Behavioral Decision of Employment for Rural Labors: Evidence from Peasant Households in Central China

Based on the facts of the peasant household and its labors, this paper analyses rational behavior strategy of the peasants and studies the microeconomic impact factors on the peasant behavior of employment with a discrete choice model. According to Econometric analysis, conclusions has been derived as follows: Firstly, the peasants behavior of employment is consistent with their wills to raise their income; Secondly, the peasants allocate labor resource in their peasant household on comparative advantages; Thirdly, non-agricultural job opportunity is a vital channel to release recessive unemployment and achieve sufficient employment of rural labors; Finally, peasants in different areas show slightly different tendency on obtaining non-agricultural jobs.rural labors, impact factors, employment choice,

Modeling ammonia emissions from dairy production systems in the United States

Dairy production systems are hot spots of ammonia (NH3) emission. However, there remains large uncertainty in quantifying and mitigating NH3 emissions from dairy farms due to the lack of both long-term field measurements and reliable methods for extrapolating these measurements. In this study, a process-based biogeochemical model, Manure-DNDC, was tested against measurements of NH3 fluxes from five barns and one lagoon in four dairy farms over a range of environmental conditions and management practices in the United States. Results from the validation tests indicate that the magnitudes and seasonal patterns of NH3 fluxes simulated by Manure-DNDC were in agreement with the observations across the sites. The model was then applied to assess impacts of alternative management practices on NH3 emissions at the farm scale. The alternatives included reduction of crude protein content in feed, replacement of scraping with flushing for removal of manure from barn, lagoon coverage, increase in frequency for removal of slurry from lagoon, and replacement of surface spreading with incorporation for manure land application. The simulations demonstrate that: (a) all the tested alternative management practices decreased the NH3 emissions although the efficiency of mitigation varied; (b) a change of management in an upstream facility affected the NH3 emissions from all downstream facilities; and (c) an optimized strategy by combining the alternative practices on feed, manure removal, manure storage, and land application could reduce the farm-scale NH3 emission by up to 50%. The results from this study may provide useful information for mitigating NH3 emissions from dairy production systems and emphasize the necessity of whole-farm perspectives on the assessment of potential technical options for NH3 mitigation. This study also demonstrates the potential of utilizing process-based models, such as Manure-DNDC, to quantify and mitigate NH3 emissions from dairy farms

Pushing towards the Limit of Sampling Rate: Adaptive Chasing Sampling

Measurement samples are often taken in various monitoring applications. To reduce the sensing cost, it is desirable to achieve better sensing quality while using fewer samples. Compressive Sensing (CS) technique finds its role when the signal to be sampled meets certain sparsity requirements. In this paper we investigate the possibility and basic techniques that could further reduce the number of samples involved in conventional CS theory by exploiting learning-based non-uniform adaptive sampling. Based on a typical signal sensing application, we illustrate and evaluate the performance of two of our algorithms, Individual Chasing and Centroid Chasing, for signals of different distribution features. Our proposed learning-based adaptive sampling schemes complement existing efforts in CS fields and do not depend on any specific signal reconstruction technique. Compared to conventional sparse sampling methods, the simulation results demonstrate that our algorithms allow $46\%$ less number of samples for accurate signal reconstruction and achieve up to $57\%$ smaller signal reconstruction error under the same noise condition.Comment: 9 pages, IEEE MASS 201