Trait physiology and crop modelling to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. We explore whether a crop growth and development modelling framework can link phenotype complexity to underlying genetic systems in a way that strengthens molecular breeding strategies. We use gene-to-phenotype simulation studies on sorghum to consider the value to marker-assisted selection of intrinsically stable QTLs that might be generated by physiological dissection of complex traits. The consequences on grain yield of genetic variation in four key adaptive traits – phenology, osmotic adjustment, transpiration efficiency, and staygreen – were simulated for a diverse set of environments by placing the known extent of genetic variation in the context of the physiological determinants framework of a crop growth and development model. It was assumed that the three to five genes associated with each trait, had two alleles per locus acting in an additive manner. The effects on average simulated yield, generated by differing combinations of positive alleles for the traits incorporated, varied with environment type. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages with gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies. We simulated a marker-assisted selection (MAS) breeding strategy based on the analyses of gene effects. When marker scores were allocated based on the contribution of gene effects to yield in a single environment, there was a wide divergence in rate of yield gain over all environments with breeding cycle depending on the environment chosen for the QTL analysis. It was suggested that knowledge resulting from trait physiology and modelling would overcome this dependency by identifying stable QTLs. The improved predictive power would increase the utility of the QTLs in MAS. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate QTLs.

CLIMATE CHANGE ADAPTATION IN VULNERABLE CROP AND LIVESTOCK PRODUCTION SYSTEMS IN MGETA, TANZANIA

Increased occurrence of drought and dry spells during the growing season have resulted in increased interest in protection of tropical water catchment areas. In Mgeta, a water catchment area in the Uluguru Mountains in Tanzania, water used for vegetable and fruit production is provided through canals from the Uluguru South Forest Reserve. The clearing of forest land for cultivation in the steep slopes in the area is causing severe land degradation, which is threatening the water catchment area, livelihoods, and food security of the local communities, as well as the major population centers in the lowlands. In this paper, the economic performance of a traditional cropping-livestock system with East African (EA)-goats and pigs and extensive vegetable production is compared with a more sustainable and environmentally friendly crop-dairy goat production system. A linear programming (LP) crop-livestock model, maximizing farm income considering the environmental constraints in the area was applied for studying the economic performance of dairy goats in the production system. The model was worked out for the rainy and dry seasons and the analysis was conducted for a basic scenario representing the current situation, based on the variability in the 30 years period from 1982-2012, and in a scenario of both lower crop yields and increased crop variability due to climate change. Data obtained from a sample of 60 farmers that were interviewed using a questionnaire was used to develop and parameterize the model. The study found that in the steep slopes of the area, a crop-dairy goat system with extensive use of grass and multipurpose trees (MPTs) would do better than the traditional vegetable gardening with the EA goat production system. The crop-dairy goat system was superior both in the basic and in a climate change scenario since the yield variation of the grass and MPTs system was less affected compared to vegetable crops due to more tree cover and the use of perennial grasses. However, the goat milk production in the area was constrained by inadequate feeding and lack of an appropriate breeding program. Hence, farmers should enhance goat milk production by supplementing with more concentrate feed and by implementing goat-breeding principles. Moreover, policy measures to promote such a development are briefly discussed.

Nutrient content in aboveground biomass of Brazilian peanut cultivars in conservation tillage on sugarcane straw and pasture area.

Fields studies were conducted in 2004/2005 in order to evaluate the effects of tillage on nutrient content in aboveground biomass of two peanut cultivars, cultivated in rotation after mechanical harvested sugarcane and pastures. These trials were carried out in two types of soils; Oxisol and Ultisol, respectively in Ribeir?ao Preto and Mirassol, S?ao Paulo State, Brazil. The experimental design was split-plot with four replications. Tillage treatments (conventional, minimum and no-tillage) were main plots while sub-plots were peanut genotypes IAC-Tatu ST (Valencia market-type, erect growth habit, red seed coat, maturity range around 100 days after planting) and IAC-Caiap´o (Runner market-type, prostate growth habit, pink testa, maturity range more than 135 days). From 15 to 90 days after emergence, samples of leaves and stems were harvested, dried, weighted and ground to determine macro and micronutrient concentration. At 75 days after sowing, the cultivar IAC-Caiap´o showed higher contents of N, P, K, Cu, and Zn while IAC-Tatu presented higher concentrations of Ca, Mg, and S. Zn content was higher in conservation tillage than in conventional, mainly in Oxisoil for both of cultivars.

The distribution and spread of citrus canker in Emerald, Australia

Citrus canker is a disease of citrus and closely related species, caused by the bacterium Xanthomonas citri subsp. citri. This disease, previously exotic to Australia, was detected on a single farm [infested premise-1, (IP1). IP is the terminology used in official biosecurity protocols to describe a locality at which an exotic plant pest has been confirmed or is presumed to exist. IP are numbered sequentially as they are detected] in Emerald, Queensland in July 2004. During the following 10 months the disease was subsequently detected on two other farms (IP2 and IP3) within the same area and studies indicated the disease first occurred on IP1 and spread to IP2 and IP3. The oldest, naturally infected plant tissue observed on any of these farms indicated the disease was present on IP1 for several months before detection and established on IP2 and IP3 during the second quarter (i.e. autumn) 2004. Transect studies on some IP1 blocks showed disease incidences ranged between 52 and 100% (trees infected). This contrasted to very low disease incidence, less than 4% of trees within a block, on IP2 and IP3. The mechanisms proposed for disease spread within blocks include weather-assisted dispersal of the bacterium (e.g. wind-driven rain) and movement of contaminated farm equipment, in particular by pivot irrigator towers via mechanical damage in combination with abundant water. Spread between blocks on IP2 was attributed to movement of contaminated farm equipment and/or people. Epidemiology results suggest: (i) successive surveillance rounds increase the likelihood of disease detection; (ii) surveillance sensitivity is affected by tree size; and (iii) individual destruction zones (for the purpose of eradication) could be determined using disease incidence and severity data rather than a predefined set area.

Vectors and alternative hosts of tobacco yellow dwarf virus in southeastern Australia

Factors that determine the epidemiology of Tobacco yellow dwarf virus (TbYDV), including alternative host plants and insect vector(s), were assessed over three consecutive growing seasons at four field sites in Northeastern Victoria in commercial tobacco growing properties. In addition, these factors were assessed for one growing season at three bean growing properties. Overall, 23 leafhopper species were identified at the 7 sites, with Orosius orientalis as the predominant leafhopper. Of the leafhoppers collected, only O. orientalis and Anzygina zealandica tested positive for TbYDV by polymerase chain reaction (PCR). The population dynamics of O. orientalis was assessed using sweep net sampling over three growing seasons and a trimodal distribution was observed. Despite large numbers of O. orientalis occurring early in the growing season (September–October), TbYDV was only detected in these leafhoppers between late November and end of January. The peaks in the detection of TbYDV in O. orientalis correlated with the observation of disease symptoms in tobacco and bean and were associated with warmer temperatures and lower rainfall. Spatial and temporal distribution of vegetation at selected sites was determined using quadrat sampling. Of the 40 plant species identified, TbYDV was detected only in four dicotyledonous species, Amaranthus retroflexus, Phaseolus vulgaris, Nicotiana tabacum and Raphanus raphanistrum. The proportion of host and non-host availability for leafhoppers was associated with climatic conditions.

Accumulation of recombinant cellobiohydrolase and endoglucanase in the leaves of mature transgenic sugar cane

A major strategic goal in making ethanol from lignocellulosic biomass a cost-competitive liquid transport fuel is to reduce the cost of production of cellulolytic enzymes that hydrolyse lignocellulosic substrates to fermentable sugars. Current production systems for these enzymes, namely microbes, are not economic. One way to substantially reduce production costs is to express cellulolytic enzymes in plants at levels that are high enough to hydrolyse lignocellulosic biomass. Sugar cane fibre (bagasse) is the most promising lignocellulosic feedstock for conversion to ethanol in the tropics and subtropics. Cellulolytic enzyme production in sugar cane will have a substantial impact on the economics of lignocellulosic ethanol production from bagasse. We therefore generated transgenic sugar cane accumulating three cellulolytic enzymes, fungal cellobiohydrolase I (CBH I), CBH II and bacterial endoglucanase (EG), in leaves using the maize PepC promoter as an alternative to maize Ubi1 for controlling transgene expression. Different subcellular targeting signals were shown to have a substantial impact on the accumulation of these enzymes; the CBHs and EG accumulated to higher levels when fused to a vacuolar-sorting determinant than to an endoplasmic reticulum-retention signal, while EG was produced in the largest amounts when fused to a chloroplast-targeting signal. These results are the first demonstration of the expression and accumulation of recombinant CBH I, CBH II and EG in sugar cane and represent a significant first step towards the optimization of cellulolytic enzyme expression in sugar cane for the economic production of lignocellulosic ethanol.

Improving accuracy and intelligibility of decisions

Intelligible and accurate risk-based decision-making requires a complex balance of information from different sources, appropriate statistical analysis of this information and consequent intelligent inference and decisions made on the basis of these analyses. Importantly, this requires an explicit acknowledgement of uncertainty in the inputs and outputs of the statistical model. The aim of this paper is to progress a discussion of these issues in the context of several motivating problems related to the wider scope of agricultural production. These problems include biosecurity surveillance design, pest incursion, environmental monitoring and import risk assessment. The information to be integrated includes observational and experimental data, remotely sensed data and expert information. We describe our efforts in addressing these problems using Bayesian models and Bayesian networks. These approaches provide a coherent and transparent framework for modelling complex systems, combining the different information sources, and allowing for uncertainty in inputs and outputs. While the theory underlying Bayesian modelling has a long and well established history, its application is only now becoming more possible for complex problems, due to increased availability of methodological and computational tools. Of course, there are still hurdles and constraints, which we also address through sharing our endeavours and experiences.

Comparison of scoring systems for invasive pests using ROC analysis and Monte Carlo simulation

Different international plant protection organisations advocate different schemes for conducting pest risk assessments. Most of these schemes use structured questionnaire in which experts are asked to score several items using an ordinal scale. The scores are then combined using a range of procedures, such as simple arithmetic mean, weighted averages, multiplication of scores, and cumulative sums. The most useful schemes will correctly identify harmful pests and identify ones that are not. As the quality of a pest risk assessment can depend on the characteristics of the scoring system used by the risk assessors (i.e., on the number of points of the scale and on the method used for combining the component scores), it is important to assess and compare the performance of different scoring systems. In this article, we proposed a new method for assessing scoring systems. Its principle is to simulate virtual data using a stochastic model and, then, to estimate sensitivity and specificity values from these data for different scoring systems. The interest of our approach was illustrated in a case study where several scoring systems were compared. Data for this analysis were generated using a probabilistic model describing the pest introduction process. The generated data were then used to simulate the outcome of scoring systems and to assess the accuracy of the decisions about positive and negative introduction. The results showed that ordinal scales with at most 5 or 6 points were sufficient and that the multiplication-based scoring systems performed better than their sum-based counterparts. The proposed method could be used in the future to assess a great diversity of scoring systems.

How can soil monitoring networks be used to improve predictions of organic carbon pool dynamics and CO2 fluxes in agricultural soils?

Abstract As regional and continental carbon balances of terrestrial ecosystems become available, it becomes clear that the soils are the largest source of uncertainty. Repeated inventories of soil organic carbon (SOC) organized in soil monitoring networks (SMN) are being implemented in a number of countries. This paper reviews the concepts and design of SMNs in ten countries, and discusses the contribution of such networks to reducing the uncertainty of soil carbon balances. Some SMNs are designed to estimate country-specific land use or management effects on SOC stocks, while others collect soil carbon and ancillary data to provide a nationally consistent assessment of soil carbon condition across the major land-use/soil type combinations. The former use a single sampling campaign of paired sites, while for the latter both systematic (usually grid based) and stratified repeated sampling campaigns (5–10 years interval) are used with densities of one site per 10–1,040 km². For paired sites, multiple samples at each site are taken in order to allow statistical analysis, while for the single sites, composite samples are taken. In both cases, fixed depth increments together with samples for bulk density and stone content are recommended. Samples should be archived to allow for re-measurement purposes using updated techniques. Information on land management, and where possible, land use history should be systematically recorded for each site. A case study of the agricultural frontier in Brazil is presented in which land use effect factors are calculated in order to quantify the CO2 fluxes from national land use/management conversion matrices. Process-based SOC models can be run for the individual points of the SMN, provided detailed land management records are available. These studies are still rare, as most SMNs have been implemented recently or are in progress. Examples from the USA and Belgium show that uncertainties in SOC change range from 1.6–6.5 Mg C ha−1 for the prediction of SOC stock changes on individual sites to 11.72 Mg C ha−1 or 34% of the median SOC change for soil/land use/climate units. For national SOC monitoring, stratified sampling sites appears to be the most straightforward attribution of SOC values to units with similar soil/land use/climate conditions (i.e. a spatially implicit upscaling approach). Keywords Soil monitoring networks - Soil organic carbon - Modeling - Sampling design