Effect of Al hydroxide polymers on cation exchange of montmorillonite

Al hydroxide polymers (AlHO) can significantly influence the cation exchange behaviour of clays. We have determined the effect of synthesized AlHO on Ca–Na, Zn–Na and Pb–Na exchange for a series of exchanger compositions and two Al loadings at pH 6.0 and an ionic strength of 0.01  m . The preference for Ca on the siloxane surface of the clay–AlHO system (CAlHO) was greater than for the pure clay, and the average K _V (Vanselow selectivity coefficient) was determined to be 2.16 and 1.24, respectively. The selectivity coefficients for the exchange reactions Zn–Na and Pb–Na were not directly determined in CAlHO systems, because heavy-metal ions bind as well to the clay surface as to the AlHO over a wide range of pH. We have estimated the effect of the presence of AlHO on the selectivity coefficients of Zn–Na and Pb–Na exchange by extrapolation of the experimental results of Ca–Na, Zn–Na and Pb–Na exchange for pure clay and Ca–Na exchange for CAlHO. The average K _V was increased by the presence of the AlHO from 1.23 to 2.16 for Zn–Na exchange and from 1.59 to 2.77 for Pb–Na exchange. The increase in the preference for the divalent cations is probably caused by parallel alignment of clay platelets by sorption of AlHO. Increasing the amount of AlHO did not change the selectivity for Ca–Na exchange, and probably the structure of the system or the arrangement of the clay platelets and AlHO particles was not substantially changed. This was supported by the linear reduction of the cation exchange capacity with amount of AlHO present at pH 6.6. It seems likely that the selectivity coefficients for Ca–Na, Zn–Na and Pb–Na exchange that we found apply in naturally occurring montmorillonite–AlHO systems.     

Empirical predictions of plant material C and N mineralization patterns from near infrared spectroscopy, stepwise chemical digestion and C/N ratios

Prediction of carbon (C) and nitrogen (N) mineralization patterns of plant litter is desirable for both agronomic and environmental reasons. Near infrared reflectance (NIR) spectroscopy has recently been introduced in decomposition studies to characterize biochemical composition. The purpose of the current study was to use empirical techniques to predict C and N mineralization patterns of a wide range of plant materials incubated under controlled temperature and moisture conditions. We hypothesized that the richness of information in the NIR spectra would considerably improve predictions compared to traditional stepwise chemical digestion (SCD) or C/N ratios. Initially, we fitted a number of empirical functions to the observed C and N mineralization patterns. The best functions fitted with R²=0.990 and 0.949 to C and N, respectively. The fractions of C and N mineralized at different points in time were then either predicted directly with regression functions or indirectly by prediction of the parameters of the empirical functions fitted to incubation data. In both cases, partial least squares (PLS) regressions were used and predictions were validated by cross-validations. We found that the NIR spectra (best R²=0.925) were able to predict C mineralization patterns marginally better than the SCD fractions (best R²=0.911), but considerably better than the C/N ratios (best R²=0.851). In contrast, N mineralization was better predicted by SCD fractions (best R²=0.533) than the C/N ratio (best R²=0.497), which was better than NIR predictions (best R²=0.446). Although the predictions with the NIR spectra were only slightly better for C and worse for N mineralization compared to SCD fractions, NIR spectroscopy still holds advantages, as it is a much less laborious and cheaper analytical method. Furthermore, exploration of the applications of NIR spectroscopy in decomposition studies has only just begun, and offers new ways to gain insights into the decomposition process.     

A global benchmark map of water productivity for rainfed and irrigated wheat

The growing pressure on fresh water resources demands that agriculture becomes more productive with its current water use. Increasing water productivity is an often cited solution, though the current levels of water productivity are not systematically mapped. A global map of water productivity helps to identify where water resources are productively used, and identifies places where improvements are possible. The WATPRO water productivity model for wheat, using remote sensing data products as input, was applied at a global scale with global data sets of the NDVI and surface albedo to benchmark water productivity of wheat for the beginning of this millennium. Time profiles of the NDVI were used to determine the time frame from crop establishment to harvest on a pixel basis, which was considered the modelling period. It was found that water productivity varies from approximately 0.2 to 1.8 kg of harvestable wheat per cubic metre of water consumed. From the 10 largest producers of wheat, France and Germany score the highest country average water productivity of 1.42 and 1.35 kg m⁻³, respectively. The results were compared with modelling information by Liu et al. (2007) who applied the GEPIC model at a global scale to map water productivity, and by Chapagain and Hoekstra (2004) who used FAO statistics to determine water productivity per country. A comparison with Liu et al. showed a good correlation for most countries, but the correlation with the results by Chapagain and Hoekstra was less obvious. The global patterns of the water productivity map were compared with global data sets of precipitation and reference evapotranspiration to determine the impact of climate and of water availability reflected by precipitation. It appears that the highest levels of water productivity are to be expected in temperate climates with high precipitation. Due to its non-linear relationship with precipitation, it is expected that large gains in water productivity can be made with in situ rain water harvesting or supplemental irrigation in dry areas with low seasonal precipitation. A full understanding of the spatial patterns by country or river basin will support decisions on where to invest and what measures to take to make agriculture more water productive.     

Sustainable Aquaculture Products: Implications of Consumer Awareness and of Consumer Preferences for Promising Market Communication in Germany

Sustainability issues are becoming increasingly important in Western food consumption. This research aims at identifying ways to increase the market share of sustainable fish from aquaculture by analyzing consumer awareness and preferences for sustainable aquaculture products and by drawing conclusions on improving communication in the German market. An integrative research approach was used: it began with an inventory of sustainable seafood products on the German market, followed by qualitative and quantitative consumer research, and ending with a stakeholder workshop, which reflected on the research outcome with practitioners. Sustainability and country of origin proved to be of minor relevance to the vast majority of the consumers interviewed. Indeed, attributes such as freshness, taste, and price frequently ranked higher. Like most of the other consumers, the small consumer segment that is generally interested in sustainability issues of food also often lacks knowledge of fish farming and its products. Consequently, communication of sustainability in aquaculture is challenging, and the development of this market requires concerted actions along the whole value chain, with the retail sector as the main actor.     

Linking models for assessing agricultural land use change

The ex-ante assessment of the likely impacts of policy changes and technological innovations on agriculture can provide insight into policy effects on land use and other resources and inform discussion on the desirability of such changes. Integrated assessment and modeling (IAM) is an approach that can be used for ex-ante assessment. It may combine several quantitative models representing different processes and scales into a framework for integrated assessment to allow for multi scale analysis of environmental, economic and social issues. IAM is a challenging task as models from different disciplines have a different representation of data, space and time. The aim of this paper is to describe our strategy to conceptually, semantically and technically integrate a chain of models from different domains to assess land use changes. The models that were linked are based on different modeling techniques (e.g. optimization, simulation, estimation) and operate on different time and spatial scales. The conceptual integration to ensure consistent linkage of simulated processes and scales required modelers representing the different models to clarify the data exchanged and interlinking of modeling methodologies across scales. For semantic integration, ontologies provided a way to rigorously define conceptualizations that can be easily shared between various disciplines. Finally, for technical integration, OpenMI was used and supplemented with the information from ontologies. In our case, explicitly tackling the challenge of semantic, conceptual and technical integration of models forced researchers to clarify the assumptions of their model interfaces, which helped to document the model linkage and to efficiently run models together. The linked models can now easily be used for integrated assessments of policy changes, technological innovations and societal and biophysical changes.     

Long-Term Production and Profitability From Grazing Cattle in the Northern Mixed Grass Prairie

Conventional wisdom among rangeland professionals has been that for long-term sustainability of grazing livestock operations, rangeland should be kept in high good to low excellent range condition. Our objective was to analyze production parameters, costs, returns, and profit using data generated over a 34-yr period (1969–2002) from grazing a Clayey range site in the mixed-grass prairie of western South Dakota with variable stocking rates to maintain pastures in low–fair, good, and excellent range condition classes. Cattle weights were measured at turnout and at the end of the grazing season. Gross income · ha^?1 was the product of gain · ha^?1 and price. Prices were based on historical National Agricultural Statistics Services feeder cattle prices. Annual variable costs were estimated using a yearling cattle budget developed by South Dakota State University agricultural economists. All economic values were adjusted to a constant dollar using the Bureau of Labor Statistics' Consumer Price Index. Stocking rate, average daily gain, total gain, net profit, gross revenue, and annual costs · ha^?1 varied among range condition classes. Net income for low–fair range condition ($27.61 · ha^?1) and good range condition ($29.43 · ha^?1) were not different, but both were greater than excellent range condition ($23.01 · ha^?1). Over the life of the study, real profit (adjusted for inflation) steadily increased for the low–fair and good treatments, whereas it remained level for the excellent treatment. Neither drought nor wet springs impacted profit differently for the three treatments. These results support generally observed rancher behavior regarding range condition: to maintain their rangeland in lower range condition than would be recommended by rangeland professionals. Ecosystem goods and services of increasing interest to society and associated with high range condition, such as floristic diversity, hydrologic function, and some species of wildlife, come at an opportunity cost to the rancher.