Ano de publicação: 2014

The aim of this study was to model, based on the overall distribution of extreme values, the probability of occurrence of a particular level of annual maximum daily rainfall in three Brazilian regions (Midwest, Southeast and South) and study their behavior over the past 71 years. The parameters of the general distribution of extreme values were estimated by the maximum likelihood method. The Mann–Kendall test showed that there is a positive trend in the annual maximum daily rainfall data series. The non-stationarity was rejected by the augmented Dickey–Fuller test supporting the use of the density function of extreme value distribution to describe the values of the occurrence of annual maximum daily rainfall. The Kolmogorov–Smirnov/Lilliefors goodness-of-fit test showed the good fit of the studied variable to the probability distribution function. The Midwest region has a return period of more frequent annual maximum daily rainfall below 300 mm in comparison with other regions. There is a clear change in the behavior of this extreme event in the Southern region. According to the literature, in past decades annual maximum daily rainfall of 248 mm has been estimated for a return period of 100 years for the state of Santa Catarina-South region, while the results found with the current series, annual maximum daily rainfall of 250 mm was estimated for a return period of 10 years. Extreme annual maximum daily rainfalls for return periods smaller were also found in other regions.

Climate changeClimate periodRainfall variability

https://www.sciencedirect.com/science/article/pii/S2212094714000802?via%3Dihub

Ano de publicação: 2013

In this paper we calculated soil carbon stocks in Brazil studying 17 paired sites where soil stocks were determined in native vegetation, pastures and crop-livestock systems (CPS), and in other regional samplings encompassing more than 100 pasture soils, from 6.58 to 31.53° S, involving three major Brazilian biomes: Cerrado, Atlantic Forest, and the Pampa. The average native vegetation soil carbon stocks at 10, 30 and 60 cm soil depth were equal to approximately 29, 64, and 92 Mg ha−1, respectively. In the paired sites, carbon losses of 7.5 Mg ha−1 and 11.6 Mg ha−1 in CPS systems were observed at 10 cm and 30 cm soil depths, respectively. In pasture soils, carbon losses were similar and equal to 7.5 Mg ha−1 and 11.0 Mg ha−1 at 10 cm and 30 cm soil depths, respectively. Differences at 60 cm soil depth were not significantly different between land uses. The average soil δ13C under native vegetation at 10 and 30 cm depth were equal to −25.4‰ and −24.0‰, increasing to −19.6‰ and −17.7‰ in CPS, and to −18.9‰, and −18.3‰ in pasture soils, respectively; indicating an increasing contribution of C4 carbon in these agrosystems. In the regional survey of pasture soils, the soil carbon stock at 30 cm was equal to approximately 51 Mg ha−1, with an average δ13C value of −19.67‰. Key controllers of soil carbon stock in pasture sites were sand content and mean annual temperature. Collectively, both could explain approximately half of the variance of soil carbon stocks. When pasture soil carbon stocks were compared with the average soil carbon stocks of native vegetation estimated for Brazilian biomes and soil types by Bernoux et al. (2002) there was a carbon gain of 6.7 Mg ha−1, which is equivalent to a carbon gain of 15% compared to the carbon soil stock of the native vegetation. The findings of this study are consistent with differences found between regional comparisons like our pasture sites and plot-level paired study sites in estimating soil carbon stocks changes due to land use changes.

carbon stocks

https://www.biogeosciences.net/10/6141/2013/

Ano de publicação: 2015

In this paper, soil carbon, nitrogen and phosphorus concentrations and stocks were investigated in agricultural and natural areas in 17 plot-level paired sites and in a regional survey encompassing more than 100 pasture soils In the paired sites, elemental soil concentrations and stocks were determined in native vegetation (forests and savannas), pastures and crop–livestock systems (CPSs). Nutrient stocks were calculated for the soil depth intervals 0–10, 0–30, and 0–60 cm for the paired sites and 0–10, and 0–30 cm for the pasture regional survey by sum stocks obtained in each sampling intervals (0–5, 5–10, 10–20, 20–30, 30–40, 40–60 cm). Overall, there were significant differences in soil element concentrations and ratios between different land uses, especially in the surface soil layers.

soil carbon; nitrogen; phosphorus

https://www.biogeosciences.net/12/4765/2015/bg-12-4765-2015.pdf

Ano de publicação: 2013

Coffee is one of the most important crops in Brazil. Monitoring the crop is necessary to understand future production and a sound understanding of coffee's biophysical properties improves such monitoring. Biophysical properties such as dry biomass can be estimated using remote sensing, including the new generation of high-resolution images (GeoEye-1, for instance). In this study we aim to investigate the relationship between vegetation indices (VI) of high-resolution images (GeoEye-1) and coffee biophysical properties, including dry biomass and carbon. The study also aims at establishing an empirical relationship between remote sensing data (vegetation indices), simple field measurements and dry biomass, allowing calculation of coffee biomass and carbon without resorting to destructive methods. Individual GeoEye-1 satellite's bands (NIR, RED and GREEN) showed significant correlation with biomass, but the best correlation occurred with vegetation index. There is a strong correlation between NDVI, RVI, GNDVI and dry biomass, allowing the estimation of coffee crops' carbon stock. RVI had correlation with plant area index (PAI). The empirical correlation was established and the forecast equation of coffee biomass was created.

Correlation, Biomass, Vegetation mapping, Carbon, Indexes, Remote sensing, Agriculture

http://ieeexplore.ieee.org/document/6520004/

Ano de publicação: 2015

Leaf area index (LAI) and above ground biomass (AGB) are two parameters that are difficult to measure but very useful. In this paper we investigated the relationship between coffee biophysical properties and LAI and AGB in two coffee production systems: full sun (FS) and shaded with macadamia nuts (SH). The paper proposes an empirical relationship for calculating coffee AGB and coffee LAI which avoids destructive methods, using simple field measurements and agrometeorological data. Here, we reported that LAI is related to canopy structure but subject to strong seasonal variations, which can be identified using water requirements satisfaction index (WRSI). Coffee LAI answers to the decreased WRSI with 1 month lag (WRSI-1) and LAI values decreases more for FS systems than for SH systems during dry periods. The best empirical model to predict LAI for FS coffee production system was based on canopy height (ch) and WRSI-1 value. For SH systems, the best model used ch, WRSI-1 and the height of the first pair of branches. Coffee AGB values were measured using destructive analyses and an empirical equation was developed. Both coffee production systems stocked carbon, whereas the SH system stocked an increased carbon amount provided by the macadamia trees that contributed with 15 % of the total carbon above ground. Both systems can be considered mitigation techniques since they are able to remove atmospheric carbon and stock it in the biomass, which has been largely proposed as a compensation mechanism for greenhouse gas emissions.

Carbon stock; Coffee shaded system; Empirical equations; Seasonal LAI variation 

https://link.springer.com/article/10.1007%2Fs10457-015-9799-5

Ano de publicação: 2013

The aim of this study was to model the occurrence of extreme dry spells in the Midwest, Southeast and Southern regions of Brazil and estimate the return period of the phenomenon indicating the time when the occurrence is more severe. The generalized extreme value distribution was the best fit for a series of maximum dry spell number and the parameters estimated by the maximum likelihood method. The data series adherence to the probability distribution was verified by the Kolmogorov–Smirnov test and the percentile–percentile charts. The positive trend of dry spells was verified by the Mann–Kendall test and non-stationarity rejected by Dickey–Fuller and augmented Dickey–Fuller tests. The irregular distribution of rainfall in the growing season for the Midwest region has increased the number of dry spells. The increase of rainy days in the Southeast and the South resulted in a decrease of dry spells in these regions. Regarding the return period of one year, dry spells occurred from 5 to 25 days in the Midwest region meaning a loss of productivity for Brazilian agriculture if it happened between the flowering and grain filling phases, making it, therefore the region with the largest agricultural risk. When the intensity of the dry spells was analyzed for different return periods, the Southern region was the most vulnerable.

Dry spellsGeneralized extreme value distributionNumber of rainy daysReturn period

https://www.sciencedirect.com/science/article/pii/S016980951300118X?via%3Dihub

Ano de publicação: 2010

As condições meteorológicas são determinantes para a produção agrícola; a precipitação, em particular, pode ser citada como a mais influente por sua relação direta com o balanço hídrico. Neste sentido, modelos agrometeorológicos, os quais se baseiam nas respostas das culturas às condições meteorológicas, vêm sendo cada vez mais utilizados para a estimativa de rendimentos agrícolas. Devido às dificuldades de obtenção de dados para abastecer tais modelos, métodos de estimativa de precipitação utilizando imagens dos canais espectrais dos satélites meteorológicos têm sido empregados para esta finalidade. O presente trabalho tem por objetivo utilizar o classificador de padrões "floresta de caminhos ótimos" para correlacionar informações disponíveis no canal espectral infravermelho do satélite meteorológico GOES-12 com a refletividade obtida pelo radar do IPMET/UNESP localizado no município de Bauru, visando o desenvolvimento de um modelo para a detecção de ocorrência de precipitação. Nos experimentos foram comparados quatro algoritmos de classificação: redes neurais artificiais (ANN), k-vizinhos mais próximos (k-NN), máquinas de vetores de suporte (SVM) e floresta de caminhos ótimos (OPF). Este último obteve melhor resultado, tanto em eficiência quanto em precisão.

Classificadores Supervisionados, Floresta de Caminhos Ótimos, GOES, Estimativa de Ocorrência de Precipitação

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862010000100002&lng=pt&tlng=pt

Ano de publicação: 2010

Weather conditions in critical periods of the vegetative crop development influence crop productivity, thus being a basic parameter for crop forecast. Reliable extended period weather forecasts may contribute to improve the estimation of agricultural productivity. The production of soybean plays an important role in the Brazilian economy, because this country is ranked among the largest producers of soybeans in the world. This culture can be significantly affected by water conditions, depending on the intensity of water deficit. This work explores the role of extended period weather forecasts for estimating soybean productivity in the southern part of Brazil, Passo Fundo, and Londrina (State of Rio Grande do Sul and Paraná, respectively) in the 2005/2006 harvest. The goal was to investigate the possible contribution of precipitation forecasts as a substitute for the use of climatological data on crop forecasts. The results suggest that the use of meteorological forecasts generate more reliable productivity estimates during the growth period than those generated only through climatological information.

Weather Forecast; Productivity Estimate; Ensemble Forecast; Forecast Period; Crop Cycle 

https://link.springer.com/article/10.1007%2Fs00704-010-0264-0

Ano de publicação: 2017

The intensification of drought incidence is one of the most important threats of the 21st century with significant effects on food security. Accordingly, there is a need to improve the understanding of the regional impacts of climate change on this hazard. This study assessed long-term trends in probability-based drought indices (Standardized Precipitation Index and Standardized Evapotranspiration Index) in the State of São Paulo, Brazil. Owing to the multi-scalar nature of both indices, the analyses were performed at 1 to 12-month time scales. The indices were calculated by means of a relativist approach that allowed us to compare drought conditions from different periods. The years 1961-1990 were used as the referential period. To the authors’ best knowledge, this is the first time that such relativist approach is used in historical trend analysis. The results suggest that the evapotranspiration rates have intensified the regional drought conditions. The time scale used to calculate the indices significantly affected the outcomes of drought trend assessments. The reason behind this feature is that the significant changes in the monthly regional patterns are limited to a specific period of the year. More specifically, virtually all significant changes have been observed during the first trimester of the rainy season (October, November and December). Considering that this period corresponds to critical plant growth stages (flowering/regrowth/sprouting) of several major crops (e.g. Sugarcane and Citrus), we may conclude that these significant changes have increased the risk of crop yield reductions due to agricultural drought.

intensification of drought; drought Index; agricultural drought; crop yields

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0006-87052018000100201&lng=en&tlng=en

Ano de publicação: 2013

The objective of this work was to evaluate the effect of the temperature increase forecasted by the Intergovernmental Panel on Climate Change (IPCC) on agricultural zoning of cotton production in Brazil. The Northeastern region showed the highest decrease in the low-risk area for cotton cultivation due to the projected temperature increase. This area in the Brazilian Northeast may decrease from 83 million ha in 2010 to approximately 71 million ha in 2040, which means 15% reduction in 30 years. Southeastern and Center-Western regions had small decrease in areas suitable for cotton production until 2040, while the Northern region showed no reduction in these areas. Temperature increase will not benefit cotton cultivation in Brazil because dimension of low-risk areas for economic cotton production may decrease.

Gossypium hirsutum, evapotranspiration, global warming

http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2013000100001&lng=en&tlng=en

Ano de publicação: 2019

Coffee is one of the most consumed beverages in the world, and its international market has been growing for many years. Unfortunately, the Brazilian coffee production is threatened by high temperatures projected by climate change models. We evaluated three schemes of low levels of shade, which avoid the loss of production, as a strategy to adapt coffee to possible climate change. Additionally, as field measurements are expensive and often difficult to implement, we used numerical simulation to complement the evaluation. The microclimate simulator software Envi-met is a computer program often used to simulate urban environments, and we tested it on agriculture design. We verified that the shaded schemes assessed in the field decreased the air temperature in 0.6 °C in the studied period and reduced other possible climate stressors such as wind speed, radiation and raised air humidity in the dry period. Envi-met described the studied meteorological variable cycle very well, showing that combining numerical modelling and field research may be an important tool for planning the adaptation of the coffee sector to possible climate change, allowing growers choose a proper technique for their regions and environmental conditions. Finally, we highlighted the importance of planning the shade scheme on coffee areas in an interdisciplinary approach, including local climate evaluation to achieve a balance between temperature attenuation and production.

Agriculture, 

Environmental science.

https://www.sciencedirect.com/science/article/pii/S2405844018347698?via%3Dihub

Ano de publicação: 2011

Agriculture appears to be one of the human activities most vulnerable to climatic changes due to its large dependence on environmental conditions. However, the diversity of Brazilian environmental conditions could be of great advantage to adapting this sector to new climatic conditions, which should be assessed as in this study on shifting Arabica coffee cultivation to the extreme south of the country. The methodology applied is the same the one used to define climatic risks in current productive regions of Brazil and their vulnerability to climatic change predicted by IPCC reports. The basic climatic parameters applied were frost probability and annual average temperature, since annual water deficit did not prove to be a restricting factor for Arabica coffee cultivation in the study area. The climatic conditions suitable for coffee production are: annual average temperature between 18°C and 22°C, annual water deficit less than 100 mm and frost probability (risk of lowest annual temperature less than 1°C) less than 25%. An area is said to have “low climatic risks” for coffee production when these three climatic conditions are met. Current climatic conditions were used and simulations of four temperature increases between 1°C and 4°C were also performed. The results indicated a substantial increase in the size of low climatic risks areas for the production of Arabica coffee in the extreme south of Brazil, mainly for mean temperature increases of 3°C in the study area in relation to present conditions. Increases of 2°C and 4°C were also favorable, but not as good as those obtained for 3°C. It should be underscored that areas with low climatic risks will be able to be found mainly in the extreme south of the study region, the border with Uruguay and North of Argentina.

Global Warming; Climatic Risk; Coffee Production; Current Climatic Condition; Arabica Coffee 

https://link.springer.com/article/10.1007%2Fs10584-011-0058-0