Prediction of baking results from farinograph measurements by using stepwise linear regression and artificial neuronal networks

In an effort to extract additional data from farinograph experiments a model was developed to simulate the measurements and correlate the parameters of the model with results from baking tests. This additional information can be used in bakeries to predict the baking properties of the flours and adjust the recipes to maintain a constant product quality. For this eight different flours were characterized with a farinograph and 13 different results from baking experiments. An approach with five nonlinear differential equations was able to model the farinograph measurements very well (average R² = 0.995 ± 0.005). While a stepwise multilinear regression only showed weak correlations in cross validation between a single parameter of the model and the baking volume (R² = 0.745) and the volume yield (R² = 0.796) respectively, the artificial neuronal network was more successful. For the baking weight (R² = 0.926), the dough yield gross (R² = 0.909) and net (R² = 0.913) strong correlations were found. A good correlation for the baking volume (R² = 0.853) was also determined, while the volume yield showed comparable results to the linear regression (R² = 0.792).     

Effect of process conditions and amylose/amylopectin ratio on the pasting behavior of maize starch: A modeling approach

The effects of different process conditions on the pasting behavior of the 14%, w/w suspensions of high amylose, waxy and normal maize starches at mixing speeds of 50, 160 and 250 rpm with the heating rates of 2.5, 5 and 10 °C/min were investigated. In addition, the impact of the starch mixture with an amylose-amylopectin ratio of 0–70% at 160 rpm and a heating rate of 5 °C/min on the pasting parameters was studied. According to the results, when stirring speed decreased from 250 rpm to 50 rpm, the peak viscosity dramatically increased. Furthermore, both heating and stirring rates significantly affected the pasting properties (p < 0.05). The amylose content of maize starch had a negative correlation with peak viscosity, trough viscosity, breakdown viscosity, final viscosity, and setback viscosity. Besides, syneresis values decreased as amylose content decreased from 70% to 0%. According to the kinetic modelling of pasting curves, starch coefficients were found to be higher than 1 for all starches, indicating that the penetration of water into starch granules increased granule swelling rate. The findings of the present study confirmed that both process conditions and amylose/amylopectin ratio can be optimized without necessity of starch modification to obtain the products with the desired quality.     

Simulating farming practices within a region using a stochastic bio-decisional model: Application to irrigated maize in south-western France

Agricultural is a major contributor to environmental resource management problems. Modelling the distribution of agricultural land use to evaluate current situations or scenarios is an important issue for policy-makers and natural resource managers. A promising approach is the use of bio-decisional models based on decision rules. However, at the regional scale, the large number of farmers makes it difficult to identify decision rules, and the diversity of farmers' decisions is rarely considered. To this end, we developed SIMITKO, a spatialised and stochastic bio-decisional model, able to simulate the spatial and temporal variability in farming practices. We focused on the choice of varietal earliness and sowing practices of maize (Zea mays L.) in the Baïse sector (south-western France). Model development was based on statistical analyses of surveys of farmers’ practices to identify their current strategies, the best variables to describe the practices and the probabilities associated with the values of the variables for each strategy. We tested SIMITKO by simulating the dynamics of areas sown with maize. Comparing model predictions of practices to observed data showed generally good predictions of sowing dynamics but less satisfactory predictions of varietal earliness choices. Possible improvements are suggested.     

How to characterize ‘good’ and ‘greening’ in the EU Common Agricultural Policy (CAP): the case of clay soils in the Netherlands

The change in the Common Agricultural Policy (CAP) of the European Union from product to producer support, including requirements for ‘good agricultural and environmental conditions’ and ‘greening’, is excellent. However, these requirements are now defined in rather general terms. Questions can be raised about suitable indicators, and there is a recognized need for effective management recommendations to support farmers in achieving the required ‘good’ conditions. These recommendations are bound to be quite different for different soils in different countries. A study of Dutch clay soils was based on a storyline describing current problems and management options for improvement, which were quantified using a soil–water–crop simulation model. Indicators were defined for agricultural conditions and suggestions made for the use of the model in a predictive mode to help farmers improve their soil management. Environmental conditions were judged by current environmental guidelines for water and air. When modelling, implicit assumptions that soils are homogeneous were shown to be unrealistic for these clay soils, requiring development of innovative methods and procedures, presenting a challenge for soil research.     

A quantitative assessment of the validity of animal-health surveys using stochastic modelling

This paper presents a stochastic simulation model to evaluate the efficacy of regional or national surveys aimed at identifying infection in populations of animals. The process of evaluation involves specification or calculation of cluster-level test sensitivity and specificity, which are derived from two probability distributions of the number of individual-level positive tests expected from non-infected and infected clusters, respectively. Probability distributions for the number of positive clusters expected in a situation of freedom from infection and under various levels of cluster prevalence are specified and used to determine survey properties (the survey being considered a diagnostic system), and ROC curves are drawn. Likelihood ratios allow investigators to state the extent to which a survey result is more likely to be observed if the region or country is infected at a given prevalence than if it is free from infection. The result of a survey carried out to investigate the presence of porcine reproductive and respiratory syndrome (PRRS) in Switzerland is used to illustrate this approach. The model can be adapted to a wide range of survey designs.     

Empirical algebraic modelling of live weight of Irish dairy cows over lactation

The aim of this study is to derive an equation that has the ability to model live weight of Irish dairy cows over lactation. The data set consisted of 6899 cows from 63 herds, of which 428 were from experimental herds and 6471 cows were from commercial herds. An initial examination focussed on time series techniques, as the data are of a time series nature. Splines were also examined to determine the dimensions of a model required to represent the data. As an incomplete gamma function, which was previously used to model milk yield, has been used in other studies to model live weight, various milk yield models were investigated. Finally, live weight changes between two calvings were modelled as a function of age, days in milk and pregnancy. As multicollinearity was evident in this function, the variance inflation factor was examined and principal component analysis was carried out on the variables responsible for multicollinearity. The proposed live weight model has a better fit than previous models, weak multicollinearity and the residuals are homoskedastic, independent and normally distributed. This live weight model therefore provides an acceptable level of accuracy in representing the shape of the live weight curve for Irish dairy cows and can be easily modified for different environmental scenarios.     

A linear programming assessment of the profit from strategies to reduce the prevalence of Staphylococcus aureus mastitis

We used a linear programming model to estimate the financial returns to a Staphylococcus aureus testing and control program over a 1-year period for a 100-cow herd, with a 8636-kg rolling-herd average. Six tests, which vary in sensitivity from 0.80 to 0.98 and specificity of 0.99, were examined in simulated herds with 10, 20 and 30% prevalence of S. aureus infection. Sensitivity of these results to a range of assumptions regarding rolling-herd average, milk price, somatic cell-count premium, and cost and cure rate of dry treatment were examined to determine the profits from the program. The profits of a control program are most dependent upon prevalence, cell-count premium, and cost of dry treatment. In our simulation for a 100-cow herd, a testing and control program appears to cost less than US$10 per cow per year, and pays for itself within 1 yr, except under the lowest prevalence and most-adverse conditions (low yield, high cost of dry treatment, or low SCC premium).     

Application of the Soil and Water Assessment Tool (SWAT) to predict the impact of alternative management practices on water quality and quantity

Alternative land management practices such as conservation or no-tillage, contour farming, terraces, and buffer strips are increasingly used to reduce nonpoint source and water pollution resulting from agricultural activities. Models are useful tools to investigate effects of such management practice alternatives on the watershed level. However, there is a lack of knowledge about the sensitivity of such models to parameters used to represent these conservation practices. Knowledge about the sensitivity to these parameters would help models better simulate the effects of land management. Hence, this paper presents in the first step a sensitivity analysis for conservation management parameters (specifically tillage depth, mechanical soil mixing efficiency, biological soil mixing efficiency, curve number, Manning's roughness coefficient for overland flow, USLE support practice factor, and filter strip width) in the Soil and Water Assessment Tool (SWAT). With this analysis we aimed to improve model parameterisation and calibration efficiency. In contrast to less sensitive parameters such as tillage depth and mixing efficiency we parameterised sensitive parameters such as curve number values in detail.In the second step the analysis consisted of varying management practices (conventional tillage, conservation tillage, and no-tillage) for different crops (spring barley, winter barley, and sugar beet) and varying operation dates. Results showed that the model is very sensitive to applied crop rotations and in some cases even to small variations of management practices. But the different settings do not have the same sensitivity. Duration of vegetation period and soil cover over time was most sensitive followed by soil cover characteristics of applied crops.     

A European irrigation map for spatially distributed agricultural modelling

We present a pan-European irrigation map based on regional European statistics, a European land use map and a global irrigation map. The map provides spatial information on the distribution of irrigated areas per crop type which allows determining irrigated areas at the level of spatial modelling units. The map is a requirement for a European scale assessment of the impacts of irrigated agriculture on water resources based on spatially distributed modelling of crop growth and water balance. The irrigation map was compiled in a two step procedure. First, irrigated areas were distributed to potentially irrigated crops at a regional level (European statistical regions NUTS3), combining Farm Structure Survey (FSS) data on irrigated area, crop-specific irrigated area for crops whenever available, and total crop area. Second, crop-specific irrigated area was distributed within each statistical region based on the crop distribution given in our land use map. A global map of irrigated areas with a 5′ resolution was used to further constrain the distribution within each NUTS3 based on the density of irrigated areas. The constrained distribution of irrigated areas as taken from statistics to a high resolution dataset enables us to estimate irrigated areas for various spatial entities, including administrative, natural and artificial units, providing a reasonable input scenario for large-scale distributed modelling applications. The dataset bridges a gap between global datasets and detailed regional data on the distribution of irrigated areas and provides information for various assessments and modelling applications.     

Concept and feasibility study for the integrated evaluation of environmental monitoring data in forests

In the 1970s unexpected forest damages, called “new type of forest damage” or “forest decline”, were observed in Germany and other European countries. The Federal Republic of Germany and the German Federal States implemented a forest monitoring system in the early 1980s, in order to monitor and assess the forest condition. Due to the growing public awareness of possible adverse effects of air pollution on forests, in 1985 the ICP Forests was launched under the convention on long-range transboundary air pollution (CLRTAP) of the United Nations Economic Commission for Europe (UN-ECE). The German experience in forest monitoring was a base for the implementation of the European monitoring system. In 2001 the interdisciplinary case study “concept and feasibility study for the integrated evaluation of environmental monitoring data in forests”, funded by the German Federal Ministry of Education and Research, concentrated on in-depths evaluations of the German data of forest monitoring. The objectives of the study were: (a) a reliable assessment of the vitality and functioning of forest ecosystems, (b) the identification and quantification of factors influencing forest vitality, and (c) the clarification of cause-effect-relationships leading to leaf/needle loss. For these purposes additional data from external sources were acquired: climate and deposition, for selected level I plots tree growth data, as well as data on groundwater quality. The results show that in particular time series analysis (crown condition, tree growth, and tree ring analysis), in combination with climate and deposition are valuable and informative, as well as integrated evaluation of soil, tree nutrition and crown condition data. Methods to combine information from the extensive and the intensive monitoring, and to transfer process information to the large scale should be elaborated in future.