Repeated Manure Application for Eleven Years Stimulates Enzymatic Activities and Improves Soil Attributes in a Typic Hapludalf

Animal manure may be a valuable resource for the development of agricultural sustainability. We proposed to verify the feasibility of applications of three types of animal manures to improve soil attributes and to sustain crop yields under intensive cropping and no-tillage systems. The field experiment was established in 2004 on Typic Hapludalf soil with pig slurry (PS), cattle slurry (CS), pig deep-litter (PL), mineral fertilizer (MF) and a non-fertilized treatment. From 2004 to 2015, were grown black oat, maize, forage turnip, black beans, and wheat. Soil samples were taken after winter 2014 and summer 2015, and submitted to chemical, physical, microbiological and biochemical analyses. Animal manures increased soil pH, but MF caused acidification of soil. The PL and CS applications reduced soil density, and increased total pore volume and hydraulic conductivity. Animal manures increased soil P fractions, total organic carbon, total nitrogen, stimulated soil respiration, and had higher activities of glucosidase and acid phosphatase. Wheat had its biggest dry matter and grain yields with MF, but maize grain yields with CS were higher than MF. All indicators pointed that application of animal manure converges to an interesting strategy to recycle nutrients at farmyard level and to contribute to global sustainability.

Mycorrhizal Association in Wheat Genotypes Submitted to Variable Irrigation in the Brazilian Cerrado

Mycorrhizal association contributes to plant growth, influencing tolerance to abiotic stresses such as water deficit. There is considerable variation in infection by arbuscular mycorrhizal fungi (AMF) in cultivars of the same crop, but there is little information regarding these differences in wheat. The objective of this work was to evaluate the influence of water deficit on the arbuscular mycorrhizal association in wheat genotypes in the Cerrado region and the association between soil attributes and mycorrhizal colonization. The experiment was conducted in a no-till system, using different water regimes. The experimental design was a randomized block with subdivided plots scheme, with 12 treatments and 3 repetitions. The plots consisted of 4 wheat genotypes and the subplots included 3 water regimes. Mycorrhizal colonization, soil microbial biomass carbon, total soil organic carbon, easily extractable glomalin-related soil protein, spore number and AMF species diversity were evaluated. Mycorrhizal colonization was not influenced by wheat genotypes, but it was favored by the higher water regime, being 44.8% higher when compared to the lower water regime. The soil moisture was positively correlated with the soil attributes with the exception of the number of AMF spores. The community of AMF associated with wheat genotypes was similar, comprising of 12 species, predominantly Claroideoglomus etunicatum and Glomus macrocarpum. The low variation among wheat genotypes for AMF diversity suggests no selective influence of the plants on the AMF community in the area of the study. Water regime was shown to be a dominant factor in mycorrhizal association.

Definition of Optimal Maize Seeding Rates Based on the Potential Yield of Management Zones

The decision on crop population density should be a function of biotic and abiotic field parameters and optimize the site-specific yield potential, which can be a real challenge for farmers. The objective of this study was to investigate the yield of maize hybrids subjected to variable rate seeding (VRS) and in differentiated management zones (MZs). The experiment was conducted between 2013 and 2015 in a commercial field in the Central-West region of Brazil. First, MZ were delineated using the K-means algorithm with layers involving soil electrical conductivity, yield maps from previous years, and elevation. Seven maize hybrids at five seeding rates were evaluated in the context of each MZ and the cause-and-effect relationship with soil attributes was investigated. Optimal yields were obtained for crop population densities between 70,000 plants ha−1 and 80,000 plants ha−1. Hybrids which perform well under higher densities are key in achieving positive results using VRS. The plant population densities that resulted in maximum yields were obtained for densities at least 27% higher than the recommended seeding rates. The yield variance between MZs can be explained by the variance in soil attributes, while the yield variance within MZs can be explained by the variance in plant population densities. The study shows that on-farm experimentation can be key for obtaining information concerning yield potential. The management by VRS in different MZs is a low-cost technique that can reduce input application costs and optimize yield according to the site-specific potential of the field.

Evaluating the Precision and Accuracy of Proximal Soil vis–NIR Sensors for Estimating Soil Organic Matter and Texture

Measuring soil texture and soil organic matter (SOM) is essential given the way they affect the availability of crop nutrients and water during the growing season. Among the different proximal soil sensing (PSS) technologies, diffuse reflectance spectroscopy (DRS) has been deployed to conduct rapid soil measurements in situ. This technique is indirect and, therefore, requires site- and data-specific calibration. The quality of soil spectra is affected by the level of soil preparation and can be accessed through the repeatability (precision) and predictability (accuracy) of unbiased measurements and their combinations. The aim of this research was twofold: First, to develop a novel method to improve data processing, focusing on the reproducibility of individual soil reflectance spectral elements of the visible and near-infrared (vis–NIR) kind, obtained using a commercial portable soil profiling tool, and their direct link with a selected set of soil attributes. Second, to assess both the precision and accuracy of the vis–NIR hyperspectral soil reflectance measurements and their derivatives, while predicting the percentages of sand, clay and SOM content, in situ as well as in laboratory conditions. Nineteen locations in three agricultural fields were identified to represent an extensive range of soils, varying from sand to clay loam. All measurements were repeated three times and a ratio spread over error (RSE) was used as the main indicator of the ability of each spectral parameter to distinguish among field locations with different soil attributes. Both simple linear regression (SLR) and partial least squares regression (PLSR) models were used to define the predictability of % SOM, % sand, and % clay. The results indicated that when using a SLR, the standard error of prediction (SEP) for sand was about 10–12%, with no significant difference between in situ and ex situ measurements. The percentage of clay, on the other hand, had 3–4% SEP and 1–2% measurement precision (MP), indicating both the reproducibility of the spectra and the ability of a SLR to accurately predict clay. The SEP for SOM was only a quarter lower than the standard deviation of laboratory measurements, indicating that SLR is not an appropriate model for this soil property for the given set of soils. In addition, the MPs of around 2–4% indicated relatively strong spectra reproducibility, which indicated the need for more expanded models. This was apparent since the SEP of PLSR was always 2–3 times smaller than that of SLR. However, the relatively small number of test locations limited the ability to develop widely applicable calibration models. The most important finding in this study is that the majority of vis–NIR spectral measurements were sufficiently reproducible to be considered for distinguishing among diverse soil samples, while certain parts of the spectra indicate the capability to achieve this at α = 0.05. Therefore, the innovative methodology of evaluating both the precision and accuracy of DRS measurements will help future developers evaluate the robustness and applicability of any PSS instrument.