Ag Decision Maker, April 2016, Vol. 20, no. 6

A Business Newsletter for Agriculture

Biomass equations for European trees : addendum

Abstract

What matters for predicting spatial distributions of trees: techniques, data, or species' characteristics?

Data characteristics and species traits are expected to influence the accuracy with which species' distributions can be modeled and predicted. We compare 10 modeling techniques in terms of predictive power and sensitivity to location error, change in map resolution, and sample size, and assess whether some species traits can explain variation in model performance. We focused on 30 native tree species in Switzerland and used presence-only data to model current distribution, which we evaluated against independent presence-absence data. While there are important differences between the predictive performance of modeling methods, the variance in model performance is greater among species than among techniques. Within the range of data perturbations in this study, some extrinsic parameters of data affect model performance more than others: location error and sample size reduced performance of many techniques, whereas grain had little effect on most techniques. No technique can rescue species that are difficult to predict. The predictive power of species-distribution models can partly be predicted from a series of species characteristics and traits based on growth rate, elevational distribution range, and maximum elevation. Slow-growing species or species with narrow and specialized niches tend to be better modeled. The Swiss presence-only tree data produce models that are reliable enough to be useful in planning and management applications.

Ag Decision Maker, May 2016, Vol. 20, no. 7

A Business Newsletter for Agriculture

Optimization strategies for fast detection of positive selection on phylogenetic trees.

MOTIVATION: The detection of positive selection is widely used to study gene and genome evolution, but its application remains limited by the high computational cost of existing implementations. We present a series of computational optimizations for more efficient estimation of the likelihood function on large-scale phylogenetic problems. We illustrate our approach using the branch-site model of codon evolution. RESULTS: We introduce novel optimization techniques that substantially outperform both CodeML from the PAML package and our previously optimized sequential version SlimCodeML. These techniques can also be applied to other likelihood-based phylogeny software. Our implementation scales well for large numbers of codons and/or species. It can therefore analyse substantially larger datasets than CodeML. We evaluated FastCodeML on different platforms and measured average sequential speedups of FastCodeML (single-threaded) versus CodeML of up to 5.8, average speedups of FastCodeML (multi-threaded) versus CodeML on a single node (shared memory) of up to 36.9 for 12 CPU cores, and average speedups of the distributed FastCodeML versus CodeML of up to 170.9 on eight nodes (96 CPU cores in total).Availability and implementation: ftp://ftp.vital-it.ch/tools/FastCodeML/. CONTACT: selectome@unil.ch or nicolas.salamin@unil.ch.

'Tommy Atkins' mango trees subjected to high density planting in subhumid tropical climate in northeastern Brazil

The objective of this work was to evaluate the effects of high density planting on 'Tommy Atkins' mango trees cultivated in subhumid warm tropical climate in northeastern Brazil. Treatments consisted of five spacial arrangements of plants (8x5 m, 7x4 m, 6x3 m, 5x2 m and 4x2 m), which resulted in the following plant densities: 250 (control), 357, 555, 1,000 and 1,250 plants per hectare. Plant vegetative and reproductive variables, besides fruit quality parameters, were evaluated at seven and eight years after transplantation to the field. In general, high density planting caused reduction in vegetative and reproductive variables of individual mango trees, but had little influence on fruit quality. Above 555 plants per hectare, a significant decrease was observed in mango tree growth. Furthermore, there were decreases in the percentage of flowering, fruit yield per plant and per area. However, planting density up to 357 plants per hectare, in spite of decreasing plant growth and fruit yield per tree, increases fruit yield per area in 30% in comparison to the control.

The timing of exploratory decision-making revealed by single-trial topographic EEGanalyses.

Decision-making in an uncertain environment is driven by two major needs: exploring the environment to gather information or exploiting acquired knowledge to maximize reward. The neural processes underlying exploratory decision-making have been mainly studied by means of functional magnetic resonance imaging, overlooking any information about the time when decisions are made. Here, we carried out an electroencephalography (EEG) experiment, in order to detect the time when the brain generators responsible for these decisions have been sufficiently activated to lead to the next decision. Our analyses, based on a classification scheme, extract time-unlocked voltage topographies during reward presentation and use them to predict the type of decisions made on the subsequent trial. Classification accuracy, measured as the area under the Receiver Operator's Characteristic curve was on average 0.65 across 7 subjects. Classification accuracy was above chance levels already after 516 ms on average, across subjects. We speculate that decisions were already made before this critical period, as confirmed by a positive correlation with reaction times across subjects. On an individual subject basis, distributed source estimations were performed on the extracted topographies to statistically evaluate the neural correlates of decision-making. For trials leading to exploration, there was significantly higher activity in dorsolateral prefrontal cortex and the right supramarginal gyrus; areas responsible for modulating behavior under risk and deduction. No area was more active during exploitation. We show for the first time the temporal evolution of differential patterns of brain activation in an exploratory decision-making task on a single-trial basis.

A decision support system for selective cleaning

[Abstract]

Automatic Decision-Oriented Mapping of Pollution Data

The paper deals with the development and application of the methodology for automatic mapping of pollution/contamination data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve this problem. The automatic tuning of isotropic and an anisotropic GRNN model using cross-validation procedure is presented. Results are compared with k-nearest-neighbours interpolation algorithm using independent validation data set. Quality of mapping is controlled by the analysis of raw data and the residuals using variography. Maps of probabilities of exceeding a given decision level and ?thick? isoline visualization of the uncertainties are presented as examples of decision-oriented mapping. Real case study is based on mapping of radioactively contaminated territories.

Bud dormancy in apple trees after thermal fluctuations

The objective of this work was to evaluate the effect of heat waves on the evolution of bud dormancy, in apple trees with contrasting chilling requirements. Twigs of 'Castel Gala' and 'Royal Gala' were collected in orchards in Papanduva, state of Santa Catarina, Brazil, and were exposed to constant (3°C) or alternating (3 and 15°C for 12/12 hours) temperature, combined with zero, one or two days a week at 25°C. Two additional treatments were evaluated: constant temperature (3°C), with a heat wave of seven days at 25°C, in the beginning or in the middle of the experimental period. Periodically, part of the twigs was transferred to 25°C for daily budburst evaluation of apical and lateral buds. Endodormancy (dormancy induced by cold) was overcome with less than 330 chilling hours (CH) of constant cold in 'Castel Gala' and less than 618 CH in 'Royal Gala'. A daily 15°C-temperature cycle did not affect the endodormancy process. Heat waves during endodormancy resulted in an increased CH to achieve bud requirements. The negative effect of high temperature depended on the lasting of this condition. Chilling was partly cancelled during dormancy when the heat wave lasted 36 continuous hours or more. Therefore, budburst prediction models need adjustments, mainly for regions with mild and irregular winters, such as those of Southern Brazil.

Microclimate and development of 'Conilon' coffee intercropped with rubber trees

The objective of this work was to evaluate the influence of intercropping 'Conilon' coffee (Coffea canephora) with rubber trees on coffee tree microclimate, nutrition, growth, and yield. Rubber trees were planted in two double rows 33 m apart, with 4x2.3 m spacing between plants. Treatments consisted of the distances from the coffee plants to the rubber trees: 3, 6, 9, 12, and 15 m. Measurements of atmospheric variables (temperature, irradiance, and relative humidity), leaf nutrient concentration, internode length of plagiotropic and orthotropic branches, individual leaf area, chlorophyll content, and yield were performed. Intercropping promotes changes in the microclimatic conditions of coffee plants close to rubber trees, with reduction of temperature and irradiance level and increase in air relative humidity. The proximity of the coffee tree to the rubber trees promotes the elongation of the plagiotropic and orthotropic branches and increases the individual leaf area; however, it does not affect leaf concentrations of N, K, Mg, Fe, Zn, and B in 'Conilon' coffee and does not have a negative impact on yield.

Multiple sclerosis decreases explicit counterfactual processing and risk taking in decision making.

INTRODUCTION: Deficits in decision making (DM) are commonly associated with prefrontal cortical damage, but may occur with multiple sclerosis (MS). There are no data concerning the impact of MS on tasks evaluating DM under explicit risk, where different emotional and cognitive components can be distinguished. METHODS: We assessed 72 relapsing-remitting MS (RRMS) patients with mild to moderate disease and 38 healthy controls in two DM tasks involving risk with explicit rules: (1) The Wheel of Fortune (WOF), which probes the anticipated affects of decisions outcomes on future choices; and (2) The Cambridge Gamble Task (CGT) which measures risk taking. Participants also underwent a neuropsychological and emotional assessment, and skin conductance responses (SCRs) were recorded. RESULTS: In the WOF, RRMS patients showed deficits in integrating positive counterfactual information (p<0.005) and greater risk aversion (p<0.001). They reported less negative affect than controls (disappointment: p = 0.007; regret: p = 0.01), although their implicit emotional reactions as measured by post-choice SCRs did not differ. In the CGT, RRMS patients differed from controls in quality of DM (p = 0.01) and deliberation time (p = 0.0002), the latter difference being correlated with attention scores. Such changes did not result in overall decreases in performance (total gains). CONCLUSIONS: The quality of DM under risk was modified by MS in both tasks. The reduction in the expression of disappointment coexisted with an increased risk aversion in the WOF and alexithymia features. These concomitant emotional alterations may have implications for better understanding the components of explicit DM and for the clinical support of MS patients.