Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data

One way to use climate services in the case of sugarcane is to develop models that forecast yields to help the sector to be better prepared against climate risks. In this study, several models for forecasting sugarcane yields were developed and compared in the north of Ivory Coast (West Africa). These models were based on statistical methods, ranging from linear regression to machine learning algorithms such as the random forest method, fed by climate data (rainfall, temperature); satellite products (NDVI, EVI from MODIS Vegetation Index product) and information on cropping practices. The results show that the forecasting of sugarcane yield depended on the area considered. At the plot level, the noise due to cultivation practices can hide the effects of climate on yields and leads to poor forecasting performance. However, models using satellite variables are more efficient and those with EVI alone may explain 43% of yield variations. Moreover, taking into account cultural practices in the model improves the score and enables one to forecast 3 months before harvest in 50% and 69% of cases whether yields will be high or low, respectively, with errors of only 10% and 2%, respectively. These results on the predictive potential of sugarcane yields are useful for planning and climate risk management in this sector.

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Prediction of Maize Yield at the City Level in China Using Multi-Source Data

Remote Sensing ◽

10.3390/rs13010146 ◽

2021 ◽

Vol 13 (1) ◽

pp. 146

Author(s):

Xinxin Chen ◽

Lan Feng ◽

Rui Yao ◽

Xiaojun Wu ◽

Jia Sun ◽

...

Keyword(s):

Machine Learning ◽

Satellite Data ◽

Vegetation Index ◽

Maize Yield ◽

Machine Learning Algorithms ◽

Shortwave Radiation ◽

Growth Stages ◽

Climate Data ◽

Early Peak ◽

The City

Maize is a widely grown crop in China, and the relationships between agroclimatic parameters and maize yield are complicated, hence, accurate and timely yield prediction is challenging. Here, climate, satellite data, and meteorological indices were integrated to predict maize yield at the city-level in China from 2000 to 2015 using four machine learning approaches, e.g., cubist, random forest (RF), extreme gradient boosting (Xgboost), and support vector machine (SVM). The climate variables included the diffuse flux of photosynthetic active radiation (PDf), the diffuse flux of shortwave radiation (SDf), the direct flux of shortwave radiation (SDr), minimum temperature (Tmn), potential evapotranspiration (Pet), vapor pressure deficit (Vpd), vapor pressure (Vap), and wet day frequency (Wet). Satellite data, including the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), and adjusted vegetation index (SAVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), were used. Meteorological indices, including growing degree day (GDD), extreme degree day (EDD), and the Standardized Precipitation Evapotranspiration Index (SPEI), were used. The results showed that integrating all climate, satellite data, and meteorological indices could achieve the highest accuracy. The highest estimated correlation coefficient (R) values for the cubist, RF, SVM, and Xgboost methods were 0.828, 0.806, 0.742, and 0.758, respectively. The climate, satellite data, or meteorological indices inputs from all growth stages were essential for maize yield prediction, especially in late growth stages. R improved by about 0.126, 0.117, and 0.143 by adding climate data from the early, peak, and late-period to satellite data and meteorological indices from all stages via the four machine learning algorithms, respectively. R increased by 0.016, 0.016, and 0.017 when adding satellite data from the early, peak, and late stages to climate data and meteorological indices from all stages, respectively. R increased by 0.003, 0.032, and 0.042 when adding meteorological indices from the early, peak, and late stages to climate and satellite data from all stages, respectively. The analysis found that the spatial divergences were large and the R value in Northwest region reached 0.942, 0.904, 0.934, and 0.850 for the Cubist, RF, SVM, and Xgboost, respectively. This study highlights the advantages of using climate, satellite data, and meteorological indices for large-scale maize yield estimation with machine learning algorithms.

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Plan for the development of specialized climate forecasting in NEACC

Hydrometeorological research and forecasting ◽

10.37162/2618-9631-2021-1-101-111 ◽

2021 ◽

pp. 101-111

Author(s):

S.V. Emelina ◽

◽

V.M. Khan ◽

Keyword(s):

Large Scale ◽

Regional Climate ◽

Optimal Strategies ◽

Economic Sectors ◽

Climate Services ◽

Climate Conditions ◽

Climate Risks ◽

The North ◽

Weather And Climate ◽

North Eurasia

The possibility of developing specialized seasonal forecasting within the framework of the North Eurasia Climate Centre is discussed. The purpose of these forecasts is to access the impacts of significant large-scale anomalies of meteorological elements on various economic sectors for the timely informing of government services and private businesses to select optimal strategies for planning preventive measures. A brief overview of the groups of climatic risks in the context of the impacts on the socio-economic sphere is given according to the Russian and foreign bibliographic sources. Examples of the activities of some Regional Climate Centers that produce forecast information with an assessment of possible impacts of weather and climate conditions at seasonal scales on various human activities are given. Keywords: climate services, regional climate forums, weather and climate risks, North Eurasia Climate Centre

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Guinea

Enforcement of Intellectual Property Rights in Africa ◽

10.1093/oso/9780198837336.003.0026 ◽

2020 ◽

Author(s):

Marius Schneider ◽

Vanessa Ferguson

Keyword(s):

West Africa ◽

Sierra Leone ◽

Ivory Coast ◽

Official Language ◽

The South ◽

Guinea Bissau ◽

The North

Guinea, also sometimes referred as Guinea-Conakry, is found in West Africa. It is bordered by Guinea-Bissau, Senegal, and Mali in the north and Sierra Leone, Liberia, and Ivory Coast in the south. In 2016, Guinea had a population of 12.6 million over a territory of 245 860 square kilometres (km). Its capital and largest city is Conakry. The official language of Guinea is French, and the currency used is the Guinean franc (GNF).

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Liberia

Enforcement of Intellectual Property Rights in Africa ◽

10.1093/oso/9780198837336.003.0031 ◽

2020 ◽

Author(s):

Marius Schneider ◽

Vanessa Ferguson

Keyword(s):

West Africa ◽

Atlantic Ocean ◽

Sierra Leone ◽

North Atlantic ◽

Ivory Coast ◽

North Atlantic Ocean ◽

Capital City ◽

The North ◽

The North Atlantic

Liberia is situated in the southern part of West Africa on the North Atlantic Ocean, bordered by Sierra Leone, Guinea, and Ivory Coast, covering an area of 111,369 square kilometres (km) with a population of 4,958,454. The majority of the population live in the Montserrado county and home to the capital city of Monrovia, with approximately 25 per cent of the Liberian population living in greater Monrovia. Monrovia is the capital and most populous city in Liberia and has the largest artificial port in West Africa. Typically, business hours are Monday to Friday from 0800 to 1700 with banks closing at 1500. The official currency of Liberia is the Liberian dollar (LRD).

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Mali

Enforcement of Intellectual Property Rights in Africa ◽

10.1093/oso/9780198837336.003.0035 ◽

2020 ◽

Author(s):

Marius Schneider ◽

Vanessa Ferguson

Keyword(s):

West Africa ◽

Burkina Faso ◽

Rural Areas ◽

Ivory Coast ◽

Southern Region ◽

Capital City ◽

Sahara Desert ◽

Chronic Instability ◽

The North

Mali is a landlocked country found in West Africa. Mali borders Algeria, Burkina Faso, Guinea, Ivory Coast, Mauritania, Niger, and Senegal. It is the eighth largest country in Africa with a population of nearly 18 million people as recorded in 2016. Only 10 per cent of the population live in the north, which represents nearly two-thirds of the country. While rich in minerals and oil, the north of Mali is desertified and suffers from chronic instability. The vast majority of people in Mali live in the southern region close to the Niger and Senegal rivers and far from the Sahara Desert. Rural areas account for 59 per cent of the population. The capital city is Bamako which is the only town in Mali with more than 1 million inhabitants and is the main commercial and industrial centre in the country. The second biggest city in terms of population is Sikasso with approximately 130,000 inhabitants.

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Agrometeorological services for smallholder farmers in West Africa

Advances in Science and Research ◽

10.5194/asr-15-15-2018 ◽

2018 ◽

Vol 15 ◽

pp. 15-20 ◽

Cited By ~ 2

Author(s):

Vieri Tarchiani ◽

José Camacho ◽

Hamidou Coulibaly ◽

Federica Rossi ◽

Robert Stefanski

Keyword(s):

West Africa ◽

Production Systems ◽

Smallholder Farmers ◽

Innovation System ◽

West African ◽

Added Value ◽

Population Pressure ◽

Climate Services ◽

Climate Risks ◽

African Agriculture

Abstract. Climate variability and change are recognised as a major threat for West African agriculture, particularly for smallholder farmers. Moreover, population pressure, poverty, and food insecurity, are worsening the vulnerability of production systems to climate risks. Application of Climate Services in agriculture, specifically Agrometeorological Services, is acknowledged as a valuable innovation to assist decision-making and develop farmers' specific adaptive capacities. In West Africa, the World Meteorological Organisation and National Meteorological Services deployed considerable efforts in the development of Agrometeorological Services. Nevertheless, the impacts of such services on West African farming communities are still largely unknown. This paper aims to delineate the added value of agrometeorological services for farmers within the Agriculture Innovation System of Mauritania. The results of this quali-quantitative assessment demonstrate that farmers use agrometeorological information for a variety of choices: making strategic choice on the seed variety and on the geographical distribution of plots, choosing the most appropriate planting date, better tuning crop development cycle with the rhythm of the rains and choosing favourable periods for different cultural operations. Globally, the effects of all these good practices can be summarized by an increase of crops productivity and a decrease of cropping costs (including opportunity cost) in terms of inputs and working time.

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Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

Remote Sensing ◽

10.3390/rs13112060 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2060

Author(s):

Trylee Nyasha Matongera ◽

Onisimo Mutanga ◽

Mbulisi Sibanda ◽

John Odindi

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Critical Role ◽

Spectral Characteristics ◽

Vegetation Indices ◽

Machine Learning Algorithms ◽

Special Focus ◽

Land Surface Phenology ◽

Spectral Vegetation Indices ◽

Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

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