Utilization of Weather Data in Predicting Bread Loaf Volume by Neural Network Method

The unique ability of wheat (Triticum aestivum L.) flour to produce cohesive dough has helped to make it the most widely used cereal crop for bread and other baked food products. Measurement of end‐use qualities, such as loaf volume, is ideally carried out through assessing loaves of bread; however, this is resource intensive. Predictive testing methods are more often utilized to identify wheat genotypes with potentially acceptable loaf volume, although more accurate predictive methods would be beneficial. Our objectives were to study the influence of weather observations on bread loaf volume and flour and dough quality data, to use a neural network (NN) model to predict loaf volume with select input data, and to compare the best multiple regression models identified with their NN counterparts. Weather data collected at 20 days after heading (DAH) showed the highest correlations with bread loaf volume when compared with prior intervals. A NN model containing maximum, minimum, and nighttime temperatures produced the highest coefficient of determination for predicting loaf volume. Our results showed that the NN model explained up to 20% more loaf volume variation than a similar regression model. Weather parameters representing conditions at 20 DAH played a significant role in loaf volume prediction.     

Prevalence of Concentrated Flow Paths in Agricultural Fields in Southern Illinois

Literature on prevalence of concentrated flow paths (CPFs) in agricultural fields are limited at field scale with only few studies that address occurrence of CFPs at large geographic extent. This study used high-resolution Light Detection and Ranging (LiDAR) data to identify CFPs in agricultural fields and calculate the percentage of the fields drained by CFPs at a county scale. In 389 agricultural fields across Jackson County, southern Illinois, this study also investigated the association between field characteristics and CFP formation using multiple regression and CART analysis. The mean number of CFPs in a field was 5 with a minimum of 0 and maximum of 17 CFPs. The majority of the CFPs fell under the large category for CFP length and drainage area that corresponds with high mean percent (81%) of field area drained by the CFPs. Further, 85% of the fields had more than 70% of their area drained by CFPs. The multiple regression and CART analysis showed slope as an important factor influencing CFP characteristics such as number of CFPs and CFP length. Both analyses also indicated physical soil properties such as bulk density, soil erodibility factor, saturated hydraulic conductivity, LS factor, organic matter, and percent sand were also predictors of the CFP characteristics. However, these factors explained only 2 to 12% of the variation observed. The significant presence of CFP’s has important implications for water quality since current conservation practices such as riparian buffers were not designed to address concentrated flow from agricultural fields.     

Korea’s importance in the East Asiatic gene center

New evidence from archeology and human genetics provides strong support for the introduction of rice agriculture to Japan by Korean farmers. This sheds new light on the formation and further evolution of the East Asiatic gene center and stresses the importance of Korean agri- and horticulturists in Eastern Asia.     

Application of a Balanced Scorecard System for Supporting Decision-Making in Contaminated Sites Remediation

This work introduces the development and application of a multi-goal evaluation procedure for assessing sets of remedial options that have previously been found technically suitable for managing a certain contaminated site. The construction of this decision support tool, in the form of a balanced scorecard, has followed a multi-attribute conception of the contaminated sites problem, identifying the most critical aspects considered by all stakeholders involved in contaminated sites management decisions. The developed tool has been checked in its formulation and application against an export benchmark of an Austrian remediation project. The performed evaluation allowed to identify and suggest a remedial option that best fitted the expectations of all stakeholders involved in the project. Furthermore, through the accomplishment of a sensitivity analysis it has been demonstrated that the system is stable and robust in terms of the chosen elicitation schemes, and that the preferences of the different stakeholders are influencing factors on the final result. The results and conclusions of this work present the benefits of implementing the developed balanced scorecard as a tool for supporting decision making in contaminated sites remediation projects.     

Water yam (Dioscorea alata L.) diversity pattern in Brazil: an analysis with SSR and morphological markers

Substantial progress was made in the last decade in understanding the diversity in Dioscorea species. However, most of the studies so far concentrated in the ‘yam belt in Africa’. We present a genetic diversity and structure analysis among commercial and local varieties of water yam (Dioscorea alata L.) in Brazil using microsatellite and morphological markers. Twelve microsatellite primers were used to generate DNA profiles of 72 local varieties and 17 commercial accessions of water yam collected in four different regions in Brazil. Also, four morphological traits were evaluated on individual plants under field conditions. The morphological characterization showed considerable diversity. High polymorphism was found with 100 % polymorphism observed for 11 primers and a discriminating power value of 0.92, on average. We did not observe a strong population structure among sampling regions, while most of the genetic diversity was concentrated within regions (95.9 %). Analysis of the relationship between accessions did not clearly separate the local and commercial genotypes. However, the molecular analyses of D. alata showed high intraspecific diversity in local accessions from different regions in Brazil, with the highest Shannon index value (H′ = 0.41) for the Southeast region. These results confirmed an admixture of accessions in all sampling regions, consistent with the lack of a significant correlation between geographic and genetic distances, suggesting that farmers exchanged water yam materials extensively. The genetic diversity can be explained by the result of a continuous exchange of accessions by farmers throughout Brazil.     

Assessment of sediment connectivity from vegetation cover and topography using remotely sensed data in a dryland catchment in the Spanish Pyrenees

Purpose

Many Mediterranean drylands are characterized by strong erosion in headwater catchments, where connectivity processes play an important role in the redistribution of water and sediments. Sediment connectivity describes the ease with which sediment can move through a catchment. The spatial and temporal characterization of connectivity patterns in a catchment enables the estimation of sediment contribution and transfer paths. Apart from topography, vegetation cover is one of the main factors driving sediment connectivity. This is particularly true for the patchy vegetation cover typical of many dryland environments. Several connectivity measures have been developed in the last few years. At the same time, advances in remote sensing have enabled an improved catchment-wide estimation of ground cover at the subpixel level using hyperspectral imagery.

Materials and methods

The objective of this study was to assess the sediment connectivity for two adjacent subcatchments (~70 km²) of the Isábena River in the Spanish Pyrenees in contrasting seasons using a quantitative connectivity index based on fractional vegetation cover and topography data. The fractional cover of green vegetation, non-photosynthetic vegetation, bare soil and rock were derived by applying a multiple endmember spectral mixture analysis approach to the hyperspectral image data. Sediment connectivity was mapped using the index of connectivity, in which the effect of land cover on runoff and sediment fluxes is expressed by a spatially distributed weighting factor. In this study, the cover and management factor (C factor) of the Revised Universal Soil Loss Equation (RUSLE) was used as a weighting factor. Bi-temporal C factor maps were derived by linking the spatially explicit fractional ground cover and vegetation height obtained from the airborne data to the variables of the RUSLE subfactors.

Results and discussion

The resulting connectivity maps show that areas behave very differently with regard to connectivity, depending on the land cover and on the spatial distribution of vegetation abundances and topographic barriers. Most parts of the catchment show higher connectivity values in August as compared to April. The two subcatchments show a slightly different connectivity behaviour that reflects the different land cover proportions and their spatial configuration.

Conclusions

The connectivity estimation can support a better understanding of processes controlling the redistribution of water and sediments from the hillslopes to the channel network at a scale appropriate for land management. It allows hot spot areas of erosion to be identified and the effects of erosion control measures, as well as different land management scenarios, to be studied.