Interactions of Environmental Variables and Water Use Efficiency in the Matopiba Region via Multivariate Analysis

Santiago, Dimas de Barros; Barbosa, Humberto Alves; Filho, Washington Luiz Félix Correia; Júnior, José Francisco de Oliveira

doi:10.3390/su14148758

Cited by 7 publications

(5 citation statements)

References 63 publications

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“…Average production from 1981 to 2010 ranged from 521 kg/ha in the Centro-Norte baiano to 2158 kg/ha in the Extremo oeste baiano, around 650 kg/ha in the Vale São-Franciscano baiano, the Centro-Sul baiano and the Nordeste baiano, and around 820 kg/ha in the Metropolitana de Salvador and the Sul baiano. Due to its size and different forms of cultivation, Bahia is a classic example of a state that has three distinct productivity classes: high productivity in the far west, related to the area of the state that is part of MATOPIBA [57], moderate productivity in the Sul baiano and the Metropolitana de Salvador, and low productivity in the other mesoregions.…”

Section: Results By State-bahiamentioning

confidence: 99%

Predictive Potential of Maize Yield in the Mesoregions of Northeast Brazil

Silva,

Peixoto,

Costa

et al. 2024

AgriEngineering

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Most of the northeastern region of Brazil (NEB) has a maize production system based on family farming, with no technological advances and totally dependent on the natural rainfall regime, which is concentrated in 4 to 5 months in most parts of the region. This means that the productivity of this crop is low in the NEB. In the northern mesoregions of the NEB, rainfall is concentrated between January and June, in the east of the NEB from April to September, and in the west of the NEB from October to March. The growing season takes place during these semesters. With this in mind, our objective was to develop a model based on canonical correlation analysis (CCA) to predict corn production in the mesoregions of the NEB between 1981 and 2010, using accumulated precipitation per semester as the predictor variable and predicting the observed production in kg/ha. Our results showed that the CCA model presented higher correlations between observed and simulated production than that obtained simply from the direct relationship between accumulated rainfall and production. The other two metrics used, RMSE and NRMSE, showed that, on average, in most mesoregions, the simulation error was around 200 kg/ha, but the accuracy was predominantly moderate, around 29% in most mesoregions, with values below 20% in six mesoregions, indicative of better model accuracy, and above 50% in two mesoregions, indicative of low accuracy. In addition, we investigated how the different combinations between two modes of climate variability with a direct influence on precipitation in the NEB impacted production in these 30 years, with the combination of El Niño and a positive Atlantic dipole being the most damaging to harvests, while years when La Niña and a negative Atlantic dipole acted together were the most favorable. Despite the satisfactory results and the practical applicability of the model developed, it should be noted that the use of only one predictor, rainfall, is a limiting factor for better model simulations since other meteorological variables and non-climatic factors have a significant impact on crops. However, the simplicity of the model and the promising results could help agricultural managers make decisions in all the states that make up the NEB.

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Section: Results By State-bahiamentioning

confidence: 99%

Predictive Potential of Maize Yield in the Mesoregions of Northeast Brazil

Silva,

Peixoto,

Costa

et al. 2024

AgriEngineering

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“…With the temperature increase predicted for the coming decades (Assad et al, 2013; Reis et al, 2021; Torres et al, 2021), the demand for water is expected to be higher, and will have a direct influence on climate risk (Abreu et al, 2022b; Assad et al, 2013; Santiago et al, 2022). In the agroclimatic zoning for corn cultivation in Alagoas reported by Lima et al (2020), regions with greater fitness in the wettest months may have reduced apt area.…”

Section: Discussionmentioning

confidence: 99%

Long‐term trend analysis in annual, seasonal and monthly rainfall in East Northeast of Brazil and the influence of modes of climate variability

Abreu,

de Souza Fraga,

Lyra

et al. 2023

Intl Journal of Climatology

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The study of rainfall trends is crucial for food security and water availability in Alagoas state, Northeast of Brazil. In this work, monthly, seasonal and annual rainfall trends have been studied (1960–2016) for homogeneous rainfall regions over the eastern part of the Northeast Brazil (ENEB) and later related to climate variability. Cluster analysis was applied to identify homogeneous rainfall regions while the Mann–Kendall (MK), modified Mann–Kendall (MMK) and Pettitt tests were used in the analysis and identification of trends on a spatial and temporal scale. To relate rainfall and climate variability modes, Spearman's correlation was used in each homogeneous region. The rainfall series provided evidence of a general decrease in rainfall in the rainy period and an increase in the dry period, mainly over the driest region. The break points of time series occurred mostly in periods of great variations in values of modes of climate variability, especially the Monthly Niño3.4 Index and the Southern Oscillation Index (SOI), both having a robust influence across the region. Moreover, the probable rainfall in the time series with trends was different in most months before and after the breakpoint. After the breakpoint, probable rainfall was lower, influenced by the breakpoint year (size of the series before and after the breakpoint), which mainly occurred in the 1980s and 1990s and presented a warm phase and a greater number of El Niño events. The MK and MMK trend tests showed the ability to detect trends, although there is no established standard on which test or version to use due to self‐correlated, nonhomogeneous series with nonrandom or nonindependent data. Rainfall is an important variable for water and food security and in the monitoring of natural disasters. The changes detected in this study can be used as reference information for public policies on water resources and future studies for Alagoas and similar regions.

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“…[58] highlighted that, under a certain level of water stress, plants could achieve higher productivity with the same amount of water lost by ET or the same productivity with a lower ET due to the increase in local WUE. On the other hand, during the La Niña years (wet years), namely 2001, 2007, 2012, and 2016, there was a reduction of 70%, 93%, 82%, and 96% in WUE values, respectively, due to the above-mean total precipitation [59,60]. Ref.…”

Section: Spatio-temporal Distribution Of Wue Anomaliesmentioning

confidence: 98%

Variability of Water Use Efficiency Associated with Climate Change in the Extreme West of Bahia

et al. 2022

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Water has become more important in agricultural implementations over the years, as has the need for water management. Thus, Water Use Efficiency (WUE) has been used as an alternative form of detecting the variability of water management based on the carbon–water cycle. The study aimed to map and quantify the spatio-temporal distribution of WUE based on its interactions with environmental changes. It focused on an agricultural area in the westernmost region of Bahia, Northeast Brazil (NEB). For WUE estimation, data from Collection 6 MODIS Gross Primary Productivity (GPP) and Evapotranspiration (ET) products with a spatial resolution of 0.05° × 0.05° were obtained from the Earth Explorer website. Subsequently, annual WUE anomalies were calculated based on the 2001–2019 period. The results obtained indicated that the highest values of GPP (580 gC/m2), ET (3000 mm), and WUE (3.5 gC/mm·m2) occurred in agricultural areas, associated with cultural treatments and insertion of irrigation, which helped in the higher WUE values and consequently increased agricultural productivity in the study region. In addition, there was a marked influence of the phases of the climate variability mode—El Niño-Southern Oscillation (ENSO)—on the annual variability of the WUE, with a reduction of 96% during the La Niña of 2016 (an increase of 89% during El Niño of 2005). During El Niños, vegetation had greater efficiency resulting from the adaptation of vegetation in maintaining the carbon–water balance, using water more efficiently. However, unlike Las Niñas, with excessive precipitation there is an interference in the WUE, which interferes with the absorption of radiation and nutrients for the biophysical processes of vegetation and agriculture and, consequently, agricultural production. The use of WUE for agriculture is extremely important, especially for Brazil and countries with an economy based on primary production. This information on the way vegetation (native or agricultural) responds to interactions with the environment aids in decision-making about water management, possibly lowering losses or agricultural damage caused by a lack of water.

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Interactions of Environmental Variables and Water Use Efficiency in the Matopiba Region via Multivariate Analysis

Cited by 7 publications

References 63 publications

Predictive Potential of Maize Yield in the Mesoregions of Northeast Brazil

Predictive Potential of Maize Yield in the Mesoregions of Northeast Brazil

Long‐term trend analysis in annual, seasonal and monthly rainfall in East Northeast of Brazil and the influence of modes of climate variability

Variability of Water Use Efficiency Associated with Climate Change in the Extreme West of Bahia

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