Neural networks in climate spatialization and their application in the agricultural zoning of climate risk for sunflower in different sowing dates

ABSTRACT

Sunflower is a species that is sensitive to local climate conditions. However, studies that use artificial neural networks (ANNs) to evaluate this influence and create tools such as agricultural zoning of climate risk (ZARC) have not been conducted for this species. Due to the importance of sunflower as a human food source and for biodiesel production, and also the necessity of conducting research to evaluate the suitability of this oleaginous species under different climatic conditions. Thus, we seek to construct a ZARC for sunflower in Brazil simulating sowing on different dates and using meteorological elements spatialized by ANNs. Climate data were used: air temperature (T), rainfall (P), relative air humidity (UR), solar radiation (MJ_m^?2_d^?1) and wind velocity (U²). Climatic regions considered suitable for the cultivation of sunflower had average annual values for T between 20 and 28°C, P between 500 and 1.500 mm per cycle, and soil water deficit (DEF) below 140 mm per cycle. A neural network is an efficient tool that can be used in spatialization of climate variables quickly and accurately. Sunflower sowing in the spring and summer are the ones that provide the largest suitable areas in southeastern Brazil, with 58.13 and 64.36% of suitable areas, respectively.     

Applying a Lagrangian dispersion analysis to infer carbon dioxide and latent heat fluxes in a corn canopy

Warland and Thurtell (2000) proposed an analytical dispersion Lagrangian analysis (hereafter WT analysis) to relate the mean scalar concentration field to source profiles inside the canopy. The first objective of this study was to evaluate the performance of the WT analysis with existing turbulence statistics parameterizations in a corn canopy, by comparing its inferred net ecosystem CO₂ exchange (NEE) and latent heat flux (λE) with eddy covariance measurements. The second objective was to assess the performance of the WT analysis to infer the soil CO₂ flux. Four parameterizations of turbulence statistics were used to estimate Lagrangian time scale (T_L) and standard deviation of vertical wind velocity (σ_w) profiles. The estimated T_L and σ_w profiles were then corrected for atmospheric stability conditions. The field experiment was carried out in a corn field from August to October 2007 and 2008. Profiles of water vapour and CO₂ mixing ratios were measured using a multiport sampling system connected to an infrared gas analyzer. Wind velocity within and above the canopy and eddy covariance measurements over the canopy were taken. The soil respiration, estimated using the WT analysis, was compared to estimates obtained by an empirical model. WT analysis fluxes showed good correlation (R² = 0.77-0.88) with NEE and λE obtained by the eddy covariance technique, but overestimated net fluxes, especially when corrections for atmospheric stability were applied. The optimization of T_L and σ_w profiles using in-canopy turbulence measurements improved the agreement between measured and modeled NEE and λE. Inferred soil CO₂ fluxes were underestimated and were poorly correlated (R² = 0.02-0.01) with estimates obtained using an empirical model based on soil temperature. This poor performance in estimating the soil respiration is likely caused by the decoupling between inside and above canopy flows.     

Heterochromatin type, amount and distribution in wild species of chili peppers (Capsicum, Solanaceae)

Triple staining with the fluorochromes chromomycin A3, distamycin A and 4′-6-diamidino-2-phenylindole (CMA/DA/DAPI) was applied to somatic metaphases and interphase nuclei of 11 taxa of wild chili peppers (Capsicum), with 2n = 2x = 24 (C. annuum var. glabriusculum, C. cardenasii, C. chacoense, C. flexuosum, C. galapagoense, C. eximium, C. praetermissum and C. tovarii) and 2n = 2x = 26 (C. recurvatum, C. rhomboideum and C. villosum) to analyse heterochromatin type, amount and distribution in wild members of this genus. Heterochromatic banding patterns allowed the identification of all the taxa examined and contributed to their taxonomic grouping. GC-rich heterochromatin (CMA+/DAPI?) was typical in all taxa; only C. praetermissum possessed also AT-rich (CMA?/DAPI+) and mixed GC- and AT-rich (CMA+/DAPI+) bands. Heterochromatin amount (expressed as % of karyotype length) ranged between 1.72 (C. chacoense) and 16.82 (C. flexuosum) and was positively correlated with karyotype length in most of the taxa examined. Heterochromatin located mainly at terminal position of chromosomes but intercalary position prevailed in C. flexuosum. Nucleolus organizer regions (NOR)-associated GC-rich heterochromatin was exclusively terminal and included the distal macrosatellite and a small portion on the corresponding arm. In all the taxa analysed, an equilocal heterochromatin distribution in non-homologous chromosomes of karyotype was observed, suggesting concerted evolution of heterochromatin dispersion in Capsicum.     

Fingerprints for main varieties of argentinean wines: terroir differentiation by inorganic, organic, and stable isotopic analyses coupled to chemometrics

Our main goal was to investigate if robust chemical fingerprints could be developed for three Argentinean red wines based on organic, inorganic, and isotopic patterns, in relation to the regional soil composition. Soils and wines from three regions (Mendoza, San Juan, and Co?rdoba) and three varieties (Cabernet Sauvignon, Malbec, and Syrah) were collected. The phenolic profile was determined by HPLC-MS/MS and multielemental composition by ICP-MS; (87)Sr/(86)Sr and δ(13)C were determined by TIMS and IRMS, respectively. Chemometrics allowed robust differentiation between regions, wine varieties, and the same variety from different regions. Among phenolic compounds, resveratrol concentration was the most useful marker for wine differentiation, whereas Mg, K/Rb, Ca/Sr, and (87)Sr/(86)Sr were the main inorganic and isotopic parameters selected. Generalized Procrustes analysis (GPA) using two studied matrices (wine and soil) shows consensus between them and clear differences between studied areas. Finally, we applied a canonical correlation analysis, demonstrating significant correlation (r = 0.99; p < 0.001) between soil and wine composition. To our knowledge this is the first report combining independent variables, constructing a fingerprint including elemental composition, isotopic, and polyphenol patterns to differentiate wines, matching part of this fingerprint with the soil provenance.     

Using Limestone to Improve Soil Fertility and Growth of Mango (Mangifera Indica L.)

Soil acidity is one of the most important factors limiting crop production. The objective of this work was to evaluate the effects of limestone application on the soil chemical properties, nutrition and yield of mango plants in an orchard under implementation. The design was randomized blocks, with five limestone doses (0; 2; 4; 6 and 8 t ha^?1) and four replications. Soil chemical analyses were performed (at 12, 24, 36 and 48 months after the experiment implementation) in the layers 0–20; 20–40 and 40–60 cm deep. Nutrition status and yield were assessed during the first and second crop seasons. The highest fruit yield was associated with the application of 3.9 and 3.8 t ha^?1 of limestone in the 2008/09 and 2009/10 seasons, respectively, that is, the dose recommended by the literature for raising base saturation to 80%, as a function of the fertility conditions of the soil initially obtained.     

Cloning and characterization of gene-resistant analogs (RGAs) involved in rust (Puccinia psidii) resistance in Eucalyptus grandis

Disease-resistant genes play an important role in defending against a variety of pathogens and insect pests in plants. Most of the disease-resistant genes encode pro-teins with conserved leucine rich r...     

Learning by doing: a participatory methodology for systematization of experiments with agroforestry systems, with an example of its application

Participatory research methods have helped scientists to understand how farmers experiment and to seek partnerships with farmers in developing technologies with enhanced relevance and adoption. This paper reports on the development of a participatory methodology to systematize long-term experimentation with agroforestry systems carried out in a hotspot of biodiversity by non-governmental organizations and local farmers. A methodological guide for systematization and techniques used for Participatory Rural Appraisal formed the basis of our work. We propose an analytical framework that recognizes systems of reflexive and learning interactions, in order to make the learned lessons explicit. At the process level, the main lessons and recommendations are as follows. It is important to establish partnerships to conduct innovative and complex experimentation with agroforest. Participatory systematization allows us to improve the methodological aspects of design, implementation and management of on-farm participatory experimentation. It also serves to synthesize the main findings and to extract lessons from agroforestry systems experiments. It fosters the technical improvement of agroforestry systems. It creates possibilities for reflection on agroforestry systems by farmers, extensionists and researchers, as well as their learning with respect to management of such systems. The findings are placed in the context of current theory on participatory experimentation in agriculture. Extractive and interactive approaches help to produce rich insights of mutual interest through collaboration by identifying local, regional and global convergences, complementarities, and conflicts of interest; which affect the advance of new eco-friendly technologies, to both improve the livelihoods and to reverse biodiversity loss and environmental degradation.     

Effects of X-rays on Tuta absoluta for use in inherited sterility programmes

Tuta absoluta is a key pest of tomato crops originating from South America. The consequences of X-radiation on this species were studied under laboratory controlled conditions. The effect of radiation on adult emergence was evaluated exposing male and female pupae to increasing X-rays. Adult emergence decreased as doses of X-radiation increased, with the appearance of deformities such as malformed wings and bent legs at doses ≥350?Gy. Besides, males and females obtained from irradiated pupae were out crossed with untreated counterparts to explore the effects of X-radiation on inherited sterility. (a) Irradiated male?×?untreated female crosses. Both fecundity and fertility of the untreated females were reduced by radiation, and the effect was stronger as the doses increased. Neither the longevity of parental males and F1 adults nor the sex ratios of the F1 and F2 generations were affected by X-radiation (F1 and F2: first and second generation of descendants of irradiated adults). Inherited sterility effects were manifested by a significant reduction in the F1 fecundity, F1 fertility, and the amount of larvae and pupae produced. Doses of 200–250?Gy could be used to induce inherited sterility in T. absoluta males. (b) Untreated male?×?irradiated female crosses. The minimum dose at which irradiated females were completely sterile was 200?Gy. The present study is the first study in T. absoluta that provides the starting point for implementing the inherited sterility in this species.     

Bayesian inference for multi-environment spatial individual-tree models with additive and full-sib family genetic effects for large forest genetic trials

Context

The gain in accuracy of breeding values with the use of single trial spatial analysis is well known in forestry. However, spatial analyses methodology for single forest genetic trials must be adapted for use with combined analyses of forest genetic trials across sites.

Aims

This paper extends a methodology for spatial analysis of single forest genetic trial to a multi-environment trial (MET) setting.

Methods

A two-stage spatial MET approach using an individual-tree model with additive and full-sib family genetic effects was developed. Dispersion parameters were estimated using Bayesian techniques via Gibbs sampling. The procedure is illustrated using height growth data at age 10 from eight large Tsuga heterophylla (Raf.) Sarg. second-generation full-sib progeny trials from two series established across seven sites in British Columbia (Canada) and on one in Washington (USA).

Results

The proposed multi-environment spatial mixed model displayed a consistent reduction of the posterior mean and an increase in the precision of error variances $ \left( {\sigma _{e}^{2}} \right) $ than the model with ??sets in replicates?? or incomplete block alpha designs. Also, the multi-environment spatial model provided an average increase in the posterior means of the narrow- and broad-sense individual-tree heritabilities (h _N ² and h _B ² , respectively). No consistent changes were observed in the posterior means of additive genetic correlations (r _Ajj??).

Conclusion

Although computationally demanding, all dispersion parameters were successfully estimated from the proposed multi-environment spatial individual-tree model using Bayesian techniques via Gibbs sampling. The proposed two-stage spatial MET approach produced better results than the commonly used nonspatial MET analysis.