Evaluation of Genetic Traits for Improving Productivity and Adaptation of Groundnut to Climate Change in India

Anticipated climate change will alter the temperature and rainfall characteristics of crop growing seasons. This will require genetic improvement of crops for adapting to future climates for higher yields. The CROPGRO model for groundnut was used to evaluate genetic traits of Virginia and Spanish types of groundnut for various climate scenarios of India. The analysis revealed that productivity of groundnut can be increased in current and future climates by adjusting the duration of various life‐cycle phases, especially the seed‐filling to physiological maturity (SD‐PM). Increased maximum leaf photosynthesis rate (AMAX), increased partitioning to reproductive organs (XFRT) and increased individual seed‐fill duration (SFDUR) all contributed to the increase in pod yield in all climates. More determinate pod set (shorter PODUR) was beneficial only in the water deficit environments. The positive effect of increasing specific leaf area (SLA) and leaf size (SIZLF) on pod yield was greater in environments more favourable for plant growth. Increasing reproductive tolerance to high temperature by 2 °C increased pod yield of groundnut in warmer environments, especially where the crop often suffers from drought. Increased adaptive partitioning to roots (ATOP) increased drought resistance of groundnut on high water‐holding capacity soils. Combination of traits had additive effects and pod yield increased substantially. These results indicate that the CROPGRO model can be used to assess the potential of individual or combination of plant traits for guiding breeding of improved groundnut varieties for current and future climates.     

The Regional Multi-Agent Simulator (RegMAS): An open-source spatially explicit model to assess the impact of agricultural policies

Regional Multi-Agent Simulator (RegMAS) is an open-source spatially explicit multi-agent model framework specifically designed for long-term simulations of the effects of policies on agricultural systems. Using iterated conventional optimisation problems as agents’ behavioural rules, it allows for a bidirectional integration between geophysical and social models where spatially distributed characteristics are taken into account in the programming problem of the optimising agents. With RegMAS it is possible to simulate the local specific response to a given policy (or scenario), where policies, together with macro- and regional characteristics, are read into the program in specially formatted spreadsheets and standard GIS files.The paper presents the model logic and structure and describes its functioning by applying it to a case-study, where RegMAS results are compared with conventional agent-based modelling to demonstrate the advantages of spatial explicitness. The simulation refers to the impact of the recent “Health Check” of the CAP on farm structures, income and land use in a hilly area of a central Italian region (Marche).     

Soil Sealing: Quantifying Impacts on Soil Functions by a Geospatial Decision Support System

Soil sealing is considered among the most dangerous of land degradation processes on global, European and national scales. Despite important policy documents aiming to mitigate this soil threat, it currently shows no signs of abating, and current efforts often do not result in appropriate implementation of soil sealing mitigation in spatial planning, which represents the subject area governing soil sealing. In this paper, we show a spatial decision support system – based on a Geospatial Cyberinfrastructure – with the aim of applying it as an operational instrument aiming towards soil sealing mitigation. The system has the ambition to impact on those who take decision over soil sealing; typically, these are not agriculture experts but rather spatial planners. This tool, focusing on mitigating such crucial land degradation, allows the users – via the Web – to produce ‘what‐if’ land planning scenarios thanks to the ‘on‐the‐fly’ modelling engines. Therefore, integrated geospatial quantitative data and procedures may be directly and freely used by planners. The tool has been applied to and tested in an area in the South of Italy. Results from two applications are reported: one addressing municipal planning and the other on a more detailed spatial scale. Furthermore, results include quantification of rural fragmentation, loss of soil ecosystem services and an estimate of soil sealing evolution over time. The tool was developed with the help of end users and indirectly explores a change of paradigm where soil science and landscape/urban planning work together to provide operational instruments that may be adopted by local communities in addressing soil sealing issues with a proactive approach. Copyright © 2017 John Wiley & Sons, Ltd.     

Soil surface infiltration capacity classification based on the bi-directional reflectance distribution function sampled by aerial photographs. The case of vineyards in a Mediterranean area

Spatially distributed hydrological modelling is required to understand and predict erosion, flooding and pollution risks that affect the vine cultivated Mediterranean environment. Previous field studies have demonstrated the dominant influence of soil surface features on overland flow and they therefore constitute an essential input to the hydrological model. In this paper we propose a remote sensing based method to map vineyard soil surface features with a spatial and temporal resolution appropriate for integration into the model. Our goal was to classify each soil surface portion in accordance with a pre-established, field measured infiltration capacity based typology. The radiometric characteristics of the classes of this typology were measured in the field and their Bi-directional Reflectance Distribution Function (BRDF) was modelled. Vineyard sunlit soil surface pixels were automatically extracted from high spatial resolution scanned aerial colour photographs Wassenaar et al., 2001 and Wassenaar et al., 2002. These pixels are radiometrically classified by comparison of their reflectance with BRDF-based reflectance predictions of each soil surface type for the specific illumination and viewing geometry of the pixel.The results show that most hydrological soil surface classes have distinct bi-directional radiometric properties. For one given geometric configuration however, the predicted reflectance ranges of some classes can considerably overlap (tilled soils and stone layers for example), while others can always unambiguously be identified (bare soil crusts, surfaces covered for more than 50% by weed or litter).We conclude that our fuzzy classification approach and the simple radiometric information used, allow us to identify the majority of the hydrological surface types. The method can easily be transposed in time and space. Its performance quite strongly depends on the radiometric and geometric accuracy of the input data. Significant improvements in soil surface classification precision are expected from considering spatial context information and monitoring the soil surface evolution.     

海岸风沙运动观测与模拟的研究与进展

海岸风沙运动的观测与模拟研究是海岸风沙地貌等海岸风沙问题研究的基础与核心。本文依据 1 980— 1 999年间的国内外研究文献 ,较为详尽地回顾近 2 0年来国内外尤其是国外的海岸风沙运动观测与模拟研究 ,综述了包括在海岸沙丘表面气流运动与风沙流结构观测、海岸风沙运动速率观测以及海岸风沙运动方程的验证、修正与建立等方面的主要进展、成果与问题     

Effect of weather conditions on local spread and infection by pea bacterial blight (Pseudomonas syringae pv. pisi)

The effect of weather conditions on simultaneous local (plant to plant) spread and infection of peas (Pisum sativum) with bacterial blight (Pseudomonas syringae pv. pisi) was investigated by exposing susceptible bait plants for 24 h periods in infected field plots. Following exposure, bait plants were maintained in a glasshouse. Disease symptoms were recorded on 55 out of a total of 105 days on which plants were exposed. Nearly all of these infection events (53) were associated with the occurrence of rain. A series of Generalised Linear Models was fitted to the data to examine the relationships of the mean number of lesions (m) or the proportion of bait plants infected (p) to various weather variables and disease levels in the plots. Rainfall rate and wind run were the most important explanatory variables for the mean number of lesions followed by maximum temperature, rainfall duration, rainfall in the previous week and disease incidence in the surrounding crop. However, rainfall duration and disease incidence were the most important for the proportion of bait plants infected, followed by wind run. A four variable model relating the mean number of lesions to the rainfall rate, wind run, maximum temperature and either rainfall the previous week or disease incidence in the surrounding crop was considered to be the most useful for use in simulation studies.     

Characteristics of immunofluorescence microscopy and of dilution-plating to detect Pseudomonas syringae pv. phaseolicola in bean seed lots and for risk assessment of field incidence of halo blight

Routine laboratory testing of 710 bean seed lots from various origins forPseudomonas syringae pv. phaseolicola (Psp) with immunofluorescence microscopy (IF) showed that 27.5% of the seed lots (five subsamples of 1000 seeds tested per sample) contained two or more IF-positive cells in a total of 500 microscope fields (magnification 500×). Simultaneously performed dilution-platings of IF-positive subsamples on King's medium B confirmed presence of Psp for one-third of these IF-positive seed lots. The grey area of disagreement between both laboratory tests was studied by comparison of test data and by field trials.The number of IF-positive cells per subsample was positively correlated with isolation and identification of Psp (R=0.85). The detection level of IF was ca. 10² Psp cells per ml of undiluted subsample extract. The detection level of Psp by isolation on King's medium B was variable, being inversely related with the saprophyte to Psp ratio. The high sensitivity of IF was in part due to high percentages of dormant or dead IF-positive cells in the sample extract. Field trials over two years with 10 000 seeds per seed lot, showed disease incidence for 9 of the 22 seed lots. Of ten IF-positive lots with five positive subsamples per sample, nine were positive in the field test plot (the negative lot gave primary infection spots of Psp when used for commercial growing). By isolation, seven of these ten IF-positive lots were positive. Of the five IF-positive lots with two or less positive subsamples, isolation and field trial were both negative. Based on data on seed transmission from literature, field incidence was unlikely for these five samples in a 10 000 seeds field trial. All seven IF-negative lots were negative in the field trial. The value of IF and isolation for indexing bean seed lots for Psp is discussed.This study was carried out at the Centre for Plant Breeding and Reproduction Research (CPRO-DLO), Binnenhaven 1, P.O. Box 16, 6700 AA Wageningen the Netherlands, to which address correspondence should be addressed.     

Modelling the effects of sub-lethal doses of herbicide and nitrogen fertilizer on crop–weed competition

The effects of sub‐lethal dose of herbicide and nitrogen fertilizer on crop–weed competition were investigated. Biomass increases of winter wheat and a model weed, Brassica napus, at no‐herbicide treatment with increasing nitrogen were successfully described by the inverse quadratic model and the linear model respectively. Increases in weed competitivity (β₀) of the rectangular hyperbola and parameter B in the dose–response curve for weed biomass, with increasing nitrogen were also successfully described by the exponential model. New models were developed by incorporating inverse quadratic and exponential models into the combined rectangular hyperbola with the standard dose–response curve for winter wheat biomass yield and the combined standard dose—response model with the rectangular hyperbola for weed biomass, to describe the complex effects of herbicide and nitrogen on crop–weed competition. The models developed were used to predict crop yield and weed biomass and to estimate the herbicide doses required to restrict crop yield loss caused by weeds and weed biomass production to an acceptable level at a range of nitrogen levels. The model for crop yield was further modified to estimate the herbicide dose and nitrogen level to achieve a target crop biomass yield. For the target crop biomass yield of 1200 g m^?2 with an infestation of 100 B. napus plants m^?2, the model recommended various options for nitrogen and herbicide combinations: 140 and 2.9, 180 and 0.9 and 360 kg ha^?1 and 1.7 g a.i. ha^?1 of nitrogen and metsulfuron‐methyl respectively.     

Prediction of the spatial distribution and relative abundance of ground-dwelling mammals using remote sensing imagery and simulation models

We present an approach that allows current, retrospective and future relative abundances of mammal species to be predicted across landscapes. A spatial generalized regression model of species relative abundance based on habitat quality and time since disturbance was combined with coverages of the spatial distribution of habitat quality derived from a simulation model which predicts the historical and future spatial arrangement of forest habitat. The strength of this approach is that the input habitat data can be derived as part of a standard forest inventory mapping program with the addition of high spatial resolution remote sensing imagery. Furthermore, it operates at the scale used for wildlife management in Australia, which makes it widely applicable. To demonstrate the approach we use data collected over 20 years on the long-nosed potoroo (Potorous tridactylus) and the large wallabies (red-necked wallaby, Macropus rufogriseus, and swamp wallaby, Wallabia bicolor) and their habitats following wildfire. Results indicate the relative abundance of the potoroo has increased, from initially sparse numbers of less than 0.5 % of plot-night occurrences to close to 3% approximately twenty years after a major fire event. The large wallabies by contrast decreased in relative abundance from about 20% since the major fire event. Presently the relative abundance of large wallabies was modelled at 2% of plot-nights with tracks which was very low. Predictions of future relative abundance without additional disturbance were low, with the region likely to be unsuitable for the species in the next 5 years. These models offer tools for investigating the current and historical abundances of key species which can provide data to forest managers for wildlife management thereby translating current scientific understanding into tools suitable for every-day use by forest managers. This revised version was published online in July 2006 with corrections to the Cover Date.     

Simulation of soil water dynamics under subsurface drip irrigation from line sources

A mathematical model which describes water flow under subsurface drip lines taking into account root water uptake, evaporation of soil water from the soil surface and hysteresis in the soil water characteristic curve θ(H) is presented. The model performance in simulating soil water dynamics was evaluated by comparing the predicted soil water content values with both those of Hydrus 2D model and those of an analytical solution for a buried single strip source. Soil water distribution patterns for three soils (loamy sand, silt, silty clay loam) and two discharge rates (2 and 4 l m⁻¹ h⁻¹) at four different times are presented. The numerical results showed that the soil wetting pattern mainly depends on soil hydraulic properties; that at a time equal to irrigation duration decreasing the discharge rate of the line sources but maintaining the applied irrigation depth, the vertical and horizontal components of the wetting front were increased; that at a time equal to the total simulation time the discharge rate has no effect on the actual transpiration and actual soil evaporation and a small effect on deep percolation. Also the numerical results showed that when the soil evaporation is neglected the soil water is more easily taken up by the plant roots.