Soil organic carbon stocks in Veracruz State (Mexico) estimated using the 1:250,000 soil database of INEGI: biophysical contributions

Purpose

In this study, we quantified soil organic carbon (SOC) stocks and analyzed their relationship with biophysical factors and soil properties.

Materials and methods

The study region was Veracruz State, located in the eastern part of Mexico, covering an area of 72,410 km². A soil database that contains physicochemical analyses of soil horizons such as carbon concentration data was the source of information used in this study. The database consisted of 163 soil profiles representing 464 genetic horizons. Statistical analysis was used to investigate the effect of each factor (climate, altitude, slope) on SOC stock to 0.50 m depth and to assess differences in the distribution of SOC stock in terms of soil depth (0.0–0.20, 0.20–0.40, 0.40–0.60, 0.60–0.80, 0.80–1.0 m) and land use. In order to compute the spatial distribution of SOC stock to 0.50 m depth based on the soil sampling location, the kriging method was used.

Results and discussion

Results indicated that SOC stock (0.50 m depth) ranged between 0.44 and 41.2 kg C m^?2. Regression analysis showed that SOC stocks (0.50 m depth) are negatively correlated with temperature (r?=??0.38; P?<?0.001) and positively correlated with altitude (r?=?0.40; P?<?0.001) and slope (r?=?0.40; P?<?0.001). In addition, by multiple regression, temperature combined with precipitation explained more SOC stock variations (r?=?0.43; P?<?0.001) than the regression model with precipitation (r?=?0.13; P?=?0.16) alone. Also, slope combined with temperature and precipitation explained more SOC stock variations (r?=?0.46; P?<?0.001) than the regression model with slope alone. Forest lands, grasslands, and croplands have higher SOC stocks in the 0.0–0.20-m soil layer than in deeper layers. On average, forest lands, grasslands, croplands, and other lands (wetland and dunes) had a SOC stock of 13.6, 14.6, 15.1, and 8.5 kg C m^?2 at 1 m depth, respectively. Soil color correlated (?0.25 ≤ r ≤ ?0.89) with SOC content.

Conclusions

Overall, these results indicate the influence of major interactions between biophysical factors and SOC stocks. This research indicated that SOC stock decreased with soil depth, but with slight variations depending on land use. Thus, there remains a need for more SOC data that include an improved distribution of soil sampling points in order to entirely understand the contributions of biophysical factors to SOC stocks in Veracruz State.     

Impact of soil types on sugarcane development monitored over time by remote sensing

Soil is one of the most important factors for agricultural production. In tropical regions, soil variability is considerable, with the most diverse combinations of physical and chemical characteristics, an influence factor in crop growth and productivity. In this research, the main objective was to identify how soil characteristics and parent material can influence sugarcane development over time using remote sensing. An area located in Sao Paulo, Brazil, of 182 ha (one point per ha with soil analysis), with high variability in the parent material and soil types, was selected. Images from the Sentinel2-MSI satellite were used to describe the spectral behavior of sugarcane over a period of one year. The NDRE (normalized difference red-edge index) was calculated for each image and then the leaf area index (LAI) was obtained from it. Maps of soil classes, soil properties at two depths (0–0.20 and 0.80–1.0 m), and parent material classes were related to sugarcane LAI variability over time. Production environment zones, which is a classification based on soil characteristics to support sugarcane development, were also obtained and related to LAI variability. Spectral signatures of the crop presented different behaviors through the season, soil types and soil attributes provided useful responses for this variability. At the beginning of the season, the surface and subsurface soil properties (texture and fertility) impacted differently on crop development. On the other hand, soil classes and parent material influenced LAI in all production environments studied. The results indicated that the soil types and their properties at different depths have a significant impact on sugarcane development. Furthermore, RS was able to monitor the plant evolution and be related to soil types which may assist in plant management. The results can bring light on how better sugarcane management can be conducted using remote sensing data and soils variability.

     

Molecular and Structural Characterizations of Lipases from Chlorella by Functional Genomics

Microalgae have been poorly investigated for new-lipolytic enzymes of biotechnological interest. In silico study combining analysis of sequences homologies and bioinformatic tools allowed the identification and preliminary characterization of 14 putative lipases expressed by Chlorella vulagaris. These proteins have different molecular weights, subcellular localizations, low instability index range and at least 40% of sequence identity with other microalgal lipases. Sequence comparison indicated that the catalytic triad corresponded to residues Ser, Asp and His, with the nucleophilic residue Ser positioned within the consensus GXSXG pentapeptide. 3D models were generated using different approaches and templates and demonstrated that these putative enzymes share a similar core with common α/β hydrolases fold belonging to family 3 lipases and class GX. Six lipases were predicted to have a transmembrane domain and a lysosomal acid lipase was identified. A similar mammalian enzyme plays an important role in breaking down cholesteryl esters and triglycerides and its deficiency causes serious digestive problems in human. More structural insight would provide important information on the enzyme characteristics.     

The oceanic sink for anthropogenic CO2

Using inorganic carbon measurements from an international survey effort in the 1990s and a tracer-based separation technique, we estimate a global oceanic anthropogenic carbon dioxide (CO2) sink for the period from 1800 to 1994 of 118 +/- 19 petagrams of carbon. The oceanic sink accounts for approximately 48% of the total fossil-fuel and cement-manufacturing emissions, implying that the terrestrial biosphere was a net source of CO2 to the atmosphere of about 39 +/- 28 petagrams of carbon for this period. The current fraction of total anthropogenic CO2 emissions stored in the ocean appears to be about one-third of the long-term potential.     

优质高产抗病小麦新品种镇麦6号的选育与栽培技术要点

镇麦6号(原名镇99018)系江苏丘陵地区镇江农业科学研究所选育的优质高产抗病小麦新品种,2005年9月通过江苏省农作物品种审定委员会审定并定名。1选育经过1994年春,用高产稳产综合性状好的扬麦158作母本,分蘖性强易争穗、抗病性好的镇麦1号为父本,通过有性杂交,采用系谱法选育。于1997年夏收,将F3中的20株优良单株赴昆明加代,并将其中15个株系混收升级鉴定,在鉴定中有11个株系的主要农艺性状仍有分离,继续选取优良单株。1999年夏收,在F6中将综合农艺性状趋向一致的10个株系进行升级鉴定试验。其中,鉴定号为“99018”株系表现突出,提升参加本…     

棉花病虫害防治中的叶面喷肥技术应用

为了增加棉花的产量,应该及时采取有效的防治病虫害措施。本文介绍棉花的叶面喷肥处理方法,及其如何应用于防治棉花的病虫害上。     

Assessment of Poisson, probit and linear models for genetic analysis of presence and number of black spots in Corriedale sheep

Black skin spots are associated with pigmented fibres in wool, an important quality fault. Our objective was to assess alternative models for genetic analysis of presence (BINBS) and number (NUMBS) of black spots in Corriedale sheep. During 2002-08, 5624 records from 2839 animals in two flocks, aged 1 through 6 years, were taken at shearing. Four models were considered: linear and probit for BINBS and linear and Poisson for NUMBS. All models included flock-year and age as fixed effects and animal and permanent environmental as random effects. Models were fitted to the whole data set and were also compared based on their predictive ability in cross-validation. Estimates of heritability ranged from 0.154 to 0.230 for BINBS and 0.269 to 0.474 for NUMBS. For BINBS, the probit model fitted slightly better to the data than the linear model. Predictions of random effects from these models were highly correlated, and both models exhibited similar predictive ability. For NUMBS, the Poisson model, with a residual term to account for overdispersion, performed better than the linear model in goodness of fit and predictive ability. Predictions of random effects from the Poisson model were more strongly correlated with those from BINBS models than those from the linear model. Overall, the use of probit or linear models for BINBS and of a Poisson model with a residual for NUMBS seems a reasonable choice for genetic selection purposes in Corriedale sheep.