Flood-irrigated Rice Research Articles

Análises produtiva e econômica de lavouras de soja em terras baixas

Abstract The objective of this work was to estimate the relative yield that maximizes the profitability of the soybean crop in rotation with flood-irrigated rice. For this, 13 high-yield areas (from 2.6 to 5.2 ha) in lowland soybean-rice systems in Southern Brazil were selected before sowing. The calculation of production costs included seeds, seed treatment, fertilizers, lime, pesticides, irrigation, land, operational outsourcing, labor, and fuel consumption. The observed yield was transformed into relative yield by multiplying the quotient of the observed yield by the yield potential estimated by the CSM-CROPGRO-Soybean model. Water productivity was calculated as the ratio between the observed yield and available water during the crop cycle. Yield potential ranged from 6.1 to 7.4 Mg ha−1, whereas relative yield ranged from 45.3 to 101.2%. In addition, costs ranged from US$564.86 to US$1,122.86 per hectare, and profitability from US$767.18 to US$3,149.75 per hectare. The highest profitability of the soybean crop in rotation with flood-irrigated rice occurs with a relative yield between 67 and 84%.

Flood-Irrigated Rice Response to Fertilizer-Phosphorus Sources in a Phosphorus-Deficient Silt-Loam Soil

Flood-Irrigated Rice Response to Fertilizer-Phosphorus Sources in a Phosphorus-Deficient Silt-Loam Soil

Seasonal and annual methane and nitrous oxide emissions affected by tillage and cover crops in flood-irrigated rice

Soil tillage plays an important role in methane (CH4) and nitrous oxide (N2O) emissions in flood-irrigated rice production. Hence, this study measured the effects of soil tillage and cover crops on the seasonal and annual CH4 and N2O emissions in flood-irrigated rice. A field experiment was conducted for two agricultural years with conventional tillage (CT), minimum tillage (MT), no-tillage (NT), and NT with Persian clover (Trifolium resupinatum) (NT+PC). The effect of different tillage systems on CH4 emissions, which occur predominantly in rice cultivation, depends on the impact of tillage and the amount of straw remaining at the time of rice seeding. When straw incorporation in MT and CT reduces the quantity of straw remaining relative to NT, lower CH4 emissions occurred in the CT and MT. In contrast, when the amount of straw remaining on the soil is similar among the types of soil tillage, lower CH4 emissions occurred in the NT. The effect of tillage on annual N2O emissions was unclear, although the highest in-season N2O emissions occurred in the NT, where the highest water-filled pore space values were recorded in both years. High N2O emissions during rice cultivation occurred in the CT after nitrogen fertilization. The effect of the cover crop in the NT varied as a function of the performance of the clover. When the PC biomass production was satisfactory in the second year, annual CH4 and N2O emissions increased by 8 % and 23 %, respectively. The seasonal and annual quantification of CH4 and N2O emissions is important to reduce the uncertainty of the effect of different land management systems on CH4 and N2O emissions.

Inter-relações genéticas de causa e efeito para atributos de qualidade de grãos de arroz irrigado

Abstract The objective of this work was to determine the genetic correlations and the direct and indirect associations of agronomic traits and grain quality attributes with the percentage of whole grains in flood irrigated rice. The experiment was carried out in two environments, in a randomized complete block design with three replicates, using 23 irrigated rice genotypes. The evaluated traits were: percentage of whole grains, caryopsis length, caryopsis width, panicle length, panicle weight, 1,000 grain weight, days to flowering, percentage of chalky grains with white belly, total chalky area, total whiteness, vitreous whiteness, and defects in coloring. The percentage of grains with white belly and total chalky area were positively correlated, whereas the percentage of grains with white belly and vitreous whiteness were negatively correlated. The traits caryopsis width, percentage of chalky grains with white belly, panicle weight, and 1,000 grain weight showed indirect effects on whole-grain yield response according to total chalky area and total whiteness. Total chalky area and total whiteness are the factors that most negatively influence the percentage of whole grains according to the genotypic correlations and direct effects.

Evaluation of the Agronomic Efficiency of Azospirillum brasilense Strains Ab-V5 and Ab-V6 in Flood-Irrigated Rice

The rational use of nitrogen fertilization is fundamental, not only to increase recovery efficiency, but also to increase crop productivity and reduce the production costs and risks of environmental impacts. In the State of Rio Grande do Sul in Brazil, irrigated rice productivity can surpass 8 tonne·ha−1 as a result of the technification of the crop and favorable environmental conditions, yet there is great variability in the agronomic efficiency of chemical nitrogen fertilizers, which rarely exceed 50% of the applied dose. Biological nitrogen fixation is one of the technological alternatives for reducing the use of nitrogen in this crop. In this study, the agronomic efficiency of Azospirillum brasilense strains Ab-V5 and Ab-V6 in terms of biological nitrogen fixation in flood-irrigated rice cultivars in a lowland agroecosystem was evaluated through five field experiments. A. brasilense combined with reduced nitrogen fertilization (reduction of 30 kg N·ha−1) increased the dry mass of the aerial part of rice plants by 3.2%, and promoted an increase in N concentration in stems and leaves and in the N content exported by grains by 43% and 27.5%, respectively, in relation to the absence of N and inoculant, and promoted an average increase of 30% in rice production.

Computational intelligence to study the importance of characteristics in flood-irrigated rice

The study of traits in crops enables breeders to guide strategies for selecting and accelerating the progress of genetic breeding. Although the simultaneous evaluation of characteristics in the plant breeding programme provides large quantities of information, identifying which phenotypic characteristic is the most important is a challenge facing breeders. Thus, this work aims to quantify the best approaches for prediction and establish a network of better predictive power in flood-irrigated rice via methodologies based on regression, artificial intelligence, and machine learning. Multiple regression, computational intelligence, and machine learning were used to predict the importance of the characteristics. Computational intelligence and machine learning were notable for their ability to extract nonlinear information from model inputs. Predicting the relative contribution of auxiliary characteristics in rice through computational intelligence and machine learning proved to be efficient in determining the relative importance of variables in flood-irrigated rice. The characteristics indicated to assist in decision making are flowering, number of grains filled by panicles and length of panicles for this study. The network with only one hidden layer with 15 neurons was observed to be efficient in determining the relative importance of variables in flooded rice.

Comparative economic and environmental assessments of furrow- and flood-irrigated rice production systems

Rice producers in the Mid-South of the United States face decreasing groundwater availability and increasing pumping depths which may jeopardize sustainable rice production. Recently, producers have started adopting furrow irrigated rice (FIR) which reduces irrigation requirements by approximately 17 % compared to conventionally flooded rice (CIR). Although driven by water savings and ease of planting and harvesting, as FIR does not require levies to be built, there are both yield (quantity) and milling (quality) concerns with FIR production. While some view FIR as a more sustainable production practice because of its reduced water usage, a comprehensive Life Cycle Analysis (LCA) has not been conducted to compare the two production systems. This study compares the economic (yield differences, NPV of water conserved and milling discounts/premiums) and environmental (a stepwise LCA which monetizes the holistic environmental of CIR and FIR on a per kg of rice basis) differences between CIR and FIR. Profitability results indicate that CIR more profitable and LCA results show that CIR is more environmentally sustainable. These results present a Gordian knot for policy makers and rice stakeholders alike as they struggle between a production practice that has a lower environmental impact (CIR) versus one that conserves water, reduces soil disturbance, and offers more flexibility to respond to market and weather conditions (FIR). Water savings has historically been a key focus of rice production from an environmental sustainability standpoint. Our research indicates that failure to both recognize and quantify holistic environmental metrics besides water savings may provide misleading recommendations.

Multiple-trait model through Bayesian inference applied to flood-irrigated rice (Oryza sativa L)

The objectives of this study were to use a bayesian multi-trait model, estimate genetic parameters, and select flood-irrigated rice genotypes with better genetic potentials in different evaluation environments. For this, twenty-five rice genotypes and six traits belonging to the flood-irrigated rice improvement program were evaluated. The experimental design used in all experiments was a randomized block design with three replications. The Monte Carlo Markov Chain algorithm estimated genetic parameters and genetic values. The grain thickness trait was considered highly heritable, with a credibility interval ranging from: $${h}^{2}$$ : 0.9480; 0.9440; 0.8610, in environments 1, 2, and 3, respectively. The grain yields showed a weak correlation estimate between grain thickness and 100-grain weight, in all environments, with a credibility interval ranging from ( $$\rho$$ = 0.5477; 0.5762; 0.5618 and 0.5973; 0.5247; 0.5632, grain thickness and 100-grain weight, in environments 1, 2, and 3, respectively). The Bayesian multi-trait model proved to be an adequate strategy for the genetic improvement of flood-irrigated.

Assessment of Struvite as an Alternative Sources of Fertilizer-Phosphorus for Flood-Irrigated Rice

Phosphorus (P) recovery from wastewaters as struvite (MgNH4PO4·6H2O) may be a viable alternative fertilizer-P source for agriculture. The objective of this study was to evaluate the economic and environmental implications of struvite as a fertilizer-P source for flood-irrigated rice (Oryza sativa) relative to other commonly used commercially available fertilizer-P sources. A field study was conducted in 2019 and 2020 to evaluate the effects of wastewater-recovered struvite (chemically precipitated struvite (CPST) and electrochemically precipitated struvite (ECST)) on rice yield response in a P-deficient, silt–loam soil in eastern Arkansas relative to triple superphosphate, monoammonium and diammonium phosphate, and rock phosphate. A life cycle assessment methodology was used to estimate the global warming potentials associated with rice produced with the various fertilizer-P sources. Life cycle inventory data were based on the field trials conducted with and without struvite application for both years. A partial budget analysis showed that, across both years, net revenues for ECST and CPST were 1.4 to 26.8% lower than those associated with the other fertilizer-P sources. The estimated greenhouse gas emissions varied between 0.58 and 0.70 kg CO2 eq kg rice−1 from CPST and between 0.56 and 0.81 kg CO2 eq kg rice−1 from ECST in 2019 and 2020, respectively, which were numerically similar to those for the other fertilizer-P sources in 2019 and 2020. The similar rice responses compared to commercially available fertilizer-P sources suggest that wastewater-recovered struvite materials might be an alternative fertilizer-P-source option for flood-irrigated rice production if struvite can become price-competitive to other fertilizer-P sources.

Multi-trait and multi-environment Bayesian analysis to predict the G x E interaction in flood-irrigated rice.

The biggest challenge for the reproduction of flood-irrigated rice is to identify superior genotypes that present development of high-yielding varieties with specific grain qualities, resistance to abiotic and biotic stresses in addition to superior adaptation to the target environment. Thus, the objectives of this study were to propose a multi-trait and multi-environment Bayesian model to estimate genetic parameters for the flood-irrigated rice crop. To this end, twenty-five rice genotypes belonging to the flood-irrigated rice breeding program were evaluated. Grain yield and flowering were evaluated in the agricultural year 2017/2018. The experimental design used in all experiments was a randomized block design with three replications. The Markov Chain Monte Carlo algorithm was used to estimate genetic parameters and genetic values. The flowering is highly heritable by the Bayesian credibility interval: h2 = 0.039-0.80, and 0.02-0.91, environment 1 and 2, respectively. The genetic correlation between traits was significantly different from zero in the two environments (environment 1: -0.80 to 0.74; environment 2: -0.82 to 0.86. The relationship of CVe and CVg higher for flowering in the reduced model (CVg/CVe = 5.83 and 13.98, environments 1 and 2, respectively). For the complete model, this trait presented an estimate of the relative variation index of: CVe = 4.28 and 4.21, environments 1 and 2, respectively. In summary, the multi-trait and multi-environment Bayesian model allowed a reliable estimate of the genetic parameter of flood-irrigated rice. Bayesian analyzes provide robust inference of genetic parameters. Therefore, we recommend this model for genetic evaluation of flood-irrigated rice genotypes, and their generalization, in other crops. Precise estimates of genetic parameters bring new perspectives on the application of Bayesian methods to solve modeling problems in the genetic improvement of flood-irrigated rice.

Water Productivity of Furrow, Drip and Flood Irrigated Rice under Loam Soil

Water Productivity of Furrow, Drip and Flood Irrigated Rice under Loam Soil

Comparative Economic and Environmental Assessments of Furrow- and Flood-Irrigated Rice Production Systems

Comparative Economic and Environmental Assessments of Furrow- and Flood-Irrigated Rice Production Systems

Multi-information analysis for recommendation of flooded-irrigated rice for adaptability and phenotypic stability

The GxE interaction is one of the major difficulties of plant breeding programs, both in the selection phase and in the recommendation of cultivars. To assess adaptability and stability, various statistical methods are used. The simultaneous use of some methodologies, using multi-information criteria for cultivar’s recommendation, can extract information that cannot be observed using each methodology separately. The aim of this work was to perform a large description of the behavior of flooded-irrigated rice genotypes, responding to environmental variations, using methods already established in the literature, but exploring the particularities of each methodology that together establish an information criterion for cultivar recommendation. To this end, 18 rice genotypes belonging to flood-irrigated rice breeding program were evaluated over four agricultural years, 2012/2013 to 2015/2016, totaling 12 environments (3 sites × 4 years). Multi-information estimates were performed to adaptability and stability analysis. There was no sign for the effect of the genotypes, and there was the significance of the effects of environment and GxE interaction. The aggregation of information and the large description of the behavior of the flooded rice genotypes demonstrated to be an efficient tool for studies of adaptability and stability.

The phosphoproteome of rice leaves responds to water and nitrogen supply.

The scarcity of freshwater is an increasing concern in flood-irrigated rice, whilst excessive use of nitrogen fertilizers is costly and contributes to environmental pollution. To co-ordinate growth adaptation under prolonged exposure to limited water or excess nitrogen supply, plants employ complex systems for signalling and regulation of metabolic processes. There is limited information on the involvement of one of the most important post-translational modifications (PTMs), protein phosphorylation, in plant adaptation to long-term changes in resource supply. Oryza sativa cv. Nipponbare was grown under two regimes of nitrogen from the time of germination to final harvest. Twenty-five days after germination, water was withheld from half the pots in each nitrogen treatment and low water supply continued for an additional 26 days, while the remaining pots were well watered. Leaves from all four groups of plants were harvested after 51 days in order to test whether phosphorylation of leaf proteins responded to prior abiotic stress events. The dominant impact of these resources is exerted in leaves, where PTMs have been predicted to occur. Proteins were extracted and phosphopeptides were analysed by nanoLC-MS/MS analysis, coupled with label-free quantitation. Water and nitrogen regimes triggered extensive changes in phosphorylation of proteins involved in membrane transport, such as the aquaporin OsPIP2-6, a water channel protein. Our study reveals phosphorylation of several peptides belonging to proteins involved in RNA-processing and carbohydrate metabolism, suggesting that phosphorylation events regulate the signalling cascades that are required to optimize plant response to resource supply.

Economic and productive analysis of irrigated rice crops using a multicase study

Abstract: The objective of this work was to evaluate the relationship between grain yield and production costs of flood-irrigated rice (Oryza sativa) in the state of Rio Grande do Sul, Brazil, in order to determine the yield range that maximizes the profit of farmers. There are estimations pointing out that the yield range that maximizes the profit for farmers is about 75 and 85% of the yield potential; however, this still needs to be validated for irrigated rice in Brazil. To validate these values, a technical and economic monitoring was carried out for eight farmers of irrigated rice, whose production costs were measured and compared with the mean yield and the percentage of potential yield achieved. To determine the potential yield of each crop, SimulArroz, a process-based model developed for the simulation of rice growth and yield in the South of Brazil, was used. The grain yield achieved by the farmers ranged from 6.3 to 11.0 Mg ha-1 (48 to 83% of the potential yield), costs ranged from R$4,197.00 ha-1 to R$6,227.00 ha-1, and profit ranged from R$-52.00 ha-1 to R$3,599.00 ha-1. Farmers who reach between 69 and 83% of yield potential achieve the highest profitability. The investment planning based on the yield potential calculated by the SimulArroz model can be efficiently adopted for irrigated rice crops.

Patterns recognition methods to study genotypic similarity in flood-irrigated rice

ABSTRACT Genetic diversity studies are performed based on information on a set of traits measured in a group of genotypes, considering one or more environments. The pattern recognition methods allow classifying genotypes from a set of important agronomic information. Thus, this study aimed to present and compare pattern recognition methods to inquire about the similarity of environments and genotypes in flood-irrigated rice for the recommendation of cultivars. The experim>ents were performed in the municipalities of Leopoldina, Lambari, and Janauba, state of Minas Gerais, Brazil. To evaluate the pattern of similarity, 25 rice genotypes in three environments belonging to the flood-irrigated rice breeding program were used. Among these genotypes, five cultivars were used as an experimental control for the grain yield, the height of the plant, flowering, panicle length, grains filled by panicles, percentages of grains filled by panicles, in the 2012/2013 agricultural year. The methods us>ed were mixtures of multivariate normal distributions and density-based clustering algorithm. It was observed, therefore, that the genotypes are distributed in three distinct groups, in which there are intragroup homogeneity and intergroup heterogeneity for the agronomic traits of the flooded rice culture. The methods >used to assess the dissimilarity of environments using pattern recognition methods were efficient in classifying flooded rice irrigated environments.

Extended use of poultry manure as a nutrient source for flood-irrigated rice crop

Abstract: The objective of this work was to evaluate changes in the soil chemical properties, nitrogen use efficiency, and grain yield of flood-irrigated rice fields after the addition of poultry manure during five years. Trials were carried out in a randomized complete block design, with three replicates. The treatments were: chemical fertilizers, N-P2O5-K2O; control, no fertilizers; and poultry manure doses, 2.5, 5.0, and 10.0 Mg ha-1. Poultry manure increased soil pH, base saturation, and phosphorus, potassium, calcium, and zinc contents more than the chemical fertilizers. Adding poultry manure to the soil increased N ammonification (35 mg kg-1 NH4 +-N) from the first to the fourth week after rice was sown. The exclusive use of poultry manure was not sufficient to provide adequate nutrition to rice plants after the decrease in NH4 +-N content in the soil. The cumulative N uptake by plants and rice yield were lower with poultry manure application than with the chemical fertilizers. Adding poultry manure to flood-irrigated rice fields for five years alters soil chemical properties, improves fertility, does not favor nitrogen use efficiency by rice plants, and promotes a lower grain yield than the chemical fertilizers.

An update of new flood-irrigated rice cultivars in the SimulArroz model

Abstract: The objective of this work was to model, in the SimulArroz model, the three flood-irrigated rice (Oryza sativa) cultivars currently most grown in the state of Rio Grande do Sul, Brazil. The experiments to calibrate and validate the model were conducted in the municipalities of Cachoeirinha, Santa Maria, Uruguaiana, Santa Vitória do Palmar, and Cachoeira do Sul during four crop seasons. The number of leaves, phenology, aboveground dry matter biomass, and yield of each cultivar were evaluated. The results showed a slight overestimate of the R1, R4, and R9 stages; however, overall, the SimulArroz model had a good performance in simulating rice phenology for the three studied genotypes. Furthermore, the model had a reasonable accuracy in simulating aboveground dry matter and yield. The root-mean-square error (RMSE) for aboveground dry matter (leaves, stems, panicles, and grains) ranged from 0.5 to 3.0 Mg ha-1. For yield, the RMSE ranged from 0.8 to 1.3 Mg ha-1. The calibration of the SimulArroz model is efficient in simulating the growth, development, and grain yield of the most important flood-irrigated rice cultivars in Southern Brazil and can be used to estimate harvest forecast and yield potential, as well for yield gap studies.

Similarity networks for the classification of rice genotypes as to adaptability and stability

Abstract: The objective of this work was to evaluate the similarity network graphic methodology for the classification of flood-irrigated rice (Orzya sativa) genotypes regarding their adaptability and stability. Two statistical measures were used to represent the proximity of the behavior (based on Pearson’s correlation) or values (based on Gower’s distance) between pairs of genotypes or between genotype and environment. Productivity data of 18 genotypes were evaluated in three locations in the state of Minas Gerais, Brazil, in the harvests of 2012/2013, 2013/2014, 2014/2015, and 2015/2016, in a randomized complete block design. The genotypes were previously assessed for adaptability and stability by the Eberhart & Russell and centroid methods. The graphical representations provided by the similarity networks allowed to better identify the pattern of the genotype x environment interaction, overcoming the interpretation difficulties due to the disagreements between the results obtained by the Eberhart & Russell and centroid methods. The similarity networks improve genotype x environment interaction studies.

Introducing rice yield loss caused by weed competition into the SimulArroz model

Abstract: The objective of this work was to model weedy rice (Oryza sativa) and barnyard grass (Echinochloa spp.) competition with flood-irrigated rice, introducing it as a submodule into the SimulArroz model. The competition of both weeds with irrigated rice was modeled using the rectangular hyperbola equation. The “i” and “a” coefficients of the rectangular hyperbola for each of these weeds were obtained from the literature and from field experiments carried out in the state of Rio Grande do Sul, Brazil. In SimulArroz, yield loss was applied to penalize yield in all three technological levels (high, medium, and low) of physiological maturity. For weedy rice, the coefficient values of imean for the high, medium, and low technological levels were 1.04, 1.50, and 3.57 respectively, and, for barnyard grass, 4.70, 10.49, and 15.51 respectively. Coefficient “a” was 100 for weedy rice, and amean values for barnyard grass were 101.63, 104.92, and 96.88 for the high, medium, and low levels, respectively. The yield loss approach was suitable to model the competition of weedy rice and barnyard grass with irrigated rice. The submodule yield loss caused by the competition of weedy rice and barnyard grass with irrigated rice improves the predictive capacity of the SimulArroz model.