This p a p e r e x a m i n e s the influence of social and e c o n o m i c change on family structure and relationships: H o w do such e c o n o m i c and social transformations as industrialization, urbanization, demographic change, the expansion of education, and the long-term g r o w t h of i n c o m e influence the family? W e take a c o m p a r a t i v e and historical approach, r e v i e w i n g the e x p e r i e n c e s of three major sociocultural regions: the West, China, and South Asia. Many of the changes that have o c c u r r e d in family life have b e e n r e m a r k a b l y similar in the three s e t t i n g s-t h e separation of the w o r k p l a c e from the home, increased training of c h i l d r e n in n o~i l i a l institutions, the d e v e l o p m e n t of living arrangements outside the family household, increased access of c h i l d r e n to financial and o t h e r p r o d u ctive resources, and increased participation b y c h i l d r e n in the selection o f a mate. While the similarities of family change in diverse cultural settings are striking, specific aspects of change have varied across settings because of significant pre-existing differences in family structure, residential patterns o f marriage, a u t o n o m y of children, and the role of marriage within kinship systems." This p a p e r examines one of the central issues in the social sciences: h o w social and e c o n o m i c change influences family structure and relationships. More specifically, h o w do such social and e c o n o m i c trans

Grassland systems frequently exhibit small‐scale botanical and structural heterogeneity with pronounced spatio‐temporal dynamics. These features present particular challenges for sensor applications, in addition to limitations posed by the high cost and low spatial resolution of many available remote‐sensing (RS) systems. There has been little commercial application of RS for practical grassland farming. This article considers the developments in sensor performance, data analysis and modelling over recent decades, identifies significant advances in RS for grassland research and practice and reviews the most important sensor types and corresponding findings in research. Beside improvements of single sensor types, the development of systems with complementary sensors is seen as a very promising research area, and one that will help to overcome the limitations of single sensors and provide better information about grassland composition, yield and quality. From an agronomic point of view, thematic maps of farm fields are suggested as the central outcome of RS and data analysis. These maps could represent the relevant grassland features and constitute the basis for various farm management decisions at strategic, tactical and operational levels. The overarching goal will be to generate low cost, appropriate and timely information that can be provided to farmers to support their decision‐making.

The use of semi-natural grasslands for the production of renewable energy through conventional conversion techniques faces major limitations because of chemical and physical properties of the biomass. A new conversion procedure was developed which separates the biomass, as silage, into a liquid phase for biogas production and into a solid fraction to be used as fuel. Separation (mechanical dehydration) is carried out with a screw press after mashing with water (hydrothermal conditioning). The effect of hydrothermal conditioning at different temperatures (5, 60 and 80°C) and mechanical dehydration on mass flows of plant compounds into the press fluid was investigated for five grassland pastures typical of mountain areas of Germany. Results show that 0AE18 of the crude fibre was transferred into the fluid, whereas more digestible organic compounds, such as crude protein and nitrogen-free extract, showed mass flows of 0AE40 and 0AE31 respectively. While 0AE52-0AE89 of potassium (K), magnesium (Mg) and chloride (Cl), which are detrimental for the combustion of the press cake, were transferred into the press fluid, more than 0AE50 of calcium, which has positive combustion properties, remained in the press cake. Significantly (P < 0AE05) higher mass flows were detected at conditioning temperatures of 60°C (K and Mg) and 80°C (crude fibre and nitrogen-free extract) compared with the 5°C treatment. Because of the separation of solids and liquids, high proportions of P (0AE61-0AE74) and K (0AE64-0AE85) but only 0AE32-0AE45 of nitrogen exported from the grassland would be recycled with an application of the digestates from the anaerobic digestion of the press liquid.

An accurate estimation of biomass is needed to understand the spatio-temporal changes of forage resources in pasture ecosystems and to support grazing management decisions. A timely evaluation of biomass is challenging, as it requires efficient means such as technical sensing methods to assess numerous data and create continuous maps. In order to calibrate ultrasonic and spectral sensors, a field experiment with heterogeneous pastures continuously stocked by cows at three grazing intensities was conducted. Sensor data fusion by combining ultrasonic sward height (USH) with narrow band normalized difference spectral index (NDSI) (R 2 CV = 0.52) or simulated WorldView2 (WV2) (R 2 CV = 0.48) satellite broad bands increased the prediction accuracy significantly, compared to the exclusive use of USH or spectral measurements. Some combinations were even better than the use of the full hyperspectral information (R 2 CV = 0.48). Spectral regions related to plant water content were found to be of particular importance (996-1225 nm). Fusion of ultrasonic and spectral sensors is a promising approach to assess biomass even in heterogeneous pastures. However, the suggested technique may have limited usefulness in the second half of the growing season, due to an increasing abundance of senesced material.

Abbreviations: ADF, acid detergent fi ber; CP, crude protein; DM, dry matter; ME, metabolizable energy; MPLS, modifi ed partial least squares; NIRS, near-infrared spectroscopy; RPD, residual predictive value; SECV, standard error of cross-validation; SMLR, stepwise multiple linear regression; VI, vegetation index; 1-VR, coeffi cient of determination of cross-validation.

No abstract

No abstract

No abstract