Feeding value of urea molasses-treated wheat straw ensiled with fresh cattle manure for growing crossbred cattle calves     

Sarwar M  Shahzad MA  Nisa MU  Afzal D  Sharif M  Saddiqi HA 《Tropical animal health and production》2011,43(3):543-548

The study was carried out to evaluate the influence of urea plus molasses-treated wheat straw (WS) ensiled with cattle manure (CM) on nutrients intake, their digestibilities, and growth performance of crossbred (Sahiwal × Holstein Friesian) cattle calves. The CM was mixed with ground WS in a ratio of 30:70 on dry matter (DM) basis. The WS–CM mixture treated with urea (4% DM) and molasses (4% DM) was allowed to ferment for 40 days in a cemented pit. Four iso-nitrogenous and iso-energetic fermented wheat straw (FWS)-based experimental diets were formulated. The FWS0, FWS20, FWS30, and FWS40 diets contained 0%, 20%, 30%, and 40% FWS, respectively. Twenty calves (9–10 months of age) were randomly allocated to four dietary treatments in a randomized complete block design, five in each group. Increasing trends for DM, organic matter, crude protein, and neutral detergent fiber intakes by calves were observed with increasing dietary FWS level. Weight gain was significantly different among calves fed different levels of FWS. The highest weight gain (491.8 g/day) was observed in calves fed FWS40 diet, while calves fed FWS0 and FWS20 diets gained 350.0 and 449.6 g/day, respectively. The results from this study imply that the FWS can be added up to 30% in the diet of growing crossbred calves without any detrimental effect on their performance.  相似文献   

Sensor data fusion to predict multiple soil properties     

Hafiz S. Mahmood  Willem B. Hoogmoed  Eldert J. van Henten 《Precision Agriculture》2012,13(6):628-645

The accuracy of a single sensor is often low because all proximal soil sensors respond to more than one soil property of interest. Sensor data fusion can potentially overcome this inability of a single sensor and can best extract useful and complementary information from multiple sensors or sources. In this study, a data fusion was performed of a Vis?CNIR spectrometer and an EM38 sensor for multiple soil properties. Stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and principal components analysis combined with stepwise multiple linear regression (PCA?+?SMLR) methods were used in three different fields. Soil properties investigated for data fusion included soil texture (clay, silt and sand), EC, pH, total organic carbon (TOC), total nitrogen (TN) and carbon to nitrogen ratio (CN). It was found that soil property models based on fusion methods significantly improved the accuracy of predictions of soil properties measureable by both sensors, such as clay, silt, sand, EC and pH from those based on either of the individual sensors. The accuracy of predictions of TOC, TN and CN was also improved in some cases, but was not consistent in all fields. Among data fusion methods, PLSR outperformed both SMLR and PCA?+?SMLR methods because it proved to have a better ability to deal with the multi-collinearity among the predictor variables of both sensors. The best data fusion results were found in a clayey field and the worst in a sandy field. It is concluded that sensor data fusion can enhance the quality of soil sensing in precision agriculture once a proper set of sensors has been selected for fusion to estimate desired soil properties. More efficient statistical data analysis methods are needed to handle a large volume of data effectively from multiple sensors for sensor data fusion.  相似文献   

Agroforestry Systems as Alternative Land-Use Options in the Arid Zone of Thal,Pakistan     

Muhammad Khan  Hafiz Zahid Mahmood  Ghulam Abbas  Christos A. Damalas 《Small-Scale Forestry》2017,16(4):553-569

Agroforestry offers unique opportunities for increasing biodiversity, preventing land degradation, and alleviating poverty, particularly in developing countries, but factors explaining the adoption by farmers are not well understood. A survey of 524 farm households was conducted in Bhakkar district of Punjab, Pakistan to study factors that determine the adoption of agroforestry on the sand dunes in the resource-deficient region of Thal. Two types of agroforestry systems were studied: intercropping and border cropping (also known as boundary or perimeter planting). Both agroforestry systems included irrigated cultivation of the timber trees Eucalyptus camaldulensis (local name: sufeda) and Tamarix aphylla (local name: sars) with wheat, chickpeas (Cicer arietinum) (local name: chana) or cluster beans (Cyamous tetragocalobe) (local name: guars). The majority of the farmers was in favour of intercropping and border cropping. Most farmers reported the protection of nearby crops from dust storms as the most important positive perception about both agroforestry systems. Age, education, and farm to market distance were significant determinants of agroforestry adoption. Older and less-educated farmers, with farms closer to markets were less likely to adopt tree planting or border cropping in Thal. In general, the agroforestry systems examined were more likely to be adopted by farmers who can wait 3–4 years for harvesting crop outputs, but not by poorer farmers who are totally dependent on subsistence agriculture and cannot afford the high initial cost of agroforestry establishment, nor can they wait for crop output for extended periods. Furthermore, the adoption of both agroforestry systems was more likely in remote marginal areas than in areas close to markets. To increase agroforestry adoption rates, government policies should strengthen farmers’ knowledge of every stage of agroforestry through extension services, focusing particularly among the prime prospects, i.e. farmers who will be most likely to adopt agroforestry. Once the prime prospects have adopted it, the older, less-educated, and poor farmers of the rural population can be also focused on to motivate adoption.  相似文献