• Energy Research
• Wageningen University & Research close

Reducing the usage of fossil fuels is a central issue in ongoing policy debates. This in particular holds for Dutch horticulture, given its energy-intensive production. We analyse differences in energy usage and price responsiveness of horticultural firms by estimating energy demand functions using a Bayesian random coefficient model. Beyond, the effects of a proposed energy tax are assessed. Allowing for firm-specific energy price coefficients gives a better model fit compared to conventional models with fixed slope parameters. This confirms that firms respond differently to energy prices, which is taken into account in simulating the effects of more restrictive energy policies. The results show that larger-sized firms use less gas per square meter yet also point at a considerable spread in additional energy expenses between firms.

The objectives of the study were to develop a multivariate framework for analyzing energy balance data from lactating cows and investigate potential changes in maintenance requirements and partial efficiencies of energy utilization by lactating cows over the years. The proposed model accounted for the fact that metabolizable energy intake, milk energy output, and tissue energy balance are random variables that interact mutually. The model was specified through structural equations implemented in a Bayesian framework. The structural equations, along with a model traditionally used to estimate energetic parameters, were fitted to a large database of indirect calorimetry records from lactating cows. Maintenance requirements and partial efficiencies for both models were similar to values reported in the literature. In particular, the estimated parameters (with 95% credible interval in parentheses) for the proposed model were: net energy requirement for maintenance equal to 0.36 (0.34, 0.38) MJ/kg of metabolic body weight·day; the efficiency of utilizing dietary energy for milk production and tissue gain were 0.63 (0.61, 0.64) and 0.70 (0.68, 0.72), respectively; the efficiency of utilizing body stores for milk production was 0.89 (0.87, 0.91). Furthermore, additional analyses were conducted for which energetic parameters were allowed to depend on the decade in which studies were conducted. These models investigated potential changes in maintenance requirements and partial efficiencies over the years. Canonical correlation analysis was used to investigate the association between changes in energetic parameters with additional dietary and animal characteristics available in the database. For both models, net energy requirement for maintenance and the efficiency of utilizing dietary energy for milk production and tissue gain increased in the more recent decades, whereas the efficiency of utilizing body stores for milk production remained unchanged. The increase in maintenance requirements in modern milk production systems is consistent with the literature that describes increased fasting heat production in cows of higher genetic merit. The increase in utilization of dietary energy for milk production and tissue gain was partially attributed to the changes in dietary composition, in particular to the increase in dietary ether extract to levels closer to currently observed in modern milk production systems. Therefore, the estimated energetic parameters from this study can be used to update maintenance requirements and partial efficiencies of energy utilization in North American feeding systems for lactating cows.

AbstractReducing the usage of fossil fuels is a central issue in ongoing policy debates. This in particular holds for Dutch horticulture, given its energy-intensive production. We analyse differences in energy usage and price responsiveness of horticultural firms by estimating energy demand functions using a Bayesian random coefficient model. Beyond, the effects of a proposed energy tax are assessed. Allowing for firm-specific energy price coefficients gives a better model fit compared to conventional models with fixed slope parameters. This confirms that firms respond differently to energy prices, which is taken into account in simulating the effects of more restrictive energy policies. The results show that larger-sized firms use less gas per square meter yet also point at a considerable spread in additional energy expenses between firms.

Closed photobioreactors have to be optimized in terms of light utilization and overall photosynthesis rate. A simple model coupling the hydrodynamics and the photosynthesis kinetics has been proposed to analyze the photosynthesis dynamics due to the continuous shuttle of microalgae between dark and lighted zones of the photobioreactor. Microalgal motion has been described according to a stochastic Lagrangian approach adopting the turbulence model suitable for the photobioreactor configuration (single vs. two-phase flows). Effects of light path, biomass concentration, turbulence level and irradiance have been reported in terms of overall photosynthesis rate. Different irradiation strategies (internal, lateral and rounding) and several photobioreactor configurations (flat, tubular, bubble column, airlift) have been investigated. Photobioreactor configurations and the operating conditions to maximize the photosynthesis rate have been pointed out. Results confirmed and explained the common experimental observation that high concentrated cultures are not photoinhibited at high irradiance level.

Convolutional neural networks (CNNs) represent the new reference approach for semantic segmentation of very-high-resolution (VHR) images, due to their ability to automatically capture semantic information while learning relevant features. However, as for most supervised methods, the map accuracy depends on the quantity and quality of ground truth (GT) used to train them. The use of densely annotated data (i.e., a detailed, exhaustive, pixel-level GT) allows to obtain effective CNN models but normally implies high efforts in annotation. Such ground truth is often available in benchmark datasets on which new methods are tested, but not on real data for land-cover applications, where only sparse annotations might be sufficiently cost effective. A CNN model trained with such incomplete GT maps has the tendency to smooth object boundaries because they are never precisely delineated in the GT. To cope with those shortcomings, we propose to exploit the intermediate activation maps of the CNN and to deploy a semisupervised fully connected conditional random field (CRF). In comparison with competitors using the same sparse annotations, the proposed method is able to better fill part of the performance gap compared to a CNN trained on the densely annotated, but generally unavailable, GTs.

The objectives of the study were to develop a multivariate framework for analyzing energy balance data from lactating cows and investigate potential changes in maintenance requirements and partial efficiencies of energy utilization by lactating cows over the years. The proposed model accounted for the fact that metabolizable energy intake, milk energy output, and tissue energy balance are random variables that interact mutually. The model was specified through structural equations implemented in a Bayesian framework. The structural equations, along with a model traditionally used to estimate energetic parameters, were fitted to a large database of indirect calorimetry records from lactating cows. Maintenance requirements and partial efficiencies for both models were similar to values reported in the literature. In particular, the estimated parameters (with 95% credible interval in parentheses) for the proposed model were: net energy requirement for maintenance equal to 0.36 (0.34, 0.38) MJ/kg of metabolic body weight·day; the efficiency of utilizing dietary energy for milk production and tissue gain were 0.63 (0.61, 0.64) and 0.70 (0.68, 0.72), respectively; the efficiency of utilizing body stores for milk production was 0.89 (0.87, 0.91). Furthermore, additional analyses were conducted for which energetic parameters were allowed to depend on the decade in which studies were conducted. These models investigated potential changes in maintenance requirements and partial efficiencies over the years. Canonical correlation analysis was used to investigate the association between changes in energetic parameters with additional dietary and animal characteristics available in the database. For both models, net energy requirement for maintenance and the efficiency of utilizing dietary energy for milk production and tissue gain increased in the more recent decades, whereas the efficiency of utilizing body stores for milk production remained unchanged. The increase in maintenance requirements in modern milk production systems is consistent with the literature that describes increased fasting heat production in cows of higher genetic merit. The increase in utilization of dietary energy for milk production and tissue gain was partially attributed to the changes in dietary composition, in particular to the increase in dietary ether extract to levels closer to currently observed in modern milk production systems. Therefore, the estimated energetic parameters from this study can be used to update maintenance requirements and partial efficiencies of energy utilization in North American feeding systems for lactating cows.

We tested whether Tundra Swans use information on the spatial distribution of cryptic food items (below ground Sago pondweed tubers) to shape their movement paths. In a continuous environment, swans create their own food patches by digging craters, which they exploit in several feeding bouts. Series of short (1 m). Tuber biomass densities showed a positive spatial auto-correlation at a short distance (25 g/m2) and to a more distant patch (at 7-8 m) if the food density in the current patch had been low (3 m) from a low-density patch and a short distance (<3 m) from a high-density patch. The quantitative agreement between prediction and observation was greater for swans feeding in pairs than for solitary swans. The result of this movement strategy is that swans visit high-density patches at a higher frequency than on offer and, consequently, achieve a 38% higher long-term gain rate. Swans also take advantage of spatial variance in food abundance by regulating the time in patches, staying longer and consuming more food from rich than from poor patches. We can conclude that the shape of the foraging path is a reflection of the spatial pattern in the distribution of tuber densities and can be understood from an optimal foraging perspective.