Method to estimate soil macroporosity and microporosity based on sand content and bulk density

Macroporosity is often used in the determination of soil compaction. Reduced macroporosity can lead to poor drainage, low root aeration and soil degradation. The aim of this study was to develop and test different models to estimate macro and microporosity efficiently, using multiple regression. Ten soils were selected within a large range of textures: sand (Sa) 0.07-0.84; silt 0.03-0.24; clay 0.13-0.78 kg kg-1 and subjected to three compaction levels (three bulk densities, BD). Two models with similar accuracy were selected, with a mean error of about 0.02 m³ m-3 (2 %). The model y = a + b.BD + c.Sa, named model 2, was selected for its simplicity to estimate Macro (Ma), Micro (Mi) or total porosity (TP): Ma = 0.693 - 0.465 BD + 0.212 Sa; Mi = 0.337 + 0.120 BD - 0.294 Sa; TP = 1.030 - 0.345 BD 0.082 Sa; porosity values were expressed in m³ m-3; BD in kg dm-3; and Sa in kg kg-1. The model was tested with 76 datum set of several other authors. An error of about 0.04 m³ m-3 (4 %) was observed. Simulations of variations in BD as a function of Sa are presented for Ma = 0 and Ma = 0.10 (10 %). The macroporosity equation was remodeled to obtain other compaction indexes: a) to simulate maximum bulk density (MBD) as a function of Sa (Equation 11), in agreement with literature data; b) to simulate relative bulk density (RBD) as a function of BD and Sa (Equation 13); c) another model to simulate RBD as a function of Ma and Sa (Equation 16), confirming the independence of this variable in relation to Sa for a fixed value of macroporosity and, also, proving the hypothesis of Hakansson & Lipiec that RBD = 0.87 corresponds approximately to 10 % macroporosity (Ma = 0.10 m³ m-3).

Proposal for an index to classify irrigation water quality: a case study in northeastern Brazil

One way of classifying water quality is by means of indices, in which a series of parameters analyzed are joined a single value, facilitating the interpretation of extensive lists of variables or indicators, underlying the classification of water quality. The objective of this study was to develop a statistically based index to classify water according to the Irrigation Water Quality Index (IWQI), to evaluate the ionic composition of water for use in irrigation and classify it by its source. For this purpose, the database generated during the Technology Generation and Adaptation (GAT) program was used, in which, as of 1988, water samples were collected monthly from water sources in the states of Paraíba, Rio Grande do Norte and Ceará. To evaluate water quality, the electrical conductivity (EC) of irrigation water was taken as a reference, with values corresponding to 0.7 dS m-1. The chemical variables used in this study were: pH, EC, Ca, Mg, Na, K, Cl, HCO3, CO3, and SO4. The data of all characteristics evaluated were standardized and data normality was confirmed by Lilliefors test. Then the irrigation water quality index was determined by an equation that relates the standardized value of the variable with the number of characteristics evaluated. Thus, the IWQI was classified based on indices, considering normal distribution. Finally, these indices were subjected to regression analysis. The method proposed for the IWQI allowed a satisfactory classification of the irrigation water quality, being able to estimate it as a function of EC for the three water sources. Variation in the ionic composition was observed among the three sources and within a single source. Although the water quality differed, it was good in most cases, with the classification IWQI II.

Optical crop sensor for variable-rate nitrogen fertilization in corn: i - plant nutrition and dry matter production

Variable-rate nitrogen fertilization (VRF) based on optical spectrometry sensors of crops is a technological innovation capable of improving the nutrient use efficiency (NUE) and mitigate environmental impacts. However, studies addressing fertilization based on crop sensors are still scarce in Brazilian agriculture. This study aims to evaluate the efficiency of an optical crop sensor to assess the nutritional status of corn and compare VRF with the standard strategy of traditional single-rate N fertilization (TSF) used by farmers. With this purpose, three experiments were conducted at different locations in Southern Brazil, in the growing seasons 2008/09 and 2010/11. The following crop properties were evaluated: above-ground dry matter production, nitrogen (N) content, N uptake, relative chlorophyll content (SPAD) reading, and a vegetation index measured by the optical sensor N-Sensor® ALS. The plants were evaluated in the stages V4, V6, V8, V10, V12 and at corn flowering. The experiments had a completely randomized design at three different sites that were analyzed separately. The vegetation index was directly related to above-ground dry matter production (R² = 0.91; p<0.0001), total N uptake (R² = 0.87; p<0.0001) and SPAD reading (R² = 0.63; p<0.0001) and inversely related to plant N content (R² = 0.53; p<0.0001). The efficiency of VRF for plant nutrition was influenced by the specific climatic conditions of each site. Therefore, the efficiency of the VRF strategy was similar to that of the standard farmer fertilizer strategy at sites 1 and 2. However, at site 3 where the climatic conditions were favorable for corn growth, the use of optical sensors to determine VRF resulted in a 12 % increase in N plant uptake in relation to the standard fertilization, indicating the potential of this technology to improve NUE.

Optical crop sensor for variable-rate nitrogen fertilization in corn: II - indices of fertilizer efficiency and corn yield

Generally, in tropical and subtropical agroecosystems, the efficiency of nitrogen (N) fertilization is low, inducing a temporal variability of crop yield, economic losses, and environmental impacts. Variable-rate N fertilization (VRF), based on optical spectrometry crop sensors, could increase the N use efficiency (NUE). The objective of this study was to evaluate the corn grain yield and N fertilization efficiency under VRF determined by an optical sensor in comparison to the traditional single-application N fertilization (TSF). With this purpose, three experiments with no-tillage corn were carried out in the 2008/09 and 2010/11 growing seasons on a Hapludox in South Brazil, in a completely randomized design, at three different sites that were analyzed separately. The following crop properties were evaluated: aboveground dry matter production and quantity of N uptake at corn flowering, grain yield, and vegetation index determined by an N-Sensor® ALS optical sensor. Across the sites, the corn N fertilizer had a positive effect on corn N uptake, resulting in increased corn dry matter and grain yield. However, N fertilization induced lower increases of corn grain yield at site 2, where there was a severe drought during the growing period. The VRF defined by the optical crop sensor increased the apparent N recovery (NRE) and agronomic efficiency of N (NAE) compared to the traditional fertilizer strategy. In the average of sites 1 and 3, which were not affected by drought, VRF promoted an increase of 28.0 and 41.3 % in NAE and NRE, respectively. Despite these results, no increases in corn grain yield were observed by the use of VRF compared to TSF.

Quantification of permanent and variable charges in reference soils of the State of Pernambuco, Brazil

The electrical charges in soil particles are divided into structural or permanent charges and variable charges. Permanent charges develop on the soil particle surface by isomorphic substitution. Variable charges arise from dissociation and association of protons (H+), protonation or deprotonation, and specific adsorption of cations and anions. The aim of this study was to quantify the permanent charges and variable charges of Reference Soils of the State of Pernambuco, Brazil. To do so, 24 subsurface profiles from different regions (nine in the Zona da Mata, eight in the Agreste, and seven in the Sertão) were sampled, representing approximately 80 % of the total area of the state. Measurements were performed using cesium chloride solution. Determination was made of the permanent charges and the charges in regard to the hydroxyl functional groups through selective ion exchange of Cs+ by Li+ and Cs+ by NH4+, respectively. All the soils analyzed exhibited variable cation exchange capacity, with proportions from 0.16 to 0.60 and an average of 0.40 when related to total cation exchange capacity.

LOSS OF EXTRACTION CAPACITY OF MEHLICH-1 AND MONOCALCIUM PHOSPHATE AS A VARIABLE OF REMAINING P AND ITS RELATIONSHIP TO CRITICAL LEVELS OF SOIL PHOSPHORUS AND SULFUR

The Mehlich-1 (M-1) extractant and Monocalcium Phosphate in acetic acid (MCPa) have mechanisms for extraction of available P and S in acidity and in ligand exchange, whether of the sulfate of the extractant by the phosphate of the soil, or of the phosphate of the extractant by the sulfate of the soil. In clayey soils, with greater P adsorption capacity, or lower remaining P (Rem-P) value, which corresponds to soils with greater Phosphate Buffer Capacity (PBC), more buffered for acidity, the initially low pH of the extractants increases over their time of contact with the soil in the direction of the pH of the soil; and the sulfate of the M-1 or the phosphate of the MCPa is adsorbed by adsorption sites occupied by these anions or not. This situation makes the extractant lose its extraction capacity, a phenomenon known as loss of extraction capacity or consumption of the extractant, the object of this study. Twenty soil samples were chosen so as to cover the range of Rem-P (0 to 60 mg L-1). Rem-P was used as a measure of the PBC. The P and S contents available from the soil samples through M-1 and MCPa, and the contents of other nutrients and of organic matter were determined. For determination of loss of extraction capacity, after the rest period, the pH and the P and S contents were measured in both the extracts-soils. Although significant, the loss of extraction capacity of the acidity of the M-1 and MCPa extractants with reduction in the Rem-P value did not have a very expressive effect. A “linear plateau” model was observed for the M-1 for discontinuous loss of extraction capacity of the P content in accordance with reduction in the concentration of the Rem-P or increase in the PBC, suggesting that a discontinuous model should also be adopted for interpretation of available P of soils with different Rem-P values. In contrast, a continuous linear response was observed between the P variables in the extract-soil and Rem-P for the MCPa extractor, which shows increasing loss of extraction capacity of this extractor with an increase in the PBC of the soil, indicating the validity of the linear relationship between the available S of the soil and the PBC, estimated by Rem-P, as currently adopted.

Surface Complexation Modeling in Variable Charge Soils: Charge Characterization by Potentiometric Titration

ABSTRACT Intrinsic equilibrium constants of 17 representative Brazilian Oxisols were estimated from potentiometric titration measuring the adsorption of H+ and OH− on amphoteric surfaces in suspensions of varying ionic strength. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. The former was fitted by calculating total site concentration from curve fitting estimates and pH-extrapolation of the intrinsic equilibrium constants to the PZNPC (hand calculation), considering one and two reactive sites, and by the FITEQL software. The latter was fitted only by FITEQL, with one reactive site. Soil chemical and physical properties were correlated to the intrinsic equilibrium constants. Both surface complexation models satisfactorily fit our experimental data, but for results at low ionic strength, optimization did not converge in FITEQL. Data were incorporated in Visual MINTEQ and they provide a modeling system that can predict protonation-dissociation reactions in the soil surface under changing environmental conditions.

Surface Complexation Modeling in Variable Charge Soils: Prediction of Cadmium Adsorption

ABSTRACT Intrinsic equilibrium constants for 22 representative Brazilian Oxisols were estimated from a cadmium adsorption experiment. Equilibrium constants were fitted to two surface complexation models: diffuse layer and constant capacitance. Intrinsic equilibrium constants were optimized by FITEQL and by hand calculation using Visual MINTEQ in sweep mode, and Excel spreadsheets. Data from both models were incorporated into Visual MINTEQ. Constants estimated by FITEQL and incorporated in Visual MINTEQ software failed to predict observed data accurately. However, FITEQL raw output data rendered good results when predicted values were directly compared with observed values, instead of incorporating the estimated constants into Visual MINTEQ. Intrinsic equilibrium constants optimized by hand calculation and incorporated in Visual MINTEQ reliably predicted Cd adsorption reactions on soil surfaces under changing environmental conditions.

Water-Stable Aggregates and Associated Carbon in a Subtropical Rice Soil Under Variable Tillage

ABSTRACT Tillage systems can influence C sequestration by changing aggregate formation and C distribution within the aggregate. This study was undertaken to explore the impact of no-tillage without straw (NT-S) and with straw (NT+S), and moldboard plow without straw (MP-S) and with straw (MP+S), on soil aggregation and aggregate-associated C after six years of double rice planting in a Hydragric Anthrosol in Guangxi, southwest of China. Soil samples of 0.00-0.05, 0.05-0.20 and 0.20-0.30 m layers were wet-sieved and divided into four aggregate-size classes, >2 mm, 2.00-0.25 mm, 0.25-0.053 and <0.053 mm, respectively, for measuring aggregate associated C and humic and fulvic acids. Results showed that the soil organic carbon (SOC) stock in bulk soil was 40.2-51.1 % higher in the 0.00-0.05 m layer and 11.3-17.0 % lower in the 0.05-0.20 m layer in NT system (NT+S and NT-S) compared to the MP system (MP+S and MP-S), respectively. However, no statistical difference was found across the whole 0.00-0.30 m layer. The NT system increased the proportion of >2 mm aggregate fraction and reduced the proportion of <0.053 mm aggregates in both 0.00-0.05 and 0.05-0.20 m layers. The SOC concentration, SOC stock and humic and fulvic acids within the >0.25 mm macroaggregate fraction also significantly increased in the 0.00-0.5 m layer in NT system. However, those within the 2.00-0.25 mm aggregate fraction were significantly reduced in the 0.05-0.200 m layer under NT system. Straw incorporation increased not only the SOC stock in bulk soil, but also the proportion of macroaggregate, aggregate associated with SOC and humic and fulvic acids concentration within the aggregate. The effect of straw on C sequestration might be dependent on the location of straw incorporation. In conclusion, the NT system increased the total SOC accumulation and humic and fulvic acids within macroaggregates, thus contributing to C sequestration in the 0.00-0.05 m layer.

Low-cost microprocessed instrument for evaluating soil temperature profile

This paper describes a low-cost microprocessed instrument for in situ evaluating soil temperature profile ranging from -20.0°C to 99.9°C, and recording soil temperature data at eight depths from 2 to 128 cm. Of great importance in agriculture, soil temperature affects plant growth directly, and nutrient uptake as well as indirectly in soil water and gas flow, soil structure and nutrient availability. The developed instrument has potential applications in the soil science, when temperature monitoring is required. Results show that the instrument with its individual sensors guarantees ±0.25°C accuracy and 0.1°C resolution, making possible localized management changes within decision support systems. The instrument, based on complementary metal oxide semiconductor devices as well as thermocouples, operates in either automatic or non-automatic mode.

Factors associated with 56-day non-return rate in dairy cattle

The objective of this work was to identify factors associated with the 56-day non-return rate (56-NRR) in dairy herds in the Galician region, Spain, and to estimate it for individual Holstein bulls. The experiment was carried out in herds originated from North-West Spain, from September 2008 to August 2009. Data of the 76,440 first inseminations performed during this period were gathered. Candidate factors were tested for their association with the 56-NRR by using a logistic model (binomial). Afterwards, 37 sires with a minimum of 150 first performed inseminations were individually evaluated. Logistic models were also estimated for each bull, and predicted individual 56-NRR rate values were calculated as a solution for the model parameters. Logistic regression found four major factors associated with 56-NRR in lactating cows: age at insemination, days from calving to insemination, milk production level at the time of insemination, and herd size. First-service conception rate, when a particular sire was used, was higher for heifers (0.71) than for lactating cows (0.52). Non-return rates were highly variable among bulls. Asignificant part of the herd-level variation of 56-NRR of Holstein cattle seems attributable to the service sire. High correlation level between observed and predicted 56-NRR was found.

Stability and adaptability of runner peanut genotypes based on nonlinear regression and AMMI analysis

The objective of this work was to estimate the stability and adaptability of pod and seed yield in runner peanut genotypes based on the nonlinear regression and AMMI analysis. Yield data from 11 trials, distributed in six environments and three harvests, carried out in the Northeast region of Brazil during the rainy season were used. Significant effects of genotypes (G), environments (E), and GE interactions were detected in the analysis, indicating different behaviors among genotypes in favorable and unfavorable environmental conditions. The genotypes BRS Pérola Branca and LViPE‑06 are more stable and adapted to the semiarid environment, whereas LGoPE‑06 is a promising material for pod production, despite being highly dependent on favorable environments.

Random regression models to estimate genetic parameters for milk production of Guzerat cows using orthogonal Legendre polynomials

The objective of this work was to compare random regression models for the estimation of genetic parameters for Guzerat milk production, using orthogonal Legendre polynomials. Records (20,524) of test-day milk yield (TDMY) from 2,816 first-lactation Guzerat cows were used. TDMY grouped into 10-monthly classes were analyzed for additive genetic effect and for environmental and residual permanent effects (random effects), whereas the contemporary group, calving age (linear and quadratic effects) and mean lactation curve were analized as fixed effects. Trajectories for the additive genetic and permanent environmental effects were modeled by means of a covariance function employing orthogonal Legendre polynomials ranging from the second to the fifth order. Residual variances were considered in one, four, six, or ten variance classes. The best model had six residual variance classes. The heritability estimates for the TDMY records varied from 0.19 to 0.32. The random regression model that used a second-order Legendre polynomial for the additive genetic effect, and a fifth-order polynomial for the permanent environmental effect is adequate for comparison by the main employed criteria. The model with a second-order Legendre polynomial for the additive genetic effect, and that with a fourth-order for the permanent environmental effect could also be employed in these analyses.

Aplicação de algoritmos genéticos na seleção de variáveis em espectroscopia no infravermelho médio: determinação simultânea de glicose, maltose e frutose

Genetic algorithm was used for variable selection in simultaneous determination of mixtures of glucose, maltose and fructose by mid infrared spectroscopy. Different models, using partial least squares (PLS) and multiple linear regression (MLR) with and without data pre-processing, were used. Based on the results obtained, it was verified that a simpler model (multiple linear regression with variable selection by genetic algorithm) produces results comparable to more complex methods (partial least squares). The relative errors obtained for the best model was around 3% for the sugar determination, which is acceptable for this kind of determination.

Estudo do coeficiente de partição octanol-água de bifenilas policloradas (PCBs) utilizando parâmetros topológicos

A model based on chemical structure was developed for the accurate prediction of octanol/water partition coefficient (K OW) of polychlorinated biphenyls (PCBs), which are molecules of environmental interest. Partial least squares (PLS) was used to build the regression model. Topological indices were used as molecular descriptors. Variable selection was performed by Hierarchical Cluster Analysis (HCA). In the modeling process, the experimental K OW measured for 30 PCBs by thin-layer chromatography - retention time (TLC-RT) has been used. The developed model (Q² = 0,990 and r² = 0,994) was used to estimate the log K OW values for the 179 PCB congeners whose K OW data have not yet been measured by TLC-RT method. The results showed that topological indices can be very useful to predict the K OW.