Journal of the Japanese Agricultural Systems Society
Online ISSN : 2189-0560
Print ISSN : 0913-7548
ISSN-L : 0913-7548
Volume 32 , Issue 4
Showing 1-2 articles out of 2 articles from the selected issue
Contributed paper
  • the case of Hunan province
    Yanrong LI
    2016 Volume 32 Issue 4 Pages 117-131
    Published: December 30, 2016
    Released: July 31, 2017

    We propose a development strategy for rural China that mixes agricultural land fluidity and cooperatives. We combine the two into a vertical organization framework and organize them from the perspective of investment in farmland. From several cases gathered in Hunan Province, we find that vertical integration can be organized into (1) full integration, (2) full cooperation integration, (3) partial cooperation integration, and (4) contract farming. In particular, we attempt to analyze the differences in investment in farmland under full cooperation integration compared to those under partial cooperation integration.In this study, first we consider why these systems are selected as agricultural produce procurement systems. We explain why full cooperation integration is selected when purchase procurement is the primary purpose of agricultural enterprises, and why partial cooperation integration is selected when procurement from directly operated farms is the primary purpose of agricultural enterprises. Second, we theoretically present the facts on how the differences in these procurement systems evoke differences in the allocation of investment in agricultural land. We explain the economically rational outcomes in two cases: one is in full cooperation integration pattern of agriculture where agricultural enterprises and farmers share investment; the second is in partial cooperation integration pattern of agriculture where only farmers take on the investment burden. Third, we theoretically demonstrate that the differences in these patterns may result in appropriate investment or underinvestment in agricultural land. We explain that appropriate investment is realized through both full integration and full cooperation integration.For rural development in China, a strategy of full cooperation integration that can properly use agricultural land can be proposed. However, in fact, the pattern of partial cooperation integration still exists although it results in underinvestment. To resolve this situation, one solution might be to legally regulate the amounts invested and ensure the availability of technology for agricultural enterprises as an entry condition for cooperatives. Moreover, in the initial stage of the establishment of cooperatives, while tolerating the existence of inefficiency in the form of underinvestment by agricultural enterprises, efforts should be made to change to full cooperation integration in conjunction with the success of the cooperatives. In rural China, a more specific strategy for rural development is required for the future that incorporates such organic relations in the system.

    Download PDF (902K)
    2016 Volume 32 Issue 4 Pages 133-145
    Published: December 30, 2016
    Released: July 31, 2017

    Forest areas in northern Thailand are endangered by wildfires. Fuel load is recognized as one of the important factors that influence wildfire occurrence and affect fire behavior. We compared the capabilities of seven vegetation indices (VIs) of Landsat satellite data in estimating leaf biomass, which is a parameter used in a leaf fuel load prediction model. The model contributes to the assessment of wildfire risk by identifying the spatial distribution of leaf fuel load to assess wildfire-prone areas across different landscapes. Significant relationships between the calculated standard leaf biomass and the seven VIs showed that a normalized difference vegetation index (NDVI) had the strongest relationship with leaf biomass. The NDVI images of normal and dry seasons (i.e., a seasonal NDVI) were used to estimate the quantities of seasonal leaf biomass and used to detect the missing leaf biomass or the leaf fuel load on the ground surface. The model of leaf fuel load prediction, based on the seasonal NDVI images, achieved accuracy of 80.43% (dipterocarp) and 71.36% (deciduous) using a statistical inference between the predicted and field-derived data. Moreover, model validation using a paired t-test indicated there was no significant difference between the means of the two data sets (p-value >0.05). Therefore, the predicted leaf fuel load derived from the developed model could be used as a substitute for estimating the actual leaf fuel load in forested areas, especially in dipterocarp and deciduous forests.

    Download PDF (858K)