16.精子形成のホルモン支配(第669回千葉医学会例会・第38回千葉泌尿器科集談会)

Understanding the temporal and spatial variability in a crop yield is viewed as one of the key steps in the implementation of precision agriculture practices. Therefore, a study on a center pivot irrigated 23.5 ha field in Saudi Arabia was conducted to assess the variability in alfalfa yield using Landsat-8 imagery and a hay yield monitor data. In addition, the study was designed to also explore the potential of predicting the alfalfa yield using vegetation indices. A calibrated yield monitor mounted on a large rectangular hay baler was used to measure the actual alfalfa yield for four alfalfa harvests performed in the period from October 2013 to May 2014. A total of 18 Landsat-8 images, representing different crop growth stages, were used to derive different vegetation indices (VIs). Data from the yield monitor was used to generate yield maps, which illustrated a definite spatial variation in alfalfa yield across the experimental field for the four studied harvests as indicated by the high spatial correlation values (0.75 to 0.97) and the low P-values (4.7E-103 to 8.9E-27). The yield monitor-measured alfalfa actual yield was compared to the predicted yield form the Vis. Results of the study showed that there was a correlation between actual and predicted yield. The highest correlations were observed between actual yield and the predicted using NIR reflectance, SAVI and NDVI with maximum correlation coefficients of 0.69, 0.68 and 0.63, respectively

Impact of raking and baling patterns on alfalfa hay dry matter and quality losses

A field study was conducted to investigate the impact of different patterns of raking and baling operations on the dry matter (DM) and quality losses of the produced alfalfa (Medicago sativa) hay. The experimental work was carried out on a 50 ha center pivot irrigated alfalfa field in a commercial farm located in the Eastern Region of Saudi Arabia. Raking operation was performed following two patterns corresponding to the direction of mowing operation, namely, Raking-I in the same direction of mowing (Mowing→  and Raking→) and Raking-II opposite to the direction of mowing (Mowing→ and Raking←). The baling operation; however, was performed following four patterns corresponding to the directions of both mowing and raking operations, namely, Baling-I (Mowing→, Raking→ and Baling→), Baling-II (Mowing→, Raking→ and Baling←), Baling-III (Mowing→, Raking← and Baling→) and Baling-IV (Mowing→, Raking← and Baling←). Results showed that cumulative DM losses in alfalfa hay yield of 30.93% occurred during the harvesting operations. Out of which, raking induced the most DM losses of 985.22 kg ha−1 (59.66% of the total DM losses and 17.35% of the total hay yield). However, the least DM losses were observed during the baling operation and were estimated at 175.81 kg ha−1 (10.22% of the total DM losses and 3.10% of the total hay yield). Raking opposite to the direction of mowing reduced the DM losses by 130.17 kg ha−1 (7.88% of the total DM losses and 2.29% of the total hay yield) compared to that with the direction of mowing. Results also indicated that out of the 21.04% losses in the total crude protein (CP) content of the produced alfalfa hay, 10.91% occurred during the raking operation. However, the baling operation induced the least amount of CP losses (only 2.32% of the total CP). Overall, the best results in terms of alfalfa hay quality and quantity losses were achieved with Baling-III, where the lowest DM losses (2.01% of the total hay yield) and the lowest CP losses (1.44% of the total CP) were recorded. Keywords: Alfalfa, Harvesting losses, Hay quality, Machinery pattern

Employment of GIS techniques to assess the long-term impact of tillage on the soil organic carbon of agricultural fields under hyper-arid conditions.

A study on six 50 ha agricultural fields was conducted to investigate the effect of conservation tillage practices on the long-term (1990-2016) changes in the soil organic carbon (SOC) content of the topsoil layers (0-10 cm) of agricultural fields. The experimental fields were selected from the 49 fields of the Tawdeehiya Arable Farm (TAF), located 200 kilometers southeast of Riyadh, the capital city of the Kingdom of Saudi Arabia. Data sets from laboratory determined SOC and the corresponding Landsat images generated vegetation indices, namely, the Normalized Difference Vegetation Index (NDVI) and the Bare Soil Index (BSI), were utilized for the prediction of SOC using multivariate regression techniques. Long-term changes in the SOC content of the experimental fields, as a result of different tillage practices, were also studied. The developed SOC prediction models exhibited high accuracy indicated by R2 values ranging from 0.73 to 0.85, RMSE values of 0.34 to 0.85 g kg-1 and P-values of less than 0.0001. The cross-validation results (R2 of 0.61-0.70, RMSE value of 0.34-0.85 g kg-1 and P-values of less than 0.0001) confirmed the high accuracy of the developed SOC prediction models. Results also revealed that the change in the SOC content was clearly associated with soil tillage practices. On the average, 76% of the all agricultural fields in the experimental farm showed a decrease of up to 24 g kg-1 in their SOC content after 10 years (1990-2000) of continuous conventional tillage practices. On the other hand, an average increase of up to 37 g kg-1 in the SOC content was observed in 88% of the studied fields at the end of the study period (2016), where conservation tillage was a continous and consistent practice in the experimental farm

Impact of water salinity levels on the spectral behavior and yield of tomatoes in hydroponics

In Saudi Arabia, irrigation water is mainly obtained from aquifers and is mostly characterized by moderate to high levels of salinity. Hence, detailed information on crop response to salinity is of great importance in order to optimize irrigation and fertilizer management. Therefore, this study was conducted to evaluate the effect of salinity and grafting on the spectral behavior and yield of three tomato (Solanum lycopersicum) cultivars (Valouro-RZ, Ghandowra-F1 and Feisty-Red) grown in a hydroponic glass greenhouse. Three salinity levels (2.5, 6.0 and 9.5 dS m−1) were used in this study. For the three tomato cultivars, the increase in salinity level was associated with an increase in the spectral reflectance in the Visible, Red-Edge and NIR regions of the electromagnetic spectrum. However, Valouro-RZ and Feisty-Red cultivars showed no significant differences in the spectral reflectance in the Visible range between salinity1 (2.5 dS m−1) and salinity2 (6.0 dS m−1). Slight reduction, but not significant, in the total fruit yield was recorded under salinity2 compared to salinity1 (2.5 dS m−1) for Valouro-RZ (6.76%) and Feisty-Red (6.79%). For salinity3 (9.5 dS m−1), significant reductions in total fruit yield were recorded compared to salinity1 (Valouro-RZ: 31.77%, Feisty-Red: 33.53%). Ghandowra-F1 cultivar was found to be the most salt tolerant among the studied tomato cultivars, where a significant reduction was observed in the total fruit yield only when the salinity increased from 2.5 to 6.0 dS m−1 (10.85%) and 9.5 dS m−1 (30.63%). Grafting on Maxifort rootstock showed neither a significant effect on the spectral response nor a significant yield improvement; hence, no positive change in salinity tolerance, due to grafting, of the studied tomato cultivars. Therefore, further research on rootstock and fruit variant combinations for the feasibility of grafting for salinity tolerance is necessary. The results of this study indicated that the Valouro-RZ and Feisty-Red tomato cultivars could be grown successfully under a hydroponic system using irrigation water of up to 6.0 dS m−1 salt concentration without sacrificing the total fruit yield while taste may improve

Danau Sentarum national park: the abandoned paradise

Danau (Lake) Sentarum is one the most unique wetlands in Asia, marked by outstanding biodiversity. This area was declared as a national park in 1999, covering 132,000 hectares. The park is located in the upper Kapuas district, which declared itself a conservation district in 2003. What’s the current condition of the park? Who manages and who owns it? And how’s the condition of the people who have been living in this area for hundreds of years? This movie is dedicated to the local people of Danau Sentarum and to those who are trying to save it. Establishment of the national park and the conservation district requires some supporting conditions to achieve the goals

Satellite-Based Determination of the Water Footprint of Carrots and Onions Grown in the Arid Climate of Saudi Arabia

Increasing demand for food, climate change, and other human interventions are leading to significant increases in water consumption by the agricultural sector. This requires rationalizing the water used for the production of agricultural crops through improved irrigation management practices. Therefore, this study aimed to estimate the water footprint (WF) of onion (Allium cepa L.) and carrot (Daucus carota) crops using the CROPWAT model and the SSEB (Simplified Surface Energy Balance) algorithm. Experiments were carried out at two center-pivot irrigated fields belonging to Tawdeehiya Commercial Farms in the southeastern region of the Riyadh governorate, Saudi Arabia. Individual bands and vegetation indices (VIs) were retrieved from Sentinel-2 satellite data, including the normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), optimized soil adjusted vegetation index (OSAVI), renormalized difference vegetation index (RDVI), and enhanced vegetation index (EVI), and the land surface temperatures (LST) extracted from Landsat-8 data were used to estimate crop productivity (CP), crop water use (CWU) (i.e., evapotranspiration—ETa), and crop WF. Crop growth/phenology stages and georeferenced biophysical parameters were recorded during the growth period, and crop yield samples were collected randomly from predetermined sampling locations. It was found that the NIR band was appropriate for predicting onion yield (R2 = 0.68; p > F = 0.02) and carrot yield (R2 = 0.77; p > F = 0.02). The results also showed the feasibility of using the RDVI and EVI to estimate the yields of onion and carrot crops, with bias values of 15% and –17%, respectively. The CWU has also been successfully estimated using the SSEB algorithm, with an overall accuracy of 89%. The SSEB-estimated CWU was relatively high compared to the applied amounts by 10.6% (onions) and 12.6% (carrots). Finally, the crop WF was successfully estimated at 312 m3 t−1 and 230 m3 t−1 for carrots and onions, respectively, with an overall accuracy of 71.11%. The outcomes of this study can serve as a reference for crop irrigation management practices in the study region and areas with similar environmental conditions

Estimation of gross primary production of irrigated maize using Landsat-8 imagery and Eddy Covariance data

A study was conducted to understand the potential of Landsat-8 in the estimation of gross primary production (GPP) and to quantify the productivity of maize crop cultivated under hyper-arid conditions of Saudi Arabia. The GPP of maize crop was estimated by using the Vegetation Photosynthesis Model (VPM) utilizing remote sensing data from Landsat-8 reflectance (GPPVPM) as well as the meteorological data provided by Eddy Covariance (EC) system (GPPEC), for the period from August to November 2015. Results revealed that the cumulative GPPEC for the entire growth period of maize crop was 1871 g C m−2. However, the cumulative GPP determined as a function of the enhanced vegetation index – EVI (GPPEVI) was 1979 g C m−2, and that determined as a function of the normalized difference vegetation index – NDVI (GPPNDVI) was 1754 g C m−2. These results indicated that the GPPEVI was significantly higher than the GPPEC (R2 = 0.96, P = 0.0241 and RMSE = 12.6%). While, the GPPNDVI was significantly lower than the GPPEC (R2 = 0.93, P = 0.0384 and RMSE = 19.7%). However, the recorded relative error between the GPPEC and both the GPPEVI and the GPPNDVI was −6.22% and 5.76%, respectively. These results demonstrated the potential of the landsat-8 driven VPM model for the estimation of GPP, which is relevant to the productivity and carbon fluxes