming systems worldwide. Methods A large database of 10,940 field-year combinations from three countries in different stages of agricultural intensification was analyzed. Random effects models were used to partition crop yield variability and random forest models were used to explain and predict crop yield within a cross-validation scheme with data re-sampling over space and time. Results Yield variability in relative terms was smallest for wheat and barley in the Netherlands and for wheat in Ethiopia, intermediate for rice in the Philippines, and greatest for maize in Ethiopia. Random forest models comprising a total of 87 variables explained a maximum of 65 % of cereal yield variability in the Netherlands and less than 45 % of cereal yield variability in Ethiopia and in the Philippines. Crop management related variables were important to explain and predict cereal yields in Ethiopia, while predictive (i.e., known before the growing season) climatic variables and explanatory (i.e., known during or after the growing season) climatic variables were most important to explain and predict cereal yield variability in the Philippines and in the Netherlands, respectively. Finally, model cross-validation for regions or years not seen during model training reduced the R2 considerably for most crop x country combinations, while for wheat in the Netherlands this was model dependent. Conclusion Big data from farmers’ fields is useful to explain on-farm yield variability to some extent, but not to predict it across time and space. Significance The results call for moderate expectations towards big data and machine learning in agronomic studies, particularly for smallholder farms in the tropics where model performance was poorest independently of the variables considered and the cross-validation scheme used

Development of a Nasonia vitripennis outbred laboratory population for genetic analysis

The parasitoid wasp genus Nasonia has rapidly become a genetic model system for developmental and evolutionary biology. The release of its genome sequence led to the development of high-resolution genomic tools, for both interspecific and intraspecific research, which has resulted in great advances in understanding Nasonia biology. To further advance the utility of Nasonia vitripennis as a genetic model system and to be able to fully exploit the advantages of its fully sequenced and annotated genome, we developed a genetically variable and well-characterized experimental population. In this study, we describe the establishment of the genetically diverse HVRx laboratory population from strains collected from the field in the Netherlands. We established a maintenance method that retains genetic variation over generations of culturing in the laboratory. As a characterization of its genetic composition, we provide data on the standing genetic variation and estimate the effective population size (Ne ) by microsatellite analysis. A genome-wide description of polymorphism is provided through pooled resequencing, which yielded 417 331 high-quality SNPs spanning all five Nasonia chromosomes. The HVRx population and its characterization are freely available as a community resource for investigators seeking to elucidate the genetic basis of complex trait variation using the Nasonia model syste

Soyabean response to rhizobium inoculation across sub-Saharan Africa: Patterns of variation and the role of promiscuity

Article purchased; Published online: 7 Sept 2017Improving bacterial nitrogen fixation in grain legumes is central to sustainable intensification of agriculture in sub-Saharan Africa. In the case of soyabean, two main approaches have been pursued: first, promiscuous varieties were developed to form effective symbiosis with locally abundant nitrogen fixing bacteria. Second, inoculation with elite bacterial strains is being promoted. Analyses of the success of these approaches in tropical smallholder systems are scarce. It is unclear how current promiscuous and non-promiscuous soyabean varieties perform in inoculated and uninoculated fields, and the extent of variation in inoculation response across regions and environmental conditions remains to be determined. We present an analysis of on-farm yields and inoculation responses across ten countries in Sub Saharan Africa, including both promiscuous and non-promiscuous varieties. By combining data from a core set of replicated on-farm trials with that from a large number of farmer-managed try-outs, we study the potential for inoculation to increase yields in both variety types and evaluate the magnitude and variability of response. Average yields were estimated to be 1343 and 1227 kg/ha with and without inoculation respectively. Inoculation response varied widely between trials and locations, with no clear spatial patterns at larger scales and without evidence that this variation could be explained by yield constraints or environmental conditions. On average, specific varieties had similar uninoculated yields, while responding more strongly to inoculation. Side-by side comparisons revealed that stronger responses were observed at sites where promiscuous varieties had superior uninoculated yields, suggesting the availability of compatible, effective bacteria as a yield limiting factor and as a determinant of the magnitude of inoculation response

Estimating maize genetic erosion in modernized smallholder agriculture

Replacement of crop landraces by modern varieties is thought to cause diversity loss. We studied genetic erosion in maize within a model system; modernized smallholder agriculture in southern Mexico. The local seed supply was described through interviews and in situ seed collection. In spite of the dominance of commercial seed, the informal seed system was found to persist. True landraces were rare and most informal seed was derived from modern varieties (creolized). Seed lots were characterized for agronomical traits and molecular markers. We avoided the problem of non-consistent nomenclature by taking individual seed lots as the basis for diversity inference. We defined diversity as the weighted average distance between seed lots. Diversity was calculated for subsets of the seed supply to assess the impact of replacing traditional landraces with any of these subsets. Results were different for molecular markers, ear- and vegetative/flowering traits. Nonetheless, creolized varieties showed low diversity for all traits. These varieties were distinct from traditional landraces and little differentiated from their ancestral stocks. Although adoption of creolized maize into the informal seed system has lowered diversity as compared to traditional landraces, genetic erosion was moderated by the distinct features offered by modern varieties

The theoretical potential for tailored fertilizer application. The case of maize in Sub-Saharan Africa

Adjusting crop nutrient rates to local differences in soil fertility and yield potential is considered a promising way of improving fertilizer use efficiency in smallholder agriculture. Despite its obvious appeal, the benefits of this approach have not been evaluated empirically. In the absence of appropriate data, the theoretical potential of nutrient tailoring needs to be evaluated, and suitable models exist to do this. Such analysis may provide a benchmark for expectations of success and would form a valuable starting point for reflection and debate on the limitations of current data, models, and assumptions. This study presents a theoretical ex-ante assessment of the short-term economic benefits of fertilizer adjustments to differences in nutrient and water constraints, under the assumption that these are the only determinants of nutrient response. Combining two calibrations of a mathematical production function with digital maps of predicted nutrient and water availability, economically optimum macro-nutrient rates and resulting maize yields were calculated across a theoretical representation of soils across Sub-Saharan Africa. Consistent economic benefits from fertilizer application were predicted, raising theoretical yields from 19 to as much as 61 or 88% of water limited levels depending on the model used. Under the magnitude of soil fertility variation considered here, matching nutrient rates to indigenous levels was predicted to result in marginal added benefits at best, particularly at higher, more profitable, investment levels. Strong spatial heterogeneity in water limitation translated into more pronounced effects of rate adjustments to water limited yields, raising average predicted profit by up to 127 USD/ha compared to a fixed-rate recommendation. This suggests that tailoring to different yield levels can offer non-trivial benefits if good estimates of yield potential are available. Adding realistic levels of uncertainty around soil fertility and use efficiency parameters reduced predicted gain from tailoring and suggested that acquiring sufficient experimental data for improved nutrient recommendations, both general and specific, may be challenging. The implications and limitations of these theoretical results and potential improvements in validation, predictions and outcomes are discussed

Genome-Wide Association Analysis of Adaptation Using Environmentally Predicted Traits

Current methods for studying the genetic basis of adaptation evaluate genetic associations with ecologically relevant traits or single environmental variables, under the implicit assumption that natural selection imposes correlations between phenotypes, environments and genotypes. In practice, observed trait and environmental data are manifestations of unknown selective forces and are only indirectly associated with adaptive genetic variation. In theory, improved estimation of these forces could enable more powerful detection of loci under selection. Here we present an approach in which we approximate adaptive variation by modeling phenotypes as a function of the environment and using the predicted trait in multivariate and univariate genome-wide association analysis (GWAS). Based on computer simulations and published flowering time data from the model plant Arabidopsis thaliana, we find that environmentally predicted traits lead to higher recovery of functional loci in multivariate GWAS and are more strongly correlated to allele frequencies at adaptive loci than individual environmental variables. Our results provide an example of the use of environmental data to obtain independent and meaningful information on adaptive genetic variation