[Recueil. Album provenant de la famille Grisi-Gautier-Bergerat]

Ancien possesseur : Gautier, Judith (1845-1917)

The effects of a non-woven cover in combination with different soil mulches in strawberry cultivation

Selostus: Katteiden käyttö mansikanviljelyssä

Domain Adaptation in remote sensing: increasing the portability of land-cover classifiers

Among the types of remote sensing acquisitions, optical images are certainly one of the most widely relied upon data sources for Earth observation. They provide detailed measurements of the electromagnetic radiation reflected or emitted by each pixel in the scene. Through a process termed supervised land-cover classification, this allows to automatically yet accurately distinguish objects at the surface of our planet. In this respect, when producing a land-cover map of the surveyed area, the availability of training examples representative of each thematic class is crucial for the success of the classification procedure. However, in real applications, due to several constraints on the sample collection process, labeled pixels are usually scarce. When analyzing an image for which those key samples are unavailable, a viable solution consists in resorting to the ground truth data of other previously acquired images. This option is attractive but several factors such as atmospheric, ground and acquisition conditions can cause radiometric differences between the images, hindering therefore the transfer of knowledge from one image to another. The goal of this Thesis is to supply remote sensing image analysts with suitable processing techniques to ensure a robust portability of the classification models across different images. The ultimate purpose is to map the land-cover classes over large spatial and temporal extents with minimal ground information. To overcome, or simply quantify, the observed shifts in the statistical distribution of the spectra of the materials, we study four approaches issued from the field of machine learning. First, we propose a strategy to intelligently sample the image of interest to collect the labels only in correspondence of the most useful pixels. This iterative routine is based on a constant evaluation of the pertinence to the new image of the initial training data actually belonging to a different image. Second, an approach to reduce the radiometric differences among the images by projecting the respective pixels in a common new data space is presented. We analyze a kernel-based feature extraction framework suited for such problems, showing that, after this relative normalization, the cross-image generalization abilities of a classifier are highly increased. Third, we test a new data-driven measure of distance between probability distributions to assess the distortions caused by differences in the acquisition geometry affecting series of multi-angle images. Also, we gauge the portability of classification models through the sequences. In both exercises, the efficacy of classic physically- and statistically-based normalization methods is discussed. Finally, we explore a new family of approaches based on sparse representations of the samples to reciprocally convert the data space of two images. The projection function bridging the images allows a synthesis of new pixels with more similar characteristics ultimately facilitating the land-cover mapping across images.

Physical quality of an Oxisol cultivated with maize submited to cover crops in the pre-cropping period

The intensive use of land alters the distribution of the pore size which imparts consequences on the soil physical quality. The Least Limiting Water Range (LLWR) allows for the visualization of the effects of management systems upon either the improvement or the degradation of the soil physical quality. The objective of this study was to evaluate the physical quality of a Red Latosol (Oxisol) submited to cover crops in the period prior to the maize crop in a no-tillage and conventional tillage system, using porosity, soil bulk density and the LLWR as attributes. The treatments were: conventional tillage (CT) and a no-tillage system with the following cover crops: sunn hemp (Crotalaria juncea L.) (NS), pearl millet (Pennisetum americanum (L.) Leeke) (NP) and lablab (Dolichos lablab L.) (NL). The experimental design was randomized blocks in subdivided plots with six replications, with the plots being constituted by the treatments and the subplots by the layers analyzed. The no-tillage systems showed higher total porosity and soil organic matter at the 0-0.5 m layer for the CT. The CT did not differ from the NL or NS in relation to macroporosity. The NP showed the greater porosity, while CT and NS presented lower soil bulk density. No < 10 % airing porosity was found for the treatments evaluated, and value for water content where soil aeration is critical (θPA) was found above estimated water content at field capacity (θFC) for all densities. Critical soil bulk density was of 1.36 and 1.43 Mg m-3 for NP and CT, respectively. The LLWR in the no-tillage systems was limited in the upper part by the θFC, and in the bottom part, by the water content from which soil resistance to penetration is limiting (θPR). By means of LLWR it was observed that the soil presented good physical quality.

Morphostructural characterization of soil conventionally tilled with mechanized and animal traction with and without cover crop

The structural stability and restructuring ability of a soil are related to the methods of crop management and soil preparation. A recommended strategy to reduce the effects of soil preparation is to use crop rotation and cover crops that help conserve and restore the soil structure. The aim of this study was to evaluate and quantify the homogeneous morphological units in soil under conventional mechanized tillage and animal traction, as well as to assess the effect on the soil structure of intercropping with jack bean (Canavalia ensiformis L.). Profiles were analyzed in April of 2006, in five counties in the Southern-Central region of Paraná State (Brazil), on family farms producing maize (Zea mays L.), sometimes intercropped with jack bean. The current structures in the crop profile were analyzed using Geographic Information Systems (GIS) and subsequently principal component analysis (PCA) to generate statistics. Morphostructural soil analysis showed a predominance of compact units in areas of high-intensity cultivation under mechanized traction. The cover crop did not improve the structure of the soil with low porosity and compact units that hamper the root system growth. In areas exposed to animal traction, a predominance of cracked units was observed, where roots grew around the clods and along the gaps between them.

Nitrogen washing from C3 and C4 cover grasses residues by rain

Crop species with the C4 photosynthetic pathway are more efficient in assimilating N than C3 plants, which results in different N amounts prone to be washed from its straw by rain water. Such differences may affect N recycling in agricultural systems where these species are grown as cover crops. In this experiment, phytomass production and N leaching from the straw of grasses with different photosynthetic pathways were studied in response to N application. Pearl millet (Pennisetum glaucum) and congo grass (Brachiaria ruziziensis) with the C4 photosynthetic pathway, and black oat (Avena Strigosa) and triticale (X Triticosecale), with the C3 photosynthetic pathway, were grown for 47 days. After determining dry matter yields and N and C contents, a 30 mm rainfall was simulated over 8 t ha-1 of dry matter of each plant residue and the leached amounts of ammonium and nitrate were determined. C4 grasses responded to higher fertilizer rates, whereas N contents in plant tissue were lower. The amount of N leached from C4 grass residues was lower, probably because the C/N ratio is higher and N is more tightly bound to organic compounds. When planning a crop rotation system it is important to take into account the difference in N release of different plant residues which may affect N nutrition of the subsequent crop.