Utilization of Landsat-8 data for the estimation of carrot and maize crop water footprint under the arid climate of Saudi Arabia

Understanding the spatial variability of Water Footprint (WF) of crops is essential for the efficient use of the available water resources. Therefore, this study was designed to bridge the gap in knowledge existed in the area of WF in the arid climate of Saudi Arabia by quantifying the remote sensing based blue-WF (WFblue) of maize and carrot crops cultivated during the period from December 2015 to December 2016. Agrometeorological (empirical) estimated WF components, namely, the WFblue, the green-WF (WFgreen) and the grey-WF (WFgrey), were determined at a farm scale in conjunction with the climatic conditions and cropping patterns. On the other hand, the WFBlue was estimated from Landsat-8 data using energy balance and yield models. The empirical approach based WFBlue was used as a reference for the accuracy assessment of the Landsat-8 estimated WFBlue. The empirically estimated WF of silage maize ranged from 3540 m3 t-1 to 4960 m3 t-1. Out of which the WFgreen, the WFblue and the WFgrey composed 0.74%, 83.28% and 15.98%, respectively. For the carrot crop; however, the WF ranged between 2970 m3 t-1 and 5020 m3 t-1. Where, the WFgreen, the WFblue and the WFgrey represented 0.50%, 77.31% and 22.19%, respectively. Using Landsat-8 data, the WFblue was found to vary across the crops from 2552 m3 t-1 (silage maize) to 3010 m3 t-1 (carrot). Results also revealed a highly significant linear relationship between the empirical and the Landsat-8 derived WFBlue (R2 = 0.77, P>F = 0.001). The utility of Landsat-8 data in mapping WF showed reliable seasonal estimates, which can greatly enhance precision management practices of irrigation water

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

Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date

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

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

Performance of the METRIC model in estimating evapotranspiration fluxes over an irrigated field in Saudi Arabia using Landsat-8 images

Accurate estimation of evapotranspiration (ET) is essential for hydrological modeling and efficient crop water management in hyper-arid climates. In this study, we applied the METRIC algorithm on Landsat-8 images, acquired from June to October 2013, for the mapping of ET of a 50 ha center-pivot irrigated alfalfa field in the eastern region of Saudi Arabia. The METRIC-estimated energy balance components and ET were evaluated against the data provided by an eddy covariance (EC) flux tower installed in the field. Results indicated that the METRIC algorithm provided accurate ET estimates over the study area, with RMSE values of 0.13 and 4.15 mm d−1. The METRIC algorithm was observed to perform better in full canopy conditions compared to partial canopy conditions. On average, the METRIC algorithm overestimated the hourly ET by 6.6 % in comparison to the EC measurements; however, the daily ET was underestimated by 4.2 %

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

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