In this paper, nitrogen cycling of group farming systems by dairy and crop farmers was analyzed using compartment models for nitrogen flows in the systems. In particular, the effects of crop utilization on nitrogen cycling in the systems were examined by simulation. The following results were obtained from the analysis.1. There was a remarkable relationship between the number of cows per field area and nitrogen cycling in the systems.2. Nitrogen cycling was largely influenced by the ratio of paddy rice field to total faming land, feed crop yield and nitrogen losses in process of manure production in the systems.3. The nitrogen cycling and nitrogen use efficiency of group farming systems in Chugoku region were less than those of farming systems in overall Japan in spite of the fact that the number of cows per field area was smaller in the former systems. This result may be caused by larger paddy rice field and less crop yield in group faming systems in Chugoku region.

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In this study, we analyzed transition of CO₂ emission for 14 vegetables production during 1975 to 2003. The result shows that CO₂ emission was reduced from about 4 million ton-CO₂ in 1975 to 3 million ton-CO₂ in 1984. Especially, CO₂ emission for production of stem, leaf and root vegetable had decreased. This was because of the reduction of fertilizer consumption due to organic agriculture. On the other hand, CO₂ emission by fruit vegetable production was more than 40% in total CO₂ emission even though the production share was only 10%. Especially, CO₂ emission by tomato production had increased from the 1980's. This is because fuel, heat and power had consumed five times during 1975 to 1994. Eco-efficiency value, which is production per CO₂ emission (yen/kg-CO₂), in tomato production had decreased as CO₂ emission had increased. The eco-efficiency in that by greenhouse without oil heating, which was 600 yen/ kg-CO₂, was higher than that by greenhouse with heating. In addition, gross income of tomato production by greenhouse without heating was 102,000yen/ t- CO₂, which was 1.5 times as much as that by open field.

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In order to select suitable wavebands to predict crop variables, canopy reflectance and crop variables was regularly measured over rice canopy during growing season in Java Island, Indonesia. Two crop variables such as leaf area index (LAI) and SPAD values, three rice cultivars and four nitrogen levels were involved in this study. Optimal and paired-wavebands for predicting crop variables tested not only in traditional VIs (we used Reflectance Indices, RIs, instead of VIs in this paper to avoid confusion) such as NDRI (normalized difference reflectance index), also tested them in RRI (ratio reflectance index), RDRI (renormalized difference reflectance index) and SARI (soil adjusted reflectance index) with involving all possible waveband combinations to obtain best fitted two-pair waveband related to crop variables. A first derivative reflectance(FDR) spectrum was calculated and analysis from all possible paired-waveband combinations used in RIs was investigated with 6,786 combinations attributed to LAI and SPAD. The R² value of paired-wavebands used in RIs for LAI ranged from 0.883 to 0.908, and for SPAD ranged from 0.667 to 0.771. The significant relationships (R²) between optimal single band with crop variables achieved by red edge region wavebands (735 and 720 nm) of FDR attributed to LAI and SPAD respectively. The highest relationships between LAI and optimal paired-waveband combinations of near-infrared (835 nm) and red edge region (720 nm) obtained by RDRI (R² = 0.908), while for SPAD was red edge regions (715 and 710) attained by SARI （R² = 0.771）. Validation of measured and predictive value using FDR implied better accuracy to estimate LAI (R² = 0.859) than using reflectance data (R² = 0.797), meanwhile using either reflectance (R² = 0.709) or FDR (R² = 0.702) data to predict SPAD demonstrated close value. Furthermore, validation using SARI denoted the highest values for predicting LAI (R² = 0.852) and SPAD (R² = 0.658), respectively. These results giving fundamental information on monitoring growth progress and enable to estimate grain production. In addition this information provides recommendation for practical use in studying hyperspectral remote sensing for growth assessment, to support rice farming management for broader assessment using satellite.

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Hyperspectral canopy reflectance data were used for principle component regression (PCR) and partial least squares regression (PLSR) model to predict two crop variables: leaf area index (LAI) and SPAD value for 3 cultivars of rice, 4 levels of nitrogen supply in the northern Java, Indonesia. Coefficient of determination (R²), cross validated determination of coefficient (R²CV), root mean square error in prediction (RMSEP), root mean square error of cross validation (RMSECV), root mean square error entire calibration (RMSEC) of model calibration and validation were calculated for the model quality evaluation. In the present study the effectiveness and practicability of multivariate analysis methods of PCR and PLSR compared and tested over all single wavebands of reflectance and first derivative reflectance (FDR). The predictive capability of PLSR model demonstrated slightly better predictive capability than that of PCR model attributed to LAI and SPAD, indicated by rising of R² and reducing the number of latent variable (NLV) and RMSEC. The predictability (R²) of crop variables was improved using PLSR, particularly in LAI with employing single reflectance (R² = 0.956) and FDR (R²=0.956). Using PCR and PLSR improved the accuracy (R²) model when compared to using simple linear regression attributed to predict LAI and SPAD value which we have done previously. Validation of measured and predicted value using PLSR model implied better accuracy to predict LAI and SPAD than that of using PCR model.

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Winter wheat (Triticum aestivum L.) is an important rotation crop in southern Gifu prefecture, but rotational use of dried paddy fields often reduces grain quality. Consequently, grain quality varies greatly throughout a single area. This study is intended to evaluate in situ canopy reflectance to predict the total dry matter (DM), leaf area index (LAI), and leaf nitrogen concentration (N_conc). In a field experiment including four levels of N application, hyperspectral reflectance data (400–950 nm) were obtained for winter wheat during five growth stages. Band selection applied using a normalized difference vegetation index (NDVI) formula yielded the best-fitted two-pair waveband. Several paired bands were also indicated, which had higher R² values of DM (R² > 0.8), LAI (R² > 0.4), and N_conc (R² > 0.5) than those in typical red and near-infrared (NIR) based NDVI(R² = 0.39, 0.27, and 0.01 respectively in DM, LAI, and N_conc). Partial least squares (PLS) regression analyses using the best two-pair NDVI predictions improved the predictive accuracy for DM (R² = 0.93), LAI (R² = 0.80), and N_conc (R² = 0.83). Furthermore, the model using first derivative reflectance (FDR) more precisely predicted DM (R² = 0.95) and N_conc (R² = 0.88) than that using reflectance spectra. These findings suggest that PLS can provide better prediction than conventional methods using a single waveband or band combination. Strong correlations were found in regions with shorter wavelengths: green(525–570 nm), red edge (720–780 nm), and NIR (930–950 nm) regions to DM and LAI; and blue (425–490 nm) and red regions (645–685 nm) to N_conc. These results suggest that important wavelength regions are green, red edge, and NIR regions for DM and LAI, and blue and red regions for N_conc.

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The layer of soil from the surface to a depth of 1 m was monitored using a soil profile probe (Delta-T Devices Ltd. PR-1) at 76 points in the dry season from November 18, 2005 to February 8, 2006. The study sites comprised 2 small watersheds in the Khon Kaen province of Northeast Thailand. The dry season began from October 13 but there was a total of 30 mm of irregular rainfalls in early November. Rainfall greater than 1 mm was not observed during the monitoring period. The average soil-stored water was 132 mm at the beginning of December and 109 mm at the beginning of February. The decrease in soil moisture was 23 mm. This was much less than the potential evaporation of 324 mm during this period. The amount of soil moisture was different at each monitoring point. The effects of the topographical level of the watersheds were not clear for both the talweg and cross line. On the contrary, the type of vegetation affected the soil moisture. The moisture level in December was 25 mm in forest, 79 mm in fallow uplands (weeds), 96 mm in cassava fields, 131 mm in sugarcane fields, 147 mm in fallow paddy fields (weeds), and 163 mm in after-rice (little vegetation) fields. The decrease of the soil stored water between December and February was as follows: 3 mm in forest, 19 mm in fallow uplands (weeds), 30 mm in cassava fields, 26 mm in sugarcane fields, 28 mm in fallow paddy fields (weeds), and 18 mm in after-rice (little vegetation) fields. The soil moisture at each layer increased with depth. However, the soil moisture in forest was an exception; it decreased from the soil surface to a depth of 1 m. This implies that the available soil moisture had already evaporated by December. The 22 mm of soil moisture in the forest can be considered as unavailable water. In December, the available water in the after-rice (little vegetation) fields was estimated to be 141 mm. The transpiration coefficients are known to be approximately 100 for corn and 200~500 for general crops. Therefore, 141 mm of water can yield a maximum dry matter of 1.4 kg·m^–2 for corn and 0.7~0.3 kg·m^–2 of general crops.

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Total outflows of suspended solids (SS) and nutritive salts such as phosphorus from farmland as well as residential and industrial sectors are important parameters that have recently received more consideration in monitoring and managing the water quality of Japan’s rural watersheds. However, the pollution source is generally hard to specify because the source is spread widely across farming areas. To evaluate the environmental impact of agricultural activities on the water quality and resolve any problems, it is necessary to quantitatively measure the SS and nutritive salt concentrations of river water in conjunction with the dynamics of land use in the basin. This preliminary study used satellite images to estimate the acreage of major crops, and related the agricultural land use to the SS and total phosphorus (TP) concentrations and the river flow to develop a simple model for estimating the SS and TP loads per unit paddy area. We periodically measured the SS and TP concentrations and flow rates (m³/s) in four medium or small rivers located in Aichi Prefecture in central Japan. The measurements were carried out in May when puddling and rice transplanting are actively under way in the study area. Measurements were only taken when no heavy rain was recorded on the sampling day and previous day. The total area of the river basins in this study was approximately 3600 ha. We estimated the cultivated areas of paddy rice and winter wheat in each river basin during the study period using SPOT and RADARSAT images. The estimated average SS load per unit paddy area of the whole study area was 462 kg/ha in May 2005. The TP and SS loads were highly correlated (r=0.97, p<0.01). Due to the large variation in estimated SS loads (304 kg/ha – 710 kg/ha) between river basins adjacent to each other, further investigation is required to examine the relationship between the SS load and changes in land use in the area, such as recent widespread early-season cultivation and direct seeding of rice.

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