We examined the efficiency of field work contractors engaged in harvesting rice in fragmented paddy fields. Field contractors work mostly in fields fragmented by the complexities of ownership, with different topologies, soil types, rice cultivars, and harvest dates. We investigated the factors of harvesting efficiency by recording the location, duration of field tasks with GPS recorders, weather conditions and so on. Using the empirical data collected, we tried to develop a baseline for efficiency, and examined factors involved in the efficiency of harvesting rice in fragmented fields. To estimate baselines for the duration of field activities, we incorporated some variables, such as task area, rainfall before harvest, workers’ skill levels, work hours, and average cluster size of fields into our linear regression models. Using multivariate analysis, we tested the relative effects of the factors on efficiency. We also propose a new index for fragmented fields. Our results show that efficiency can be improved by hiring skilled workers, clustering fields, expected length of hours in the fields and assigning more larger fields. Using our index of field fragmentation and the number of rainy days before harvest, we showed that fragmentation reduces efficiency. Our index provide better results than the traditionally used index for fragmented fields. We propose further management options, including field assignments and hiring highly skilled workers, to improve efficiency.
The objective of this study was to estimate the autumn and winter phenology and the length of the growing season of evergreen coniferous forests from MODIS GRVI (green-red ratio vegetation index) time-series data. We derived GRVI time-series data from MODIS 500 m reflectance data (MCD43 A4) for the period 2001–2012 from plantations of Japanese cedar (Cryptomeria japonica) and hinoki cypress (Chamaecyparis obtusa) in the Ashio mountainous region, Tochigi, Japan. By fitting logistic functions to the time-series data for each pixel, we detected four phenological transition dates: the onset of GRVI increase (OGI), the onset of GRVI maximum (OGMax), the onset of GRVI decrease (OGD), and the onset of GRVI minimum (OGMin). The length of the growing season for each pixel was calculated as the difference between the date of OGI and the date of OGMin. The averages of estimated dates of OGD and OGMin were DOY (day-of-year) 301 and DOY 378, respectively. These two dates were positively related to air temperature in autumn (OGD, r = 0.71) and air temperature in winter (OGMin, r = 0.86) for the 12 years. Using the statistical relationships with air temperature, we could estimate the interannual variations in the phenological transition dates in autumn and winter for evergreen coniferous forests from the satellite-monitored GRVI. The average growing season length was 287 days, or about 80% of a year. The results show that the changes in the growing season length were caused mainly by changes in the date of OGMin in winter.