The Sustainable Expansion of the Cocoa Crop in the State of Paráand Its Contribution to Altered Areas Recovery and Fire Reduction

The state of Pará, located in the Amazon region of Brazil, has observed in recent years an increase in cocoa (Theobroma cacao) cultivation and has become the largest producer in Brazil. Due to its physiological characteristics, cacao is cultivated in native forests understory or under the shade produced by fast-growing native tree species, serving as an important species for restoration of degraded areas. However, mapping and monitoring cocoa plantation using optical sensor images is a challenge given its botanical and arboreal characteristics that can be confused with other native species at various stages of secondary regrowth. Agroforestry systems are important components of sustainable production in the Amazon and our work sought to better describe the evolution of cocoa plantations in terms of their historical expansion, farming properties practices, land use transitions and fire regimes. Our findings to analyze the relationships between cocoa plantations and hotspots, data from the INPE’s reference satellite between the years 2004 to 2020 were used in this study, polygons classified as cocoa areas, generated by the MapCacau research project, were used, in a total of 69,904 hectares distributed throughout the state of Pará. Finally, we used the protected areas’ official limits in the State of Pará to analyze the plantations’ occurrence in regions in discordance with environmental legislation. The data show that cocoa-producing properties are statistically fewer than non-producing properties, as well as having lower deforestation rates. In our study, we observed that 52,778 hectares (88.87%) of the cocoa area planted had already been deforested by the year 2008—the threshold of deforestation defined by Brazil’s Forest Code. It was also possible to verify that approximately 20,900 hectares continue to be mapped as forest by PRODES, despite our field data identifying cocoa plantations shaded by explored forest in these areas. Regarding the crop’s formation, the data show a tendency to convert pasture areas to cocoa plantations, proving that cocoa farming expansion in the State of Pará is an important activity for degraded areas recovery and not a main driver of deforestation. The finding that cocoa plantations are still classified as forest by PRODES and project TerraClass highlights the difficulty of mapping this crop using orbital images in a traditional way. Through this paper, it was possible to observe that due to the typical characteristics of perennial crops (cocoa), fire points showed a significant reduction in the mapped areas, highlighting that the expansion of cocoa plantations in the state of Pará contributed to soil protection, to the reduction of greenhouse gas emissions into, in addition to contributing to the generation of jobs and revenue. Finally, we found about 99.54% of the cacao plantations in the State of Pará are located outside of any preservation area, indigenous land or quilombola settlement.

Skill Assessment of North American Multi-Models Ensemble (NMME) for June-September (JJAS) Seasonal Rainfall over Ethiopia

In recent years, there has been increasing demand for high-resolution seasonal climate forecasts at sufficient lead times to allow response planning from users in agriculture, hydrology, disaster risk management, and health, among others. This paper examines the forecasting skill of the North American Multi-model Ensemble (NMME) over Ethiopia during the June to September (JJAS) season. The NMME, one of the multi-model seasonal forecasting systems, regularly generates monthly seasonal rainfall forecasts over the globe with 0.5 - 11.5 months lead time. The skill and predictability of seasonal rainfall are assessed using 28 years of hindcast data from the NMME models. The forecast skill is quantified using canonical correlation analysis (CCA) and root mean square error. The results show that the NMME models capture the JJAS seasonal rainfall over central, northern, and northeastern parts of Ethiopia while exhibiting weak or limited skill across western and southwestern Ethiopia. The performance of each model in predicting the JJAS seasonal rainfall is variable, showing greater skill in predicting dry conditions. Overall, the performance of the multi-model ensemble was not consistently better than any single ensemble member. The correlation of observed and predicted seasonal rainfall for the better performing models—GFDL-CM2p5-FLOR-A06, CMC2-CanCM4, GFDL-CM2p5-FLOR-B01 and NASA-GMAO-062012—is 0.68, 0.58, 0.52, and 0.5, respectively. The COLA-RSMAS-CCSM4, CMC1- CanCM3 and NCEP-CFSv2 models exhibit less skill, with correlations less than 0.4. In general, the NMME offers promising skill to predict seasonal rainfall over Ethiopia during the June-September (JJAS) season, motivating further work to assess its performance at longer lead times.

Data and tools to integrate climate and environmental information into public health

Background:During the last 30 years,the development of geographical information systems and satellites for Earth observation has made important progress in the monitoring of the weather,climate,environmental and anthropogenic factors that influence the reduction or the reemergence of vector-borne diseases.Analyses resulting from the combination of geographical information systems(GIS)and remote sensing have improved knowledge of climatic,environmental,and biodiversity factors influencing vector-borne diseases(VBDs)such as malaria,visceral leishmaniasis,dengue,Rift Valley fever,schistosomiasis,Chagas disease and leptospirosis.These knowledge and products developed using remotely sensed data helped and continue to help decision makers to better allocate limited resources in the fight against VBDs.Main body:Because VBDs are linked to climate and environment,we present here our experience during the last four years working with the projects under the,World Health Organization(WHO)/The Special Programme for Research and Training in Tropical Diseases(TDR)-International Development Research Centre(IDRC)Research Initiative on VBDs and Climate Change to integrate climate and environmental information into research and decision-making processes.The following sections present the methodology we have developed,which uses remote sensing to monitor climate variability,environmental conditions,and their impacts on the dynamics of infectious diseases.We then show how remotely sensed data can be accessed and evaluated and how they can be integrated into research and decision-making processes for mapping risks,and creating Early Warning Systems,using two examples from the WHO TDR projects based on schistosomiasis analysis in South Africa and Trypanosomiasis in Tanzania.Conclusions:The tools presented in this article have been successfully used by the projects under the WHO/TDRIDRC Research Initiative on VBDs and Climate Change.Combined with capacity building,they are an important piece of work which can significantly contribute to the goals of WHO Global Vector Control Response and to the Sustainable Development Goals especially those on health and climate action.

Climate drivers of vector-borne diseases in Africa and their relevance to control programmes

Background:Climate-based disease forecasting has been proposed as a potential tool in climate change adaptation for the health sector.Here we explore the relevance of climate data,drivers and predictions for vector-borne disease control efforts in Africa.Methods:Using data from a number of sources we explore rainfall and temperature across the African continent,from seasonality to variability at annual,multi-decadal and timescales consistent with climate change.We give particular attention to three regions defined as WHO-TDR study zones in Western,Eastern and Southern Africa.Our analyses include 1)time scale decomposition to establish the relative importance of year-to-year,decadal and long term trends in rainfall and temperature;2)the impact of the El Niño Southern Oscillation(ENSO)on rainfall and temperature at the Pan African scale;3)the impact of ENSO on the climate of Tanzania using high resolution climate products and 4)the potential predictability of the climate in different regions and seasons using Generalized Relative Operating Characteristics.We use these analyses to review the relevance of climate forecasts for applications in vector borne disease control across the continent.Results:Timescale decomposition revealed long term warming in all three regions of Africa-at the level of 0.1-0.3°C per decade.Decadal variations in rainfall were apparent in all regions and particularly pronounced in the Sahel and during the East African long rains(March-May).Year-to-year variability in both rainfall and temperature,in part associated with ENSO,were the dominant signal for climate variations on any timescale.Observed climate data and seasonal climate forecasts were identified as the most relevant sources of climate information for use in early warning systems for vector-borne diseases but the latter varied in skill by region and season.Conclusions:Adaptation to the vector-borne disease risks of climate variability and change is a priority for government and civil society in African countries.Understanding rainfall and temperature variations and trends at multiple timescales and their potential predictability is a necessary first step in the incorporation of relevant climate information into vector-borne disease control decision-making.

Combining machine learning,space-time cloud restoration and phenology for farm-level wheat yield prediction

Though studies showed the potential of high-resolution optical sensors for crop yield prediction,several factors have limited their wider application.The main factors are obstruction of cloud,identification of phenology,demand for high computing infrastructure and the complexity of statistical methods.In this research,we created a novel approach by combining four methods.First,we implemented the cloud restoration algorithm called gapfill to restore missed Normalized Difference Vegetation Index(NDVI)values derived from Sentinel-2 sensor(S2)due to cloud obstruction.Second,we created square tiles as a solution for high computing infrastructure demand due to the use of high-resolution sensor.Third,we implemented gapfill following critical crop phenology stage.Fourth,observations from restored images combined with original(from cloud-free images)values and applied for winter wheat prediction.We applied seven base machine learning as well as two groups of super learning ensembles.The study successfully applied gapfill on high-resolution image to get good quality estimates for cloudy pixels.Consequently,yield prediction accuracy increased due to the incorporation of restored values in the regression process.Base models such as Generalized Linear Regression(GLM)and Random Forest(RF)showed improved capacity compared to other base and ensemble models.The two models revealed RMSE of 0.001 t/ha and 0.136 t/ha on the holdout group.The twomodels also revealed consistent and better performance using scatter plot analysis across three datasets.The approach developed is useful to predict wheat yield at field scale,which is a rarely available but vital in many developmental projects,using optical sensors.