| Literature DB >> 35421175 |
Kota Mameno1, Takahiro Kubo2,3,4.
Abstract
The abandonment of irrigated paddy land has increased in Japan, which can cause a decline in food security and biodiversity. Despite the importance of individual decisions, most studies have only examined regional or community-based determinants of paddy land abandonment. This study aimed to uncover the socio-economic determinants affecting individual landowners' decisions to abandon paddy land, using Japanese agricultural census data (2005, 2010, and 2015) composed of over one million unique paddy landowners. Results showed that low agricultural benefits are a key driver of abandonment, similar to European countries. Conversely, there is a positive correlation between the population of full-time cultivators in a household and paddy land abandonment, which contradicts previous evidence. Although some mosaics of socio-ecological landscapes with high biodiversity formed through long-term human influence (i.e., the Satoyama landscapes) are less-favored agricultural areas, the paddy land in some of these landscapes tends not to be abandoned. These findings support effective policymaking that balances biodiversity conservation and the provision of agroecosystem services in semi-natural landscapes.Entities:
Mesh:
Year: 2022 PMID: 35421175 PMCID: PMC9009660 DOI: 10.1371/journal.pone.0266997
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Map of Japan indicating study site regions.
Japan is indicated in black and gray: Black shows where the data for the analysis were gathered; the gray areas show where the data were excluded, that is, Hokkaido, Tokyo, and Okinawa. This map was created based on Natural Earth data (http://www.naturalearthdata.com/).
The descriptions of the variables used in Tobit models.
| Variables | Definition | 2005 | 2010 | 2015 |
|---|---|---|---|---|
| Mean | ||||
|
| The area of paddy land abandonment (a) | 3.614 (15.02) | 3.165 (12.62) | 4.092 |
|
| The age of the landowner | 63.80 | 54.44 | 54.95 |
|
| The days that the landowner cultivates | 4.285 | 3.215 | 2.567 |
|
| The population of cultivators in a household | 3.593 | 2.371 | 1.766 |
|
| The population of full-time cultivators in a household | 0.2747 | 0.9980 | 0.7828 |
|
| The average age of each household | 56.38 | 58.60 | 60.67 |
|
| The rate of women in the household | 0.5029 | 0.4974 | 0.4876 |
|
| The population of people who are under 14 years old in a household | 0.4266 | 0.2455 | 0.1510 |
|
| The dummy variable: this is ‘1’, if the landowner has an heir | 0.4275 | 0.2784 | 0.1598 |
|
| The dummy variable: this is ‘1’, if the landowner mainly cultivates rice | 0.5645 | 0.3830 | 0.3051 |
|
| The dummy variable: this is ‘1’ if the landowner cultivates eco-friendly farming (decreasing chemical fertiliser) | 0.2678 | 0.2215 | 0.09911 |
|
| The number of agricultural machines the landowner has | 2.008 | 1.410 | 1.099 |
|
| The income from agricultural products (JPY) | 4.001 | 2.822 | 2.265 |
|
| The income from contract farming (JPY) | 1.056 | 0.7655 | 0.6143 |
|
| The dummy variable: this is ‘1’ if non-agricultural income is higher than agricultural income | 0.6259 | 0.4083 | 0.3028 |
|
| The dummy variable: this is ‘1’ if the landowner mainly ships products to consumers directly | 0.05760 | 0.06011 | 0.04008 |
|
| The dummy variable: this is ‘1’ if the landowner lives in the forestry village area where public services were provided insufficient (i.e., Forestry Areas) | 0.1543 | 0.1644 | 0.1625 |
|
| The dummy variable: this is ‘1’ if the landowner lives in the rural area which is mostly covered with steep area (i.e., Mountain Areas) | 0.3082 | 0.3264 | 0.3264 |
|
| The dummy variable: this is ‘1’ if the landowner lives in the depopulated area (i.e., Depopulated Areas) | 0.2378 | 0.2749 | 0.3258 |
|
| The dummy variable: this is ‘1’ if the landowner lives in Agriculture promotion area | 0.9642 (0.1858) | 0.9754 | 0.9784 |
Estimation results of the Tobit models.
| Variables | Model 1 | Model 2 | ||
|---|---|---|---|---|
| Coefficient | S. E. | Coefficient | S. E. | |
|
| -0.0760 | 4.42 × 10−3 | -0.0945 | 4.55 × 10−3 |
|
| 0.351 × 10−3
| 3.53 × 10−5 | 0.582 × 10−3
| 3.62 × 10−5 |
|
| -0.104 | 3.48 × 10−3 | -0.114 | 3.60 × 10−3 |
|
| -0.507 | 5.24 × 10−3 | -0.467 | 5.47 × 10−3 |
|
| 0.543 | 5.69 × 10−3 | 0.510 | 5.89 × 10−3 |
|
| -0.405 | 4.70 × 10−3 | -0.375 | 4.90 × 10−3 |
|
| 3.66 × 10−3
| 4.11 × 10−5 | 3.22 × 10−3
| 4.26 × 10−5 |
|
| -0.366 | 7.24 × 10−3 | -0.325 | 7.41 × 10−3 |
|
| -0.178 | 0.0336 | -0.0860 | 0.0342 |
|
| -1.11 | 0.0139 | -1.11 | 0.0142 |
|
| -0.247 | 0.0135 | -0.233 | 0.0138 |
|
| -1.25 | 0.0138 | -1.31 | 0.0141 |
|
| 0.0896 | 4.58 × 10−3 | 0.138 | 4.78 × 10−3 |
|
| -0.946 | 2.66 × 10−3 | -0.812 | 2.74 × 10−3 |
|
| -0.473 | 4.22 × 10−3 | -0.479 | 4.31 × 10−3 |
|
| 0.812 | 0.0155 | 0.748 | 0.0161 |
|
| 0.826 | 0.0264 | 0.812 | 0.0268 |
|
| −0.315 | 0.0222 | ||
|
| 4.86 | 0.0209 | ||
|
| 3.83 | 0.0165 | ||
|
| 0.695 | 0.0438 | ||
| (Intercept) | 1.71 | 0.173 | -2.45 | 0.181 |
| log∑ | 3.26 | 0.195 × 10−3 | 3.24 | 0.204 × 10−3 |
| log∑ | 2.42 | 0.463 × 10−5 | 2.42 | 0.481 × 10−5 |
| Observations | ||||
| Total | 4,770,108 | 4,592,658 | ||
| Zero-censored | 3,867,164 | 3,716,762 | ||
| Log-likelihood | -7637895 | -7377125 | ||
| Df | 20 | 24 | ||
***p < 0.001
**p < 0.01
*p < 0.05.