The application of agricultural land rating and crop models to CO2 and climate change issues in Northern regions : the Mackenzie Basin case study

Selostus: Viljelyvyöhykkeiden ja kasvumallien soveltaminen ilmastonmuutoksen tutkimisessa: Mackenzien jokialue, Kanada

Carbon balance and crop residue management in dynamic equilibrium under a no-till system in Campos Gerais

The adoption of no-tillage systems (NT) and the maintenance of crop residues on the soil surface result in the long-term increase of carbon (C) in the system, promoting C sequestration and reducing C-CO2 emissions to the atmosphere. The purpose of this study was to evaluate the C sequestration rate and the minimum amount of crop residues required to maintain the dynamic C equilibrium (dC/dt = 0) of two soils (Typic Hapludox) with different textural classes. The experiment was arranged in a 2 x 2 x 2 randomized block factorial design. The following factors were analyzed: (a) two soil types: Typic Hapludox (Oxisol) with medium texture (LVTM) and Oxisol with clay texture (LVTA), (b) two sampling layers (0-5 and 5-20 cm), and (c) two sampling periods (P1 - October 2007; P2 - September 2008). Samples were collected from fields under a long-term (20 years) NT system with the following crop rotations: wheat/soybean/black oat + vetch/maize (LVTM) and wheat/maize/black oat + vetch/soybean (LVTA). The annual C sequestration rates were 0.83 and 0.76 Mg ha-1 for LVTM and LVTA, respectively. The estimates of the minimum amount of crop residues required to maintain a dynamic equilibrium (dC/dt = 0) were 7.13 and 6.53 Mg ha-1 year-1 for LVTM and LVTA, respectively. The C conversion rate in both studied soils was lower than that reported in other studies in the region, resulting in a greater amount of crop residues left on the soil surface.

Automatic Mapping and Classification of Spatial Environmental Data

The paper deals with the development and application of the generic methodology for automatic processing (mapping and classification) of environmental data. General Regression Neural Network (GRNN) is considered in detail and is proposed as an efficient tool to solve the problem of spatial data mapping (regression). The Probabilistic Neural Network (PNN) is considered as an automatic tool for spatial classifications. The automatic tuning of isotropic and anisotropic GRNN/PNN models using cross-validation procedure is presented. Results are compared with the k-Nearest-Neighbours (k-NN) interpolation algorithm using independent validation data set. Real case studies are based on decision-oriented mapping and classification of radioactively contaminated territories.

A colorectal cancer classification system that associates cellular phenotype and responses to therapy.

Colorectal cancer (CRC) is a major cause of cancer mortality. Whereas some patients respond well to therapy, others do not, and thus more precise, individualized treatment strategies are needed. To that end, we analyzed gene expression profiles from 1,290 CRC tumors using consensus-based unsupervised clustering. The resultant clusters were then associated with therapeutic response data to the epidermal growth factor receptor-targeted drug cetuximab in 80 patients. The results of these studies define six clinically relevant CRC subtypes. Each subtype shares similarities to distinct cell types within the normal colon crypt and shows differing degrees of 'stemness' and Wnt signaling. Subtype-specific gene signatures are proposed to identify these subtypes. Three subtypes have markedly better disease-free survival (DFS) after surgical resection, suggesting these patients might be spared from the adverse effects of chemotherapy when they have localized disease. One of these three subtypes, identified by filamin A expression, does not respond to cetuximab but may respond to cMET receptor tyrosine kinase inhibitors in the metastatic setting. Two other subtypes, with poor and intermediate DFS, associate with improved response to the chemotherapy regimen FOLFIRI in adjuvant or metastatic settings. Development of clinically deployable assays for these subtypes and of subtype-specific therapies may contribute to more effective management of this challenging disease.

Comparison between artificial neural networks and maximum likelihood classification in digital soil mapping

Soil surveys are the main source of spatial information on soils and have a range of different applications, mainly in agriculture. The continuity of this activity has however been severely compromised, mainly due to a lack of governmental funding. The purpose of this study was to evaluate the feasibility of two different classifiers (artificial neural networks and a maximum likelihood algorithm) in the prediction of soil classes in the northwest of the state of Rio de Janeiro. Terrain attributes such as elevation, slope, aspect, plan curvature and compound topographic index (CTI) and indices of clay minerals, iron oxide and Normalized Difference Vegetation Index (NDVI), derived from Landsat 7 ETM+ sensor imagery, were used as discriminating variables. The two classifiers were trained and validated for each soil class using 300 and 150 samples respectively, representing the characteristics of these classes in terms of the discriminating variables. According to the statistical tests, the accuracy of the classifier based on artificial neural networks (ANNs) was greater than of the classic Maximum Likelihood Classifier (MLC). Comparing the results with 126 points of reference showed that the resulting ANN map (73.81 %) was superior to the MLC map (57.94 %). The main errors when using the two classifiers were caused by: a) the geological heterogeneity of the area coupled with problems related to the geological map; b) the depth of lithic contact and/or rock exposure, and c) problems with the environmental correlation model used due to the polygenetic nature of the soils. This study confirms that the use of terrain attributes together with remote sensing data by an ANN approach can be a tool to facilitate soil mapping in Brazil, primarily due to the availability of low-cost remote sensing data and the ease by which terrain attributes can be obtained.

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.

Soil quality indicators in a rhodic kandiudult under different uses in northern Parana, Brazil

Sustainable use of soil, maintaining or improving its quality, is one of the goals of diversification in farmlands. From this point of view, bioindicators associated with C, N and P cycling can be used in assessments of land-use effects on soil quality. The aim of this study was to investigate chemical, microbiological and biochemical properties of soil associated with C, N and P under different land uses in a farm property with diversified activity in northern Parana, Brazil. Seven areas under different land uses were assessed: fragment of native Atlantic Forest; growing of peach-palm (Bactrys gasipaes); sugarcane ratoon (Saccharum officinarum) recently harvested, under renewal; growing of coffee (Coffea arabica) intercropped with tree species; recent reforestation (1 year) with native tree species, previously under annual crops; annual crops under no-tillage, rye (Cecale cereale); secondary forest, regenerated after abandonment (for 20 years) of an avocado (Persea americana) orchard. The soil under coffee, recent reforestation and secondary forest showed higher concentrations of organic carbon, but microbial biomass and enzyme activities were higher in soils under native forest and secondary forest, which also showed the lowest metabolic coefficient, followed by the peach-palm area. The lowest content of water-dispersible clay was found in the soil under native forest, differing from soils under sugarcane and secondary forest. Soil cover and soil use affected total organic C contents and soil enzyme and microbial activities, such that more intensive agricultural uses had deeper impacts on the indicators assessed. Calculation of the mean soil quality index showed that the secondary forest was closest to the fragment of native forest, followed by the peach-palm area, coffee-growing area, annual crop area, the area of recent reforestation and the sugarcane ratoon area.

Dry matter and macronutrient accumulation in fruits of Conilon coffee with different ripening cycles

The period between anthesis and fruit ripening varies according to the Conilon coffee (Coffea canephora) genotype. Therefore, the time of the nutritional requirements for fruit formation may differ, depending on the formation phase and the genotype, and may directly affect split application of fertilizer. The aim of this study was to quantify the accumulation of dry matter and N, P, K, Ca, Mg and S at several stages in the fruit of the Conilon coffee genotype with different ripening cycles, which may suggest the need for split application of fertilizer in coffee. The experiment was carried out in the municipality of Nova Venecia, Espírito Santo, Brazil, throughout the reproductive cycle. The treatments were composed of four coffee genotypes with different ripening cycles. A completely randomised experimental design was used. with five replicates. Plagiotropic branches were harvested from flowering to fruit ripening at 28-day intervals to determine the dry matter of the fruits and the concentration and accumulation of the nutrients they contained. The behavior of dry matter and macronutrient accumulation during the study period was similar and increasing, but it differed among genotypes sampled in the same season. Early genotypes exhibited a higher speed of dry matter and nutrient accumulation. Split application of fertilizer should differ among coffee genotypes with different ripening cycles (early, intermediate, late and very late).

Soybean root growth and crop yield in reponse to liming at the beginning of a no-tillage system

Analyzing the soil near crop roots may reveal limitations to growth and yield even in a no-tillage system. The purpose of the present study was to relate the chemical and physical properties of soil under a no-tillage system to soybean root growth and plant yield after five years of use of different types of limestone and forms of application. A clayey Oxisol received application of dolomitic and calcitic limestones and their 1:1 combination in two forms: surface application, maintained on the soil surface; and incorporated, applied on the surface and incorporated mechanically. Soil physical properties (resistance to mechanical penetration, soil bulk density and soil aggregation), soil chemical properties (pH, exchangeable cations, H+Al, and cation exchange capacity) and plant parameters (root growth system, soybean grain yield, and oat dry matter production) were evaluated five years after setting up the experiment. Incorporation of lime neutralized exchangeable Al up to a depth of 20 cm without affecting the soil physical properties. The soybean root system reached depths of 40 cm or more with incorporated limestone, increasing grain yield an average of 31 % in relation to surface application, which limited the effect of lime up to a depth of 5 cm and root growth up to 20 cm. It was concluded that incorporation of limestone at the beginning of a no-tillage system ensures a favorable environment for root growth and soybean yield, while this intervention does not show long-term effects on soil physical properties under no-tillage. This suggests that there is resilience in the physical properties evaluated.

Soil aggregation and organic carbon of Oxisols under coffee in agroforestry systems

Intensive land use can lead to a loss of soil physical quality with negative impacts on soil aggregates, resistance to root penetration, porosity, and bulk density. Organic and agroforestry management systems can represent sustainable, well-balanced alternatives in the agroecosystem for promoting a greater input of organic matter than the conventional system. Based on the hypothesis that an increased input of organic matter improves soil physical quality, this study aimed to evaluate the impact of coffee production systems on soil physical properties in two Red-Yellow Oxisols (Latossolos Vermelho-Amarelos) in the region of Caparaó, Espirito Santo, Brazil. On Farm 1, we evaluated the following systems: primary forest (Pf1), organic coffee (Org1) and conventional coffee (Con1). On Farm 2, we evaluated: secondary forest (Sf2), organic coffee intercropped with inga (Org/In2), organic coffee intercropped with leucaena and inga (Org/In/Le2), organic coffee intercropped with cedar (Org/Ced2) and unshaded conventional coffee (Con2). Soil samples were collected under the tree canopy from the 0-10, 10-20 and 20-40 cm soil layers. Under organic and agroforestry coffee management, soil aggregation was higher than under conventional coffee. In the agroforestry system, the degree of soil flocculation was 24 % higher, soil moisture was 80 % higher, and soil resistance to penetration was lower than in soil under conventional coffee management. The macroaggregates in the organic systems, Org/In2, Org/In/Le2, and Org/Ced2 contained, on average, 29.1, 40.1 and 34.7 g kg-1 organic carbon, respectively. These levels are higher than those found in the unshaded conventional system (Con2), with 20.2 g kg-1.

Using numerical classification of profiles based on Vis-NIR spectra to distinguish soils from the Piracicaba Region, Brazil

Considering that information from soil reflectance spectra is underutilized in soil classification, this paper aimed to evaluate the relationship of soil physical, chemical properties and their spectra, to identify spectral patterns for soil classes, evaluate the use of numerical classification of profiles combined with spectral data for soil classification. We studied 20 soil profiles from the municipality of Piracicaba, State of São Paulo, Brazil, which were morphologically described and classified up to the 3rd category level of the Brazilian Soil Classification System (SiBCS). Subsequently, soil samples were collected from pedogenetic horizons and subjected to soil particle size and chemical analyses. Their Vis-NIR spectra were measured, followed by principal component analysis. Pearson's linear correlation coefficients were determined among the four principal components and the following soil properties: pH, organic matter, P, K, Ca, Mg, Al, CEC, base saturation, and Al saturation. We also carried out interpretation of the first three principal components and their relationships with soil classes defined by SiBCS. In addition, numerical classification of the profiles based on the OSACA algorithm was performed using spectral data as a basis. We determined the Normalized Mutual Information (NMI) and Uncertainty Coefficient (U). These coefficients represent the similarity between the numerical classification and the soil classes from SiBCS. Pearson's correlation coefficients were significant for the principal components when compared to sand, clay, Al content and soil color. Visual analysis of the principal component scores showed differences in the spectral behavior of the soil classes, mainly among Argissolos and the others soils. The NMI and U similarity coefficients showed values of 0.74 and 0.64, respectively, suggesting good similarity between the numerical and SiBCS classes. For example, numerical classification correctly distinguished Argissolos from Latossolos and Nitossolos. However, this mathematical technique was not able to distinguish Latossolos from Nitossolos Vermelho férricos, but the Cambissolos were well differentiated from other soil classes. The numerical technique proved to be effective and applicable to the soil classification process.

Hydrological processes obtained on the plot scale under four simulated rainfall tests during the cycle of different crop systems

The cropping system influences the interception of water by plants, water storage in depressions on the soil surface, water infiltration into the soil and runoff. The aim of this study was to quantify some hydrological processes under no tillage cropping systems at the edge of a slope, in 2009 and 2010, in a Humic Dystrudept soil, with the following treatments: corn, soybeans, and common beans alone; and intercropped corn and common bean. Treatments consisted of four simulated rainfall tests at different times, with a planned intensity of 64 mm h-1 and 90 min duration. The first test was applied 18 days after sowing, and the others at 39, 75 and 120 days after the first test. Different times of the simulated rainfall and stages of the crop cycle affected soil water content prior to the rain, and the time runoff began and its peak flow and, thus, the surface hydrological processes. The depth of the runoff and the depth of the water intercepted by the crop + soil infiltration + soil surface storage were affected by the crop systems and the rainfall applied at different times. The corn crop was the most effective treatment for controlling runoff, with a water loss ratio of 0.38, equivalent to 75 % of the water loss ratio exhibited by common bean (0.51), the least effective treatment in relation to the others. Total water loss by runoff decreased linearly with an increase in the time that runoff began, regardless of the treatment; however, soil water content on the gravimetric basis increased linearly from the beginning to the end of the rainfall.

Physical quality of an Oxisol under an integrated crop-livestock-forest system in the Brazilian Cerrado

Soil physical quality is an important factor for the sustainability of agricultural systems. Thus, the aim of this study was to evaluate soil physical properties and soil organic carbon in a Typic Acrudox under an integrated crop-livestock-forest system. The experiment was carried out in Mato Grosso do Sul, Brazil. Treatments consisted of seven systems: integrated crop-livestock-forest, with 357 trees ha-1 and pasture height of 30 cm (CLF357-30); integrated crop-livestock-forest with 357 trees ha-1 and pasture height of 45 cm (CLF357-45); integrated crop-livestock-forest with 227 trees ha-1 and pasture height of 30 cm (CLF227-30); integrated crop-livestock-forest with 227 trees ha-1 and pasture height of 45 cm (CLF227-45); integrated crop-livestock with pasture height of 30 cm (CL30); integrated crop-livestock with pasture height of 45 cm (CL45) and native vegetation (NV). Soil properties were evaluated for the depths of 0-10 and 10-20 cm. All grazing treatments increased bulk density (r b) and penetration resistance (PR), and decreased total porosity (¦t) and macroporosity (¦ma), compared to NV. The values of r b (1.18-1.47 Mg m-3), ¦ma (0.14-0.17 m³ m-3) and PR (0.62-0.81 MPa) at the 0-10 cm depth were not restrictive to plant growth. The change in land use from NV to CL or CLF decreased soil organic carbon (SOC) and the soil organic carbon pool (SOCpool). All grazing treatments had a similar SOCpool at the 0-10 cm depth and were lower than that for NV (17.58 Mg ha-1).

Quality of surface water related to land use: a case study in a catchment with small farms and intensive vegetable crop production in southern Brazil

Water degradation is strongly related to agricultural activity. The aim of this study was to evaluate the influence of land use and some environmental components on surface water quality in the Campestre catchment, located in Colombo, state of Parana, Brazil. Physical and chemical attributes were analyzed (total nitrogen, ammonium, nitrate, total phosphorus, electrical conductivity, pH, temperature, turbidity, total solids, biological oxygen demand, chemical oxygen demand and dissolved oxygen). Monthly samples of the river water were taken over one year at eight monitoring sites, distributed over three sub-basins. Overall, water quality was worse in the sub-basin with a higher percentage of agriculture, and was also affected by a lower percentage of native forest and permanent preservation area, and a larger drainage area. Water quality was also negatively affected by the presence of agriculture in the riparian zone. In the summer season, probably due to higher rainfall and intensive soil use, a higher concentration of total nitrogen and particulate nitrogen was observed, as well as higher electrical conductivity, pH and turbidity. All attributes, except for total phosphorus, were in compliance with Brazilian Conama Resolution Nº 357/2005 for freshwater class 1. However, it should be noted that these results referred to the base flow and did not represent a discharge condition since most of the water samples were not collected at or near the rainfall event.