Multiple Regression Relationships Research Articles

Estimation of soil health in the semi‑arid regions of northwestern Iran using digital elevation model and remote sensing data.

Nowadays, neglecting soil conservation issues is one of the most critical factors in reducing soil health (SH). In this regard, to facilitate the estimation of the SH in northwestern Iran, 292 soil samples were taken from a depth of 0-30cm of this area, and a wide range of soil properties were determined. Then, soil health indices (SHIs) were calculated. Simultaneously, the normalized difference vegetation index (NDVI), surface water capacity index (SWCI), and a digital elevation model (DEM) were obtained from satellite data. Finally, multiple linear regression (MLR) relationships between these parameters and SHIs were calculated. In this study, there was a highest significant positive correlation (P < 0.01) between IHI-LTDS and SWCI (0.71**), DEM (0.76**), and NDVI (0.73**). The MLR, with both the whole total (TDS) and minimal (MDS) dataset methods, which includes the aforementioned indices, strongly described the spatial variability of the Integrated Soil Health Index (IHI) (R2 = 0.78, AIC = - 416, RMSE = 0.05, and ρc = 0.76). According to the results of this study, it can be said that the development of SH estimation models using remote sensing extracted parameters can be one of the effective ways to reduce the cost and time of soil sampling in extensive areas.

How We Survive The Covid-19 Pandemic? Perspectives of Food Hawkers’ Businesses Performances

Malaysia is one of the many countries affected by COVID-19, socially and economically. One of the sectors most affected was the food hawkers. Thus, this study measured the food hawker business thriving efforts during the pandemic. Specifically, the study’s objectives are to determine the Impact of a movement control order on food hawker businesses’ performances and determine the moderating effect of digitalization on food hawker businesses’ performances. Two hundred and twenty-two (222) data were collected from food hawkers in Selangor, Kedah, and Terengganu between February and March 2021. At the time, Malaysia was under movement control order (MCO) 2.0. Thus, data were collected online via google form and distributed to businesses through the Whatsapp application. The data were then analyzed using SPSS v24 for descriptive analysis and multiple regression relationships. It was found that there is a relationship between MCO and food hawker business with a standard beta coefficient of 0.836. Digitalization strongly impacts food hawker businesses with a 0.886 standard beta coefficient. Even though food businesses were severely affected during MCO, the digitalization movement impacted sustaining the businesses.

Statistical analysis and degradation pathway modeling of photovoltaic minimodules with varied packaging strategies

Degradation pathway models constructed using network structural equation modeling (netSEM) are used to study degradation modes and pathways active in photovoltaic (PV) system variants in exposure conditions of high humidity and temperature. This data-driven modeling technique enables the exploration of simultaneous pairwise and multiple regression relationships between variables in which several degradation modes are active in specific variants and exposure conditions. Durable and degrading variants are identified from the netSEM degradation mechanisms and pathways, along with potential ways to mitigate these pathways. A combination of domain knowledge and netSEM modeling shows that corrosion is the primary cause of the power loss in these glass/backsheet PV minimodules. We show successful implementation of netSEM to elucidate the relationships between variables in PV systems and predict a specific service lifetime. The results from pairwise relationships and multiple regression show consistency. This work presents a greater opportunity to be expanded to other materials systems.

Utility of Ultrasonic Pulse Velocity for Estimating the Overall Mechanical Behavior of Recycled Aggregate Self-Compacting Concrete

Ultrasonic pulse velocity (UPV) is a non-destructive measurement technique with which the quality of any concrete element can be evaluated. It provides information on concrete health and for assessing the need for repair in a straightforward manner. In this paper, the relationship is studied between UPV readings and the mechanical behavior of self-compacting concrete (SCC) containing coarse, fine, and/or powdery RA. To do so, correlations and simple- and multiple-regression relationships between compressive strength, modulus of elasticity, splitting tensile strength, flexural strength, and UPV readings of nine SCC mixes were assessed. The correlations showed that the relationship of UPV with any mechanical property was fundamentally monotonic. The inverse square-root model was therefore the best-fitting simple-regression model for all the mechanical properties, although for bending-tensile-behavior-related properties (splitting tensile strength and flexural strength) the estimation accuracy was much lower than for compressive-behavior-related properties (compressive strength and modulus of elasticity). Linear-combination multiple-regression models showed that the properties related to bending-tensile behavior had a minimal influence on the UPV value, and that their introduction resulted in a decreased estimation accuracy. Thus, the multiple-regression models with the best fits were those that linked the compressive-behavior-related properties to the UPV readings. This therefore enables the estimation of the modulus of elasticity when the UPV and compressive strength are known with a deviation of less than ±20% in 87% of the SCC mixes reported in other studies available in the literature.

Regionalization of evapotranspiration in India using fuzzy dynamic clustering approach. Part 2: Applications of regions

AbstractAccurate estimation of ET0, and determination of its trend/variability and sensitivity to changes in climate variables is essential at regional scale for different applications. These are attempted for India by considering 18 homogeneous ET0 regions formed in companion paper. Food and Agriculture Organization (FAO) recommends Penman–Monteith (PM) equation for ET0 estimation, which requires information on several climate variables. The equation cannot be used in data‐sparse areas where information/forecasts on one or more required climate variables is unavailable/unreliable. To address this, relevance vector regression (RVR) relationships are developed in this paper for the 18 homogeneous regions to arrive at FAO‐PM estimate of ET0 from subsets of its predictor climate variables, which could be typically expected in data‐sparse areas. The developed relationships are shown to be better in arriving at ET0 estimates when compared to multiple linear regression (MLR) relationships and three widely used empirical equations (Hargreaves, Mcguinness–Bordne, Priestly–Taylor). Regional trend analysis revealed that ET0 is significantly decreasing (increasing) in most regions located in north (south) India. Sensitivity of ET0 and surface runoff to changes in their predictor climate variables is determined for each region by considering third‐order Taylor series approximation of their functional relationships. Key climate variable(s) that govern ET0 changes in each region are identified. Results indicate that at annual scale solar radiation and relative humidity govern ET0 changes in regions located in south and north‐east India, whereas wind speed followed by relative humidity mostly influence the ET0 changes over other regions. Existence of evaporation paradox in four regions and validity of Bouchet's complementary relationship between ET0, actual evapotranspiration (ETa), and precipitation in 11 regions is also established. Significant divergence in trend of ET0 and ETa was evident in north‐east India.

THE THREE-DIMENSIONAL MOVEMENT CORRELATIONS BETWEEN ELBOW AND WRIST JOINT AND ANTHROPOMETRIC DETERMINANTS

This study aimed to investigate three-dimensional (3D) kinematic characteristics of elbow and wrist motions, the relationship between them, and the anthropometric factors affecting them. Using motion capture system, this study measured and calculated the 3D angles of elbow flexion/extension, elbow pronation/supination, wrist flexion/extension, and wrist adduction/abduction of 40 healthy young adults. The study measured nine anthropometric variables and used unpaired [Formula: see text]-tests to assess gender difference. Also, bivariate correlation tests and step-wise multiple regression analyses were performed between joint ranges and anthropometric variables, as well as different joint motions. Results showed two opposite patterns occurred during elbow flexion/extension. The study found a correlation between the range of elbow flexion/extension and the range of elbow pronation/supination that occurred during elbow flexion/extension. Additionally, the study tested joint correlations between the four joint motions. Finally, the study established bivariate and multiple regression relationships between range of elbow motions and anthropometric factors. This research presented an unrecognized pattern of 3D elbow flexion/extension, and associations between various anthropometric factors and different joint motions. These findings can contribute to the design of orthosis of upper extremities and the rehabilitation of joint mobility.

Prediction of primary salinity, sodicity and alkalinity in South African soils

Several attempts have in the past been made to map the primary salinity, sodicity and alkalinity of South African soils. These have typically been based on personal experience and/or limited data and also failed to quantify the accuracy of prediction. This paper therefore attempted to map these variables from soil profile data, and 1:1 000 000 topographical, geological and mineral maps. In the end 58% of the variability in soil electrical conductivity in the <550 mm rainfall class and 39% of the variability in the >550 mm rainfall class could be explained. Coefficients of determination for exchangeable sodium percentage were 85% in the <550 mm rainfall class and 52% in the >550 mm rainfall class. Multiple linear regression relationships were unable to predict pHwater, indicating that log-transformation of pHwater values are needed. Of the 122 × 106 ha land in South Africa, only 23% were calculated to be slightly saline, 5.1% saline, 1.4% moderately saline, 0.4% strongly saline; 3.8% non-alkaline saline-sodic, 6.3% alkaline saline-sodic and only 0.4% sodic.

PHYSICAL AND PHYSIOLOGICAL CHARACTERISTICS OF POTATO TUBERS RELATED TO STORAGE CONDITIONS

The aim of this investigation is to study the effect of traditional and cold storage condition on physiological and physical characteristics of Lady Rosetta potato tuber variety, as one of the most important commodity for chips industrials and for exporting, but it is very sensitive for handling and storage. Half ton of fresh tubers of each of two soils cultivar (Sandy and Black soil) were cleaned and the deteriorate tubers were discarded. The tubers were graded into three sizes based on its mass. Potato tubers were cured, then the potatoes of each cultivar were divided into two samples each of 250 kg for long term storage (200 days) under two different conditions, Nawalla (aerated natural traditional condition), and under cooling condition (8C and relative humidity of 85 %).The results data of fresh and stored potatoes of different storage treatments including physiological and physical characteristics demonstrated graphically and statistical analysis conducted. The physiological measurements are moisture content, sugar content and respiration rate. The physical characteristics measurements are including dimensions, surface area, volume, spherecity, mass, particle and bulk density. Analysis of the results obtained showed that there are linear multiple regression relationships between each of mass loss %, shrinkage of tuber dimensions %, shrinkage of volumes %, particle and bulk density at each of storage condition, storage periods, tubers mass and static load stress for each of sandy and black soil cultivars. Respiration rate (mg CO2/kg.h) of fresh tubers was directly proportion with tuber specific surface area and inversely proportion with tubers mass for all cultivars. Sugar content (Brix) positively affected by each of type of soil, storage temperature, static load stress, storage time and tuber mass.

Field level digital mapping of soil mineralogy using proximal and remote‐sensed data

AbstractPrimary (e.g., quartz) and secondary (clay) minerals are key factors determining the physical and chemical characteristics of soil. Understanding spatial distribution of minerals at the field scale would, therefore, be of potential benefit for soil management. However, current analysis requires time‐consuming laboratory procedures and computational quantification analysis (e.g., SIROQUANT). Furthermore, mineral composition (e.g., quartz, kaolinite, illite and expandable clay minerals) must sum to 100. We aimed to add value to laboratory data by developing multiple linear regression (MLR) relationships between mineralogy and ancillary data such as digital numbers (DNs) (i.e., Red [R], Green [G] and Blue [B]) acquired from remotely sensed air‐photographs and soil apparent electrical conductivity (ECa – mS/m) measured from proximal sensing electromagnetic (EM) instruments (i.e., EM38 and EM31). To account for composition, we compare results from the MLR approach with those from additive log‐ratio (ALR) transformation of mineralogy prior to MLR modelling. This approach together with various ancillary data and trend surface parameters (i.e., scaled Easting and Northing) has greater precision and less bias of prediction than the MLR approach using untransformed data. Our approach also enables predictions to sum to 100. We conclude that the most useful ancillary data to predict the abundance of quartz, kaolinite and illite are B DNs and EM31, while expandable clays are best predicted with R DNs, EM38 and scaled Northing. The use of ancillary data to map mineralogical components combined with ALR‐MLR is an effective approach, with resulting maps providing insights into soil and water management issues consistent with farmer experience.

Simple and multiple linear regressions between oil palm annual yields and yearly climatic variables over a 23-year-period (1990-2012) in the coastal zone of Cameroon

This study was aimed at determining whether variations in oil palm annual yields were significantly influenced by years of production, and at establishing if so simple and multiple linear regression relationships between oil palm annual yields and yearly climatic variables. Climatic and yield data were gathered in three locations (Bota-Limbe, Dizangué and Kienké) of the coastal zone of Cameroon, within the oil palm estates of three agro-industrial corporations. Yearly climatic variables were recorded for a period of 23 consecutive years (1990-2012); oil palm annual yields of five plots in each location were also recorded for the same period. Variations in annual yields (in metric-tons of bunches per hectare) were significantly influenced (P˂0.05) by years of production. Only a few yearly climatic variables however were significantly correlated with annual yields (none of the climatic variables in Bota-Limbe; evapotranspiration and rainfall height in Dizangué and insolation in Kienké). Low but significant, simple and multiple linear regressions were established that contributed to explain 18.10 to 54.60% of annual yields. Other mathematical relationships for more accurate predictions must be built, as for example curvilinear regressions; for linear regressions may not be sufficient to explain annual yields in their completeness.© 2015 International Formulae Group. All rights reserved.Keywords: Evapotranspiration, insolation, rainfall height, variations, predictions, mathematical relationships

Modeling the transfer of arsenic from soil to carrot (Daucus carota L.)--a greenhouse and field-based study.

Reliable empirical models describing arsenic (As) transfer in soil-plant systems are needed to estimate the human As burden from dietary intake. A greenhouse experiment was conducted in parallel with a field trial located at three sites through China to develop and validate soil-plant transfer models to predict As concentrations in carrot (Daucus carota L.). Stepwise multiple linear regression relationships were based on soil properties and the pseudo total (aqua regia) or available (0.5 M NaHCO3) soil As fractions. Carrot As contents were best predicted by the pseudo total soil As concentrations in combination with soil pH and Fe oxide, with the percentage of variation explained being up to 70 %. The constructed prediction model was further validated and improved to avoid overprotection using data from the field trial. The final obtained model is of great practical relevance to the prediction of As uptake under field conditions.

基于茎干直径微变化制定苹果灌溉制度的方法及可行性

PDF HTML阅读 XML下载 导出引用 引用提醒 基于茎干直径微变化制定苹果灌溉制度 DOI: 10.5846/stxb201311132724 作者: 作者单位: 南京信息工程大学,中国农业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金资助项目(51321001, 91425302,51309132); 江苏省农业气象重点实验室开放基金(S5312041001); 江苏省高校自然科学研究面上项目(13KJB170015); 江苏省基础研究计划青年基金项目(BK20130995) Assessment of trunk diameter fluctuation for irrigation schedule in apple trees Author: Affiliation: Nanjing University of Information Science and Technology,China Agricultural University Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:茎干直径的动态微变化是研究植物体水分和生长状况的重要指标。利用测树器监测西北旱区盛果期苹果树茎干直径微变化规律,根据监测记录获得茎干直径日最大值(MXTD)、茎干直径日最大收缩量(MDS)数据,并探讨茎干直径微变化规律及其对环境因素的响应,为茎干直径微变化用于指导精确灌溉提供科学依据。实验结果表明,晴天或多云天气下,苹果树茎干直径在每天的7:00或8:00时刻达到一天最大值,在16:00左右达到一天的最小值,茎干直径年增长量与果实产量成反比例关系。整个生育期MXTD呈先快速增加后平稳的变化特征。2010年MDS与茎干水势(φstem)呈显著负相关关系(r2=0.76* * *, n=14),这表明MDS可以反映苹果树的水势状况。生育后期的MDS对环境因素响应比生育前期敏感,全生育期MDS与气象因素的决定系数大小顺序为日最大水汽压差(VPDmax)>日最高温度(Tmax)>净辐射(Rn)。茎干直径微变化规律可以反映西北旱区盛果期果树的水分状况,可以为果园灌溉制度的确定提供科学依据。 Abstract:Research on agricultural water consumption in northwest China is crucial to relieve problems associated with the water crisis. Apples are a typical crop in China that have been used in agriculture for centuries. As production increases for economic growth, it is essential to analyze water transport mechanisms and water accessibility to mature apple orchards in arid regions. Trunk diameter fluctuation is an effective indicator of the water condition of plants. Its role as an indicator of plant water conditions thus emphasizes that it is crucial to design an irrigation schedule. The changes in the trunk diameter of mature apple trees (Malus domestica Borkh. cv Golden Delicious) was monitored using dendrometers at the Shiyanghe Experimental Station for Water-saving in Agriculture and Ecology of China Agricultural University (37°52'N, 102°51'E, altitude 1581 m). Stem water potential was measured using a pressure chamber. Wind speed, net radiation, relative humidity, and air temperature were monitored by an automatic meteorological station, and soil moisture was measured every 5 days using Time-Domain Reflectometry (TDR) methods based on the previously described data analysis. Change in the maximum daily trunk diameter (MXTD) and maximum daily diameter shrinkage (MDS), as well as their response to environmental factors were also monitored. The results showed that MXTD occurred at 7:00 to 8:00 in the morning and minimum of daily trunk diameter (MNTD) occurred at approximately 16:00 during sunny or cloudy days in 2008 on a daily scale. The trunk diameter increased at night and decreased during the day, which was meanly depended on reference evapotranspiration. MXTD increased during bud development, flowering, and leaf expansion periods (stage I) and remained constant during the fruit expanding and maturing periods (stage II), which was closely related to the MDS of the apple tree. Higher fruit yields were associated with smaller fruit stem diameter growth. MDS increased at the beginning and then gradually decreased during the entire growing stage. The relationship between MDS and stem water potential was linear and the determination coefficient was 0.76* * *. Consequently, MDS indicated the water status of the mature apple trees. However, MDS was more responsive to net radiation, reference evapotranspiration, vapor pressure deficit, and air temperature at stage II than at stage I because the canopy structure was not developed, and the water stored in the apple trees less frequently fluctuated during stage I. The order of determination coefficient over the whole growing stage was as follows: maximum vapor pressure deficit > maximum air temperature > net radiation. Multiple regression relationships among MDS and environmental factors can be used to calculate the MDS for well water supplied to apple trees, which could be considered as a reference value when the tree requires irrigation. Thus, the fluctuation regularities of tree trunk diameter could reflect the water status of the entire fruit period of fruit trees situated in arid areas of Northwest China and help improve orchard water management, as well as ensure the normal growth of fruit trees. 参考文献 相似文献 引证文献

Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

Abstract. The objective of this paper is to test the relationships between Above Ground Biomass (AGB) and remotely sensed vegetation indices for AGB assessments in the Kimberley area in Western Australia. For 19 different sites, vegetation indices were derived from eight Landsat ETM+ scenes over a period of two years (2011–2013). The sites were divided into three groups (Open plains, Bunch grasses and Spinifex) based on similarities in dominant vegetation types. Dry and green biomass fractions were measured at these sites. Single and multiple regression relationships between vegetation indices and green and total AGB were calibrated and validated using a "leave site out" cross validation. Four tests were compared: (1) relationships between AGB and vegetation indices combining all sites; (2) separate relationships per site group; (3) multiple regressions including selected vegetation indices per site group; and (4) as in 3 but including rainfall and elevation data. Results indicate that relationships based on single vegetation indices are moderately accurate for green biomass in wide open plains covered with annual grasses. The cross-validation results for green AGB improved for a combination of indices for the Open plains and Bunch grasses sites, but not for Spinifex sites. When rainfall and elevation data are included, cross validation improved slightly with a Q2 of 0.49–0.72 for Open plains and Bunch grasses sites respectively. Cross validation results for total AGB were moderately accurate (Q2 of 0.41) for Open plains but weak or absent for other site groups despite good calibration results, indicating strong influence of site-specific factors.

Designing of plug-in for estimation of coal proximate parameters using statistical analysis and coal seam correlation

This paper aims to design plug-in at matrix laboratory (MATLAB) for estimation of coal proximate parameters and coal seam correlation from well logs. Five major sub-functions such as: normalisation of single point resistance log, shifting of logs for depth matching, lithology marking, calibration for estimation of coal proximate parameters and coal seam correlation are included with graphical user interfaces. The geophysical log data of Jharia and Bishrampur coalfields, India are calibrated visually with the laboratory analysed data where recovery is considerable for a certain number of scattered boreholes designated as master boreholes. Multiple linear regression relationships are obtained between geophysical logging parameters: density, natural gamma ray, normalised single point resistance and coal proximate parameters: ash, moisture content and fixed carbon. The software then proceeds towards the estimation of ash, moisture and fixed carbon (band by band) for the other boreholes except master boreholes. In the present study the coal seams are correlated from total 5 and 15 numbers of exploratory wells in Jharia and Bishrampur coalfields respectively. The correlation is made and accordingly correlation table is prepared using graphic display mode of the software by matching the lithology of all the boreholes and correlation plot is generated during the process.

Chemical and physical predictors of the nutritive value of wheat in broiler diets.

The aim of this study was to establish relationships between chemical and physical parameters of wheat with performance and digestibilities of feed components in broiler chickens fed on wheat-based diets. Ninety-four wheat samples were selected for inclusion in four bird trials. Birds were housed in individual wire metabolism cages from 7 to 28 d and offered water and feed ad libitum. Dry matter intake (DMI), liveweight gain (LWG) and gain:feed were measured weekly. A balance collection was carried out from 14 to 21 d for determination of apparent metabolizable energy (AME), ME:gain, dry matter retention, oil and neutral detergent fibre (NDF) digestibility. At 28 d the birds were humanely killed, the contents of the jejunum removed for determination of in vivo viscosity and the contents of the ileum removed for determination of ileal dry matter, starch and protein digestibility. When wheat parameters were correlated with bird performance data, it was found that specific weight was not significantly (p>0.05) related to bird performance. Bird DMI, LWG and gain:feed were best correlated (p<0.05) with the rate of starch digestion, although the coefficients of correlation (r) were still low (0.246 to 0.523). A negative relationship (p<0.01) between AME and total (r = −0.432) and soluble (r = −0.304) non starch polysaccharide (NSP) was observed in this study. Thousand grain weight (TG) was positively correlated with DMI (r = 0.299), LWG (r = 0.343) and gain:feed (r = 0.371). When establishing multiple regression relationships, correlation coefficients greater than 0.8 were achieved for DMI, LWG, gain:feed and ileal crude protein digestibility. However, the economics involved in determining the parameters involved in the regressions make the process impractical.

Geogene Versus Anthropogene Origin of Trace Metals in Sediments in Cua Luc Estuary and Ha Long Bay, Vietnam

In the present study, the geochemistry of 49 surficial-bed and 101 core sediment samples is investigated to clarify the origin of trace metals in the Cua Luc Estuary and Ha Long Bay, which is a famous World Natural Heritage Site in Vietnam. Moreover, the potential mobility of trace metals is also assessed and their relationship with reference elements (Al, Fe, and Ca) and organic matter is established in order to make recommendations for sediment management and monitor future pollution. Generally, trace metals display higher concentrations in Ha Long Bay compared to the Cua Luc Estuary. However, this is controlled by the distribution of the fine (clay + silt) fraction, and hence the concentrations of Al, Fe, Ca, and organic matter (OM). The comparison of concentrations of trace metals (normalized towards Al) between the surficial sediments and the subsurface core sediments based on 137Cs datings indicates that almost all surficial-sediment data fall inside or deviate slightly from the 95 % prediction interval of a background regression line. In addition, as determined by a Community Bureau of Reference three-step extraction, trace metals mainly dominate in the residual fraction (assumed to relate to crystal lattice of primary and secondary minerals), and this fraction does not change much in recent sediment layers. Therefore, trace metals are supposed to be derived from natural bio/geochemical processes and are characterized by a low potential mobility. Consequently, the established linear regression relationships of trace metal vs. Al or multiple regression relationships of trace metal vs. multi-elements (Al, Fe, Ca, and OM) are useful for the prediction of background levels of trace metals in sediments in future pollution monitoring and assessment programs.

The suspended sediment discharge of the rivers running along territories of contemporary volcanism in Kamchatka

The sediment discharge of rivers for the territories of the Avachinskaya and Northern groups of volcanoes (the Klyuchevskaya group of volcanoes, and Shiveluch Volcano), constituting zones of active volcanism on the territory of Kamchatka Peninsula was analyzed. A special model based on the principle of multiple regression relationships between the amount of suspended sediment discharge and its formation factors has been employed. This has allowed us to obtain assessments of the sediment discharge into the Pacific Ocean for the eastern coast of Kamchatka.

Air‐sea flux of CO2 in the Arctic Ocean, 1998–2003

The Arctic Ocean has experienced an unprecedented reduction in sea ice over the last 3 decades, increasing the potential for greater exchange of gases such as carbon dioxide (CO2) between the atmosphere and the upper ocean. The present study utilizes remotely sensed data on distributions of both sea ice and chlorophyll a, together with modeled temperature and salinity fields, to obtain high‐resolution basin‐scale estimates of the air‐sea flux of CO2 (FCO2) in the Arctic Ocean for the years 1998–2003. Concentrations of dissolved inorganic carbon (DIC) were derived from multiple linear regression relationships with sea surface temperature, salinity, and chlorophyll a. The partial pressure of carbon dioxide (pCO2) in surface waters was computed from DIC and alkalinity, the latter of which varied with salinity. FCO2 was calculated from the air‐sea difference in pCO2 and wind speed. Annual FCO2 was highest in the Atlantic‐dominated Greenland and Barents sectors due to their lower sea ice cover, although area‐normalized FCO2 in these sectors was low. Only the Siberian sector exhibited a significant increase in annual FCO2 during the time of our study, due to a corresponding increase in ice‐free water. Overall, the Arctic Ocean was a net atmospheric sink for CO2, with annual FCO2 averaging 118 ± 7 Tg C yr−1 during 1998–2003.

Estimation of soil hydraulic properties using proximal spectral reflectance in visible, near-infrared, and shortwave-infrared (VIS–NIR–SWIR) region

Characterization of soil hydraulic properties is an important step for assessing soil water regime in agricultural fields. Because direct measurement of soil hydraulic properties at multiple locations is costly and time-consuming, pedotransfer functions (PTF) are conveniently used to estimate these properties from easily measurable basic soil properties. Over the last two decades, several studies have demonstrated that basic soil properties of surface soils may be rapidly estimated by measuring soil spectral reflectance. In this study, we evaluated a PTF approach to use proximal spectral reflectance over the visible, near-infrared, and shortwave-infrared (VIS–NIR–SWIR) region (350–2500 nm) as predictor variable in place of basic soil properties. To develop these transfer functions, spectral reflectance of air-dried and sieved soil samples was measured using a handheld spectroradiometer equipped with a contact probe. Transfer functions in the form of multiple linear regression relationships between soil hydraulic properties and different attributes of measured spectral reflectance were developed. These new transfer functions are called spectrotransfer functions (STF). Both the parametric PTFs and STFs for the parameters of van Genuchten water retention model ( α and n) and point PTF for saturated hydraulic conductivity ( K s) were evaluated using the root-mean-squared error ( RMSE). Results show that STFs have the similar accuracy as PTFs for estimating hydraulic properties. Specifically, STFs developed with the absorption features of proximal spectral reflectance performed better than the PTFs for estimating α. Among three hydraulic parameters for which the STFs were developed, van Genuchten parameter n is well predicted with comparatively lower values of RMSE. Thus, this study shows that proximal spectral reflectance of soil may be used for rapid estimation of soil hydraulic properties in a large area with accuracy comparable to PTFs. A rigorous testing in different geographical regions is warranted to establish the utility of STFs as a method for estimating soil hydraulic properties.

Evapotranspiration in the Mekong and Yellow river basins / Evapotranspiration dans les bassins du Mekong et du Fleuve Jaune

Estimates of potential evapotranspiration (PET) and reference evapotranspiration (RET) were compared over the Mekong and Yellow river basins, representing humid and semi-arid Asian monsoon regions. Multiple regression relationships between monthly RET, PET, LAI (leaf area index) and climatic variables were explored for different vegetation types. Over the Mekong River basin, the spatial average of RET is only 1.7% lower than PET; however, RET is 140% higher than PET over parts of the Tibetan Plateau, due to the short and sparse grassland, and 30% lower than PET in parts of the lower basin due to the tall and well-developed forests. Over the Yellow River basin, RET is estimated to be higher than PET, on average about 50% higher across the whole basin, due to the generally sparse vegetation. A close linear relationship between annual RET and PET allows the establishment of a regional regression to predict monthly PET from monthly RET, climatic variables and/or vegetation LAI. However, the large prediction errors indicate that the Shuttleworth-Wallace (S-W) model, although it is more complex, should be recommended due to its more robust physical basis and because it successfully accounts for the effect of changing land surface conditions on PET. The limited available field data suggest that the S-W estimate may be more realistic. It was also found that vegetation conditions in summer are primarily controlled by the regional antecedent precipitation in the cold and dry seasons over the Loess Plateau in the middle reaches of the Yellow River.