Effects of row spacing and nitrogen topdressing fertilization on the yield of upland rice in a no-tillage system.

This study aimed to evaluate the effect of row spacing and nitrogen topdressing fertilization of two materials (genotype 07SEQCL441 CL and cultivar BRS Esmeralda) on the plant height, yield components, grain yield, and quality of an upland rice crop grown in a no-tillage system. Trials were conducted for two growing seasons under field conditions in a 3 x 4 factorial, randomized, complete block design, with four replications. For each material, treatments consisted of the combination of row spacing (0.225, 0.35, and 0.45 m) with nitrogen (N) applied as topdressing (0, 50, 100, and 150 kg ha-1). The lowest row spacing (0.225 m) for genotypes 07SEQCL441 CL and BRS Esmeralda provided a higher number of tillers, number of panicles m-2, and grain yield of rice. Increasing rates of N in the topdressing improved the rice grain yield for both cultivars, but for 07SEQCL441 CL, the grain yield was positively affected only to applications up to 50 kg N ha-1. Row spacing and N rates did not affect the rice grain quality. Therefore, these results indicate that the narrowest row spacing used (0.225 m) with N fertilization as topdressing increased the rice grain yield most in the no-tillage system.

Comparison of multitemporal MODIS-EVI smoothing algorithms and its contribution to crop monitoring.

Crop monitoring and more generally land use change detection are of primary importance in order to analyze spatio-temporal dynamics and its impacts on environment. This aspect is especially true in such a region as the State of Mato Grosso (south of the Brazilian Amazon Basin) which hosts an intensive pioneer front. Deforestation in this region as often been explained by soybean expansion in the last three decades. Remote sensing techniques may now represent an efficient and objective manner to quantify how crops expansion really represents a factor of deforestation through crop mapping studies. Due to the special characteristics of the soybean productions' farms in Mato Grosso (area varying between 1000 hectares and 40000 hectares and individual fields often bigger than 100 hectares), the Moderate Resolution Imaging Spectroradiometer (MODIS) data with a near daily temporal resolution and 250 m spatial resolution can be considered as adequate resources to crop mapping. Especially, multitemporal vegetation indices (VI) studies have been currently used to realize this task [1] [2]. In this study, 16-days compositions of EVI (MODQ13 product) data are used. However, although these data are already processed, multitemporal VI profiles still remain noisy due to cloudiness (which is extremely frequent in a tropical region such as south Amazon Basin), sensor problems, errors in atmospheric corrections or BRDF effect. Thus, many works tried to develop algorithms that could smooth the multitemporal VI profiles in order to improve further classification. The goal of this study is to compare and test different smoothing algorithms in order to select the one which satisfies better to the demand which is classifying crop classes. Those classes correspond to 6 different agricultural managements observed in Mato Grosso through an intensive field work which resulted in mapping more than 1000 individual fields. The agricultural managements above mentioned are based on combination of soy, cotton, corn, millet and sorghum crops sowed in single or double crop systems. Due to the difficulty in separating certain classes because of too similar agricultural calendars, the classification will be reduced to 3 classes : Cotton (single crop), Soy and cotton (double crop), soy (single or double crop with corn, millet or sorghum). The classification will use training data obtained in the 2005-2006 harvest and then be tested on the 2006-2007 harvest. In a first step, four smoothing techniques are presented and criticized. Those techniques are Best Index Slope Extraction (BISE) [3], Mean Value Iteration (MVI) [4], Weighted Least Squares (WLS) [5] and Savitzky-Golay Filter (SG) [6] [7]. These techniques are then implemented and visually compared on a few individual pixels so that it allows doing a first selection between the five studied techniques. The WLS and SG techniques are selected according to criteria proposed by [8]. Those criteria are: ability in eliminating frequent noises, conserving the upper values of the VI profiles and keeping the temporality of the profiles. Those selected algorithms are then programmed and applied to the MODIS/TERRA EVI data (16-days composition periods). Tests of separability are realized based on the Jeffries-Matusita distance in order to see if the algorithms managed in improving the potential of differentiation between the classes. Those tests are realized on the overall profile (comprising 23 MODIS images) as well as on each MODIS sub-period of the profile [1]. This last test is a double interest process because it allows comparing the smoothing techniques and also enables to select a set of images which carries more information on the separability between the classes. Those selected dates can then be used to realize a supervised classification. Here three different classifiers are tested to evaluate if the smoothing techniques as a particular effect on the classification depending on the classifiers used. Those classifiers are Maximum Likelihood classifier, Spectral Angle Mapper (SAM) classifier and CHAID Improved Decision tree. It appears through the separability tests on the overall process that the smoothed profiles don't improve efficiently the potential of discrimination between classes when compared with the original data. However, the same tests realized on the MODIS sub-periods show better results obtained with the smoothed algorithms. The results of the classification confirm this first analyze. The Kappa coefficients are always better with the smoothing techniques and the results obtained with the WLS and SG smoothed profiles are nearly equal. However, the results are different depending on the classifier used. The impact of the smoothing algorithms is much better while using the decision tree model. Indeed, it allows a gain of 0.1 in the Kappa coefficient. While using the Maximum Likelihood end SAM models, the gain remains positive but is much lower (Kappa improved of 0.02 only). Thus, this work's aim is to prove the utility in smoothing the VI profiles in order to improve the final results. However, the choice of the smoothing algorithm has to be made considering the original data used and the classifier models used. In that case the Savitzky-Golay filter gave the better results.

Soil management, seed treatment and soil compaction on the sowing furrows affect grain yields of upland rice genotypes.

The water availability for flood irrigated rice (Oryza sativa L.) is decreasing worldwide. Therefore, developing technologies to allow growing rice in aerobic condition, such as a no-tillage system (NTS) can contribute to produce upland rice grains without yield losses and also in saving more water. The objective of this study was to determine the effect of soil management, seed treatment and compaction on the sowing furrow on grain yield of upland rice genotypes. We made two trials, one in an NTS and another using conventional tillage, CT (one plowing and two diskings). The field experiments were performed in the Central Region of Brazil in Cerrado soils. For each trial, the experimental design was a randomized block design in a factorial scheme, with three replications. The treatments consisted of a combination of 10 genotypes with 2 compaction pressures on the sowing furrow (25 kPa and 126kPa) and 2 types of seed treatment (with and without pesticide). Under CT, the seed treatment did not contribute to increase upland rice grain yields. However, under NTS the grain yield of some genotypes [BRS Esmeralda (from 723 to 1,766 kg ha-1), BRS Pepita (from 930 to 1,874 kg ha-1), AB072044 (from 523 to 1,579 kg ha-1), and AB072085 (from 632 to 1,636 kg ha-1) at 25 kPA soil compaction pressure, and Sertaneja (from 994 to 2,167 kg ha-1), BRS Pepita (from 1,161 to 2,100 kg ha-1), and AB072085 (from 958 to 2,213 kg ha-1), at 126 kPA soil compaction pressure] increased with the use of this practice. At CT the higher soil compaction pressure on the sowing furrow (from 25 kPa to 126 kPa) increased rice grain yield only when it was used seed treatment and the genotypes Serra Dourada (from 1,239 to 2,178 kg ha-1), Sertaneja (from 1,510 to 2,379 kg ha-1), and Cambará (from 1,877 to 2,831 kg ha-1). On the other hand, under NTS, increasing soil compaction pressure on the sowing furrow allowed for an increased rice grain yield of Serra Dourada (from 1,553 to 2,347 kg ha-1), Esmeralda (from 723 to 1,643 kg ha-1), AB072044 (from 523 to 2,040 kg ha-1), and Cambará (from 1,243 to 2,032 kg ha-1) without seed treatment and Sertaneja (from 1,385 to 2,167 kg ha-1) and AB072044 (from 1,579 to 2,356 kg ha-1) with seed treatment. In CT the most productive genotypes were AB062008 (2,714 kg ha-1) and BRSMG Caravera (2,479 kg ha-1), while at NTS were the genotypes: BRSGO Serra Dourada (2,118 kg ha-1), AB072047 (1,888 kg ha-1), AB062008 (1,823 kg ha-1), BRSMG Caravera (1,737 kg ha-1), Cambará (1,716 kg ha-1), AB072044 (1,625 kg ha-1), BRS Esmeralda (1,604 kg ha-1), and BRS Pepita (1,516 kg ha-1).

Soil attributes and grain yield of upland rice as affected by cover crops.

The aim of this study was to determine the effect of pearl millet intercropped with other cover crops on mineral forms of N and urease activity in soil, nitrate reductase activity in the leaves of the follow-up rice crop, as well as the yield components of this rice crop. The experiment was performed in the year 2012/2013 at two locations of the Brazilian Cerrado.

Rice grain yield as affected by grain-producing cover crops in Cabo Delgado, Mozambique.

The aim of this study was to evaluate the production of biomass and grain cover crops, yield components, and grain yield of rice in Mozambique. The study was conducted in two sites located in the province of Cabo Delgado, in Mozambique.

Overexpression and characterization of rice Phosphorus-Starvation Tolerance 1 gene and its sorghum and maize homologs in transgenic tobacco.

2016