Climate-based statistical regression models for crop yield forecasting of coffee in humid tropical Kerala, India

Assessing scale-dependency of climate risks in coffee-based agroforestry systems

Agroforestry is a management strategy for mitigating the negative impacts of climate and adapting to sustainable farming systems. The successful implementation of agroforestry strategies requires that climate risks are appropriately assessed. The spatial scale, a critical determinant influencing climate impact assessments and, subsequently, agroforestry strategies, has been an overlooked dimension in the literature. In this study, climate risk impacts on robusta coffee production were investigated at different spatial scales in coffee-based agroforestry systems across India. Data from 314 coffee farms distributed across the districts of Chikmagalur and Coorg (Karnataka state) and Wayanad (Kerala state) were collected during the 2015/2016 to 2017/2018 coffee seasons and were used to quantify the key climate drivers of coffee yield. Projected climate data for two scenarios of change in global climate corresponding to (1) current baseline conditions (1985-2015) and (2) global mean temperatures 2 °C above preindustrial levels were then used to assess impacts on robusta coffee yield. Results indicated that at the district scale rainfall variability predominantly constrained coffee productivity, while at a broader regional scale, maximum temperature was the most important factor. Under a 2 °C global warming scenario relative to the baseline (1985-2015) climatic conditions, the changes in coffee yield exhibited spatial-scale dependent disparities. Whilst modest increases in yield (up to 5%) were projected from district-scale models, at the regional scale, reductions in coffee yield by 10-20% on average were found. These divergent impacts of climate risks underscore the imperative for coffee-based agroforestry systems to develop strategies that operate effectively at various scales to ensure better resilience to the changing climate.

Impacts of recent rainfall changes on agricultural productivity and water resources within the Southern Western Ghats of Kerala, India

Significant changes in rainfall patterns are critical to agriculture, and the dependency of cropping systems on rainfall variability would engender appropriate farming practices and agriculture policies for a climate-resilient agriculture system. This study analyses the significance of rainfall variability on agriculture productivity in the Wayanad district of Kerala (India) using time series data on rainfall (1989-2019) and crop yield (2000-2019). The spatial variability of rainfall patterns reveals a dichotomy between the rain gauge stations in the northern and southern parts of the region. Despite the absence of statistically significant trends in the monthly, seasonal and annual rainfall, based on the Mann-Kendall trend analysis, an increase in the yield of many crops (e.g., winter paddy, banana) is evident, which emphasises the critical role of irrigation in driving the crop productivity. As an adaptation strategy to changing rainfall patterns, irrigation would meet the additional crop water requirement for sustainable agricultural production under the varying rainfall distributions. However, the increase in the area under irrigation in recent years has had significant implications for both surface water and groundwater resources. The conclusive findings suggest that the region requires climate-resilient agriculture, focusing on optimising irrigation and developing sustainable agriculture and water conservation strategies.

Thermal Contaminants in Coffee Induced by Roasting: A Review

Roasting is responsible for imparting the main characteristics to coffee, but the high temperatures used in the process can lead to the formation of several potentially toxic substances. Among them, polycyclic aromatic hydrocarbons, acrylamide, furan and its derivative compounds, α-dicarbonyls and advanced glycation end products, 4-methylimidazole, and chloropropanols stand out. The objective of this review is to present a current and comprehensive overview of the chemical contaminants formed during coffee roasting, including a discussion of mitigation strategies reported in the literature to decrease the concentration of these toxicants. Although the formation of the contaminants occurs during the roasting step, knowledge of the coffee production chain as a whole is important to understand the main variables that will impact their concentrations in the different coffee products. The precursors and routes of formation are generally different for each contaminant, and the formed concentrations can be quite high for some substances. In addition, the study highlights several mitigation strategies related to decreasing the concentration of precursors, modifying process conditions and eliminating/degrading the formed contaminant. Many of these strategies show promising results, but there are still challenges to be overcome, since little information is available about advantages and disadvantages in relation to aspects such as costs, potential for application on an industrial scale and impacts on sensory properties.

Prediction of arabica coffee production using artificial neural network and multiple linear regression techniques

Crop yield and its prediction are crucial in agricultural production planning. This study investigates and predicts arabica coffee yield in order to match the market demand, using artificial neural networks (ANN) and multiple linear regression (MLR). Data of six variables, including areas, productivity zones, rainfalls, relative humidity, and minimum and maximum temperature, were collected for the recent 180 months between 2004 and 2018. The predicted yield of the cherry coffee crop continuously increases each year. From the dataset, it was found that the prediction accuracy of the R² and RMSE from ANN was 0.9524 and 0.0784 tons, respectively. The ANN model showed potential in determining the cherry coffee yields.

Humic acid as foliar and soil application improve the growth, yield and quality of coffee (cv. C × R) in Western Ghats of India

BACKGROUND

Humic acid is a promising natural resource to be utilized as an alternative for increasing soil fertility and crop production. A field experiment was conducted on the loamy sand soil at the Central Coffee Research Institute research farm, Karnataka, India for 2 years to evaluate the influence of humic acid on yield and bean quality of coffee with six treatments. The treatments comprised of recommended dose of fertilizer (RDF), humic acid soil application and foliar spray along with nutrient mixture and growth hormones.

RESULTS

The data of the yield attributes of coffee revealed that the highest total nodes per branch, crop nodes per branch, flower buds, total number of fruits per branch and fruit set percentage of 17.45, 9.4, 208.65, 153.31 and 3.28, respectively, were recorded by T₆ , which consists of RDF + humic acid granules at 10 kg acre^-1  + nutrient mixture spray (1 kg urea, 1 kg SSP, 0.75 kg MOP, 1 kg ZnSO₄  + 75 mL Planofix 200 L^-1  + humic acid at 600 mL 200 L^-1 as foliar application 25 days after blossom) during the both years of study. Humic acid application significantly improved the yield in both seasons of research. The same trend was observed in coffee bean quality and tree nutrients status. Postharvest nutrient status in the soil did not show any significance.

CONCLUSIONS

The study emphasized that application of humic acid as soil and foliar application improves the yield attributes, yield and quality of coffee apart from the economic profitability. © 2020 Society of Chemical Industry.

Comparative evaluation of linear and nonlinear weather-based models for coconut yield prediction in the west coast of India

Coconut is a major plantation crop of coastal India. Accurate prediction of its yield is helpful for the farmers, industries and policymakers. Weather has profound impact on coconut fruit setting, and therefore, it greatly affects the yield. Annual coconut yield and monthly weather data for 2000-2015 were compiled for fourteen districts of the west coast of India. Weather indices were generated using monthly cumulative value for rainfall and monthly average value for other parameters like maximum and minimum temperature, relative humidity, wind speed and solar radiation. Different linear models like stepwise multiple linear regression (SMLR), principal component analysis together with SMLR (PCA-SMLR), least absolute shrinkage and selection operator (LASSO) and elastic net (ELNET) with nonlinear models namely artificial neural network (ANN) and PCA-ANN were employed to model the coconut yield using the monthly weather indices as inputs. The model's performance was evaluated using R², root mean square error (RMSE) and absolute percentage error (APE). The R² and RMSE of the models ranged between 0.45-0.99 and 18-3624 nuts ha^-1 respectively during calibration while during validation the APE varied between 0.12 and 58.21. The overall average ranking of the models based these performance statistics were in the order of ELNET > LASSO > ANN > SMLR > PCA-SMLR > PCA-ANN. Results indicated that the ELNET model could be used for prediction of coconut yield for the region.

Not so robust: Robusta coffee production is highly sensitive to temperature

Coffea canephora (robusta coffee) is the most heat-tolerant and 'robust' coffee species and therefore considered more resistant to climate change than other types of coffee production. However, the optimum production range of robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22-30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high-resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify robusta's optimal temperature range for production. Our climate-based models explained yield variation well across the study area with a cross-validated mean R²  = .51. We demonstrate that robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5-9°C than current estimates. In the middle of robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350-460 kg/ha (95% credible interval). Our results suggest that robusta coffee is far more sensitive to temperature than previously thought. Current assessments, based on robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi-billion dollar coffee industry and the livelihoods of millions of farmers.

Weather-based coffee leaf rust apparent infection rate modeling

Brazil is the major coffee producer in the world, with 2 million hectares cropped, with 75% of this area with Coffea arabica and 25% with Coffea canephora. Coffee leaf rust (CLR) is one of the main diseases that cause yield losses by reducing healthy leaf area. As CLR is highly influenced by weather conditions, this study aimed to determine the best linearization model to estimate the CLR apparent infection rate, to correlate CLR infection rates with weather variables, and to develop and assess the performance of weather-based infection rate models to be used as a disease warning system. The CLR epidemic was analyzed for 88 site-seasons, while progress curves were assessed by linear, monomolecular, logistic, Gompertz, and exponential linearization models for apparent infection rate determination. Correlations between CLR infection rates and weather variables were conducted at different periods. From these correlations, multiple linear regressions were developed to estimate CLR infection rates, using the most weather-correlated variables. The Gompertz growth model had the best fit with CLR progress curves. Minimum temperature and relative humidity were the weather variables most correlated to infection rate and, therefore, chosen to compose a CLR forecast system. Among the models developed, the one for the condition of high coffee yield at a narrow row spacing was the best, with only 9.4% of false negative occurrences for all the months assessed.