Knowing spatial patterns of functional microbial guilds can increase our understanding of the relationships between microbial community ecology and ecosystem functions. Using geostatistical modeling to map spatial patterns, we explored the distribution of the community structure, size, and activity of one functional group in N cycling, the denitrifiers, in relation to 23 soil parameters over a 44-ha farm divided into one organic and one integrated crop production system. The denitrifiers were targeted by thenirSandnirKgenes that encode the two mutually exclusive types of nitrite reductases, the cd₁heme-type and copper reductases, respectively. The spatial pattern of the denitrification activity genes was reflected by the maps of the abundances ofnirgenes. For the community structure, only the maps of thenirScommunity were related to the activity. The activity was correlated with nitrate and dissolved organic nitrogen and carbon, whereas the gene pools for denitrification, in terms of size and composition, were influenced by the soil structure. For thenirScommunity, pH and soil nutrients were also important in shaping the community. The only unique parameter related to thenirKcommunity was the soil Cu content. However, the spatial pattern of thenirKdenitrifiers corresponded to the division of the farm into the two cropping systems. The different community patterns, together with the spatial distribution of thenirS/nirKabundance ratio, suggest habitat selection on thenirS- andnirK-type denitrifiers. Our findings constitute a first step in identifying niches for denitrifiers at scales relevant to land management.

Characterization of spatial patterns of functional microbial communities could facilitate the understanding of the relationships between the ecology of microbial communities, the biogeochemical processes they perform and the corresponding ecosystem functions. Because of the important role the ammonia-oxidizing bacteria (AOB) and archaea (AOA) have in nitrogen cycling and nitrate leaching, we explored the spatial distribution of their activity, abundance and community composition across a 44-ha large farm divided into an organic and an integrated farming system. The spatial patterns were mapped by geostatistical modeling and correlations to soil properties and ecosystem functioning in terms of nitrate leaching were determined. All measured community components for both AOB and AOA exhibited spatial patterns at the hectare scale. The patchy patterns of community structures did not reflect the farming systems, but the AOB community was weakly related to differences in soil pH and moisture, whereas the AOA community to differences in soil pH and clay content. Soil properties related differently to the size of the communities, with soil organic carbon and total nitrogen correlating positively to AOB abundance, while clay content and pH showed a negative correlation to AOA abundance. Contrasting spatial patterns were observed for the abundance distributions of the two groups indicating that the AOB and AOA may occupy different niches in agro-ecosystems. In addition, the two communities correlated differently to community and ecosystem functions. Our results suggest that the AOA, not the AOB, were contributing to nitrate leaching at the site by providing substrate for the nitrite oxidizers.

Opportunities exist for adoption of precision agriculture technologies in all parts of the world. The form of precision agriculture may vary from region to region depending on technologies available, knowledge levels and mindsets. The current review examined research articles in the English language on precision agriculture practices for increased productivity among smallholder farmers in Sub-Saharan Africa. A total of 7715 articles were retrieved and after screening 128 were reviewed. The results indicate that a number of precision agriculture technologies have been tested under SSA conditions and show promising results. The most promising precision agriculture technologies identified were the use of soil and plant sensors for nutrient and water management, as well as use of satellite imagery, GIS and crop-soil simulation models for site-specific management. These technologies have been shown to be crucial in attainment of appropriate management strategies in terms of efficiency and effectiveness of resource use in SSA. These technologies are important in supporting sustainable agricultural development. Most of these technologies are, however, at the experimental stage, with only South Africa having applied them mainly in large-scale commercial farms. It is concluded that increased precision in input and management practices among SSA smallholder farmers can significantly improve productivity even without extra use of inputs.

A protocol using real‐time polymerase chain reaction (PCR) for the direct detection and quantification of Plasmodiophora brassicae in soil samples was developed and used on naturally and artificially infested soil samples containing different concentrations of P. brassicae . Species‐specific primers and a TaqMan fluorogenic probe were designed to amplify a small region of P. brassicae ribosomal DNA. Total genomic DNA was extracted and purified from soil samples using commercial kits. The amount of pathogen DNA was quantified using a standard curve generated by including reactions containing different amounts of a plasmid carrying the P. brassicae target sequence. The PCR assay was optimized to give high amplification efficiency and three to four copies of the target DNA sequence were detected. Regression analysis showed that the standard curve was linear over at least six orders of magnitude ( R ² > 0·99) and that the amplification efficiency was >92%. The detection limit in soil samples corresponded to 500 resting spores g ⁻¹ soil. The intersample reproducibility was similar to, or higher than, that of assays for other pathogens quantified in soil samples. Bait plants were used to validate the real‐time PCR assay. The protocol developed was used to investigate the spatial distribution of P. brassicae DNA in different fields and a significant difference was found between in‐field sampling points. The reproducibility of soil sampling was evaluated and showed no significant differences for samples with low levels of inoculum, whereas at higher levels differences occurred. Indicator kriging was used for mapping the probability of detecting P. brassicae within a 2‐ha area of a field. A threshold level of 5 fg plasmid DNA g ⁻¹ soil, corresponding to approximately 3 × 10 ³ P. brassicae resting spores g ⁻¹ soil, is suggested for growing resistant cultivars. The results provide a robust and reliable technique for predicting the risk of disease development and for assessing the distribution of disease within fields.