Barley vulnerability to climate change: perspectives for cultivation in South America.

Barley (Hordeum vulgare) is a globally significant cereal crop, widely used in both food production and brewing. However, it is particularly vulnerable to climate change, especially extreme temperature fluctuations, which can severely reduce yields. To address this challenge, a detailed climate zoning study was conducted to assess the suitability of barley production areas across South America, considering both current conditions and future climate scenarios from the Intergovernmental Panel on Climate Change (IPCC).

Future climate suitability of Hemileia vastatrix in arabica coffee under CMIP6 scenarios.

Background: Coffee (Coffea arabica L.) is one of the most important commodities today, with a high economic value worldwide. Coffee leaf rust (Hemileia vastatrix Berk.

Climate and disease: tackling coffee brown-eye spot with advanced forecasting models.

Background: Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease.

Agroclimatic mapping for olive cultivation in Brazil: pinpointing optimal growing regions.

Background: This research aimed to identify the agroclimatic zones in Brazil, excluding Rio Grande do Sul, that are suitable for olive (Olea europaea L.) cultivation, considering both climatic and topographical factors. Olives require specific conditions: moderate winter temperatures (7-15 °C), warmer summers (25-35 °C) and sufficient water during growth and fruit maturation.

Climate zoning: identifying suitable regions for the occurrence of Alternaria Brown spot in tangerine trees in Brazil.

Background: Brazilian citrus farming has been migrating to nontraditional citrus-growing regions, which can be considered a challenge for citrus growers, as not all these areas are suitable for tangerine cultivation. Thus, the mapping of regions exhibiting favorable climatic conditions for Alternaria brown spot has become crucial in the selection of appropriate locations for the establishment of new orchards. This mapping enables the implementation of an avoidance strategy, which entails steering clear of areas where the disease is prevalent, aligning with fundamental principles of disease control.

Climate risk zoning for wheat crops in the southeastern region of Brazil.

Background: Wheat (Triticum aestivum L.) is the second most consumed food in the world. One way to meet this demand is the expansion of wheat cultivation to the Brazilian Cerrado in the southeastern region.

Agroclimatic zoning for bananas under climate change in Brazil.

Background: Climate change is the main cause of biotic and abiotic stresses in plants and affects yield. Therefore, we sought to carry out a study on future changes in the agroclimatic conditions of banana cultivation in Brazil. The current agroclimatic zoning was carried out with data obtained from the National Institute of Meteorology related to mean air temperature, annual rainfall, and soil texture data in Brazil.

Coffee pest severity by agrometeorological models in subtropical climate.

This study aimed to estimate the number of generations and cycle duration of the southern red mite, coffee berry borer, and coffee leaf miner using the thermal index to assist in controlling these main coffee pests in the state of Paraná, Brazil. The data of maximum and minimum air temperature (°C) and precipitation (mm) of all municipalities in the state from 1984 to 2018 were collected from the National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources (NASA/POWER). The reference evapotranspiration was estimated using the (Camargo Campinas IAC Boletim 116:9, 1971) method and the water balance was calculated using the method of ( Thornthwaite C, Mather J (1955) The water balance publications in climatology, 8 (1).

Bioclimatic zoning for dairy cows in Brazil by statistical modeling.

Background: Climate conditions affect animal welfare directly, influencing milk production. The Midwest region is the largest cattle-producing region in Brazil. The objective of this study was to elaborate on bioclimatic zoning for dairy cattle in the Midwest region of Brazil.

Can nonlinear agrometeorological models estimate coffee foliation?

Background: The loss of coffee leaves caused by the attack of pests and diseases significantly reduces its production and bean quality. Thus this study aimed to estimate foliation for regions with the highest production of arabica coffee in Brazil using nonlinear models as a function of climate. A 25-year historical series (1995-2019) of Coffea arabica foliation (%) data was obtained by the Procafé Foundation in cultivations with no phytosanitary treatment.

Climate risk to peanut cultivation in Brazil across different planting seasons.

Background: Peanuts are widely grown in Brazil because of their great importance in the domestic vegetable oil industry and the succession of sugarcane, soybean and maize crops, contributing to soil conservation and improvement in agricultural areas. Thus, the present study aimed to determine the zoning of peanuts' climatic risk by estimating the water requirement satisfaction index (WRSI) for the crop in Brazil. We used a historical series of data on average air temperature and rainfall between 1980 and 2016.

Models for simulating the frequency of pests and diseases of Coffea arabica L.

We developed models for simulating trends over time as functions of the thermal index and models for estimating the levels of infestation of the coffee leaf miner and coffee berry borer and the severity of disease for coffee leaf rust and cercospora, the main phytosanitary problems in coffee crops around the world. We used historical series of climatic data and levels of pest infestation and disease severity in Coffea arabica for high and low yields for seven locations in the two main coffee-producing regions in the state of Minas Gerais in Brazil, Sul de Minas Gerais and Cerrado Mineiro. We conducted two analyses: (a) we simulated the trends of the progress of diseases and pests over time using non-linear models.

Machine learning algorithms for forecasting the incidence of Coffea arabica pests and diseases.

Disease and pest alert models are able to generate information for agrochemical applications only when needed, reducing costs and environmental impacts. With machine learning algorithms, it is possible to develop models to be used in disease and pest warning systems as a function of the weather in order to improve the efficiency of chemical control of pests of the coffee tree. Thus, we correlated the infection rates with the weather variables and also calibrated and tested machine learning algorithms to predict the incidence of coffee rust, cercospora, coffee miner, and coffee borer.

Agrometeorological models to forecast açaí (Euterpe oleracea Mart.) yield in the Eastern Amazon.

Background: The increasing demand in Brazil and the world for products derived from the açaí palm (Euterpe oleracea Mart) has generated changes in its production process, principally due to the necessity of maintaining yield in situations of seasonality and climate fluctuation. The objective of this study was to estimate açaí fruit yield in irrigated system (IRRS) and rainfed system or unirrigated (RAINF) using agrometeorological models in response to climate conditions in the eastern Amazon. Modeling was done using multiple linear regression using the 'stepwise forward' method of variable selection.

Neural networks in spatialization of meteorological elements and their application in the climatic agricultural zoning of bamboo.

Bamboo has an important role in international commerce due to its diverse uses, but few studies have been conducted to evaluate its climatic adaptability. Thus, the objective of this study was to construct an agricultural zoning for climate risk (ZARC) for bamboo using meteorological elements spatialized by neural networks. Climate data included air temperature (T, °C) and rainfall (P) from 4947 meteorological stations in Brazil from the years 1950 to 2016.

Maturation periods for Coffea arabica cultivars and their implications for yield and quality in Brazil.

Background: Climatic conditions directly affect the maturation period of coffee plantations, affecting yield and beverage quality. The quality of coffee beverages is highly correlated with the length of fruit maturation, which is strongly influenced by meteorological elements. The objective was to estimate the probable times of graining and maturation of the main coffee varieties in Brazil and to quantify the influences of climate on coffee maturation.

Climatic potential for summer and winter wine production.

Background: The geoviticultural multicriteria climatic classification (MCC) system provides an efficient guide for assessing the influence of climate on wine varieties. Paraná is one of the three states in southern Brazil that has great potential for the expansion of wine production, mainly due to the conditions that favour two harvests a year. The objective was to apply the geoviticultural MCC system in two production seasons.