Climate Services for Resilient Development (CSRD) Partnership’s work in Latin America

The Climate Services for Resilient Development (CSRD) Partnership is a private-public collaboration led by USAID, which aims to increase resilience to climate change in developing countries through the development and dissemination of climate services. The partnership began with initial projects in three countries: Colombia, Ethiopia, and Bangladesh. The International Center for Tropical Agriculture (CIAT) was the lead organization for the Colombian CSRD efforts – which then expanded to encompass work in the whole Latin American region

Keberkesanan modul infusi kemahiran berfikir aras tinggi pembelajaran luar bilik darjah (iKBAT-PLBD) bagi bidang pembelajaran sukatan dan geometri

Kemahiran berfikir aras tinggi (KBAT) merupakan satu kemahiran berfikir yang sangat diperlukan dalam mendepani cabaran kehidupan masa kini terutama dalam bidang matematik. Oleh itu, kajian ini dijalankan untuk mengkaji sama ada KBAT matematik pelajar dapat ditingkatkan dengan menggunakan modul infusi Kemahiran Berfikir Aras Tinggi - Pembelajaran Luar Bilik Darjah (iKBAT–PLBD) atau tidak? Justeru itu, satu kerangka perancangan telah dibuat terhadap empat kemahiran tertinggi dalam Taksonomi Bloom semakan semula yang juga merupakan konstruk utama dalam KBAT. Konstruk KBAT tersebut ialah konstruk menganlisis, mengaplikasi menilai dan mencipta. Sampel kajian ini melibatkan 120 pelajar tingkatan 1 di empat buah sekolah yang berbeza di negeri Johor. Dalam menjalankan kajian kuasi eksperimental ini, data dikumpul melalui kajian keputusan ujian pra dan ujian pos sebelum dan selepas menggunakan modul bagi kumpulan rawatan. Manakala pendekatan PdP tradisional pula digunakan bagi kumpulan kawalan. Hasil daripada analisis data menunjukkan bahawa aktiviti pembelajaran dan pemudahcaraan (PdPc) yang bertunjangkan modul iKBAT–PLBD telah dapat meningkatkan penguasaan matematik pelajar dalam kempat-empat tahap KBAT serta bagi keseluruhan tahap. Dapatan kajian ini menunjukkan terdapat perbezaan yang signifikasi antara kumpulan kawalan dan kumpulan rawatan terhadap peningkatan KBAT pelajar dalam matematik dengan menggunakan pendekatan iKBAT–PLBD bagi tahap mengaplikasi, menganalisis, menilai, mencipta juga secara keseluruhan. Kesimpulannya, kajian ini dapat memberi manfaat kepada semua pihak termasuk pihak Kementerian Pendidikan Malaysia (KPM), pihak pentadbiran sekolah, ibubapa, guru matematik malah bagi pelajar itu dari segi pengubalan dasar yang berkaitan, pengaplikasian dan sebagai satu bukti keberkesanan dalam proses pemerkasaan KBAT matematik di Malaysia

Adaboost CNN with Horse Herd Optimization Algorithm to Forecast the Rice Crop Yield

Over three billion people use rice every day, and it occupies about 12% of the nation's arable land. Since, due to the growing population and the latest climate change projections, it is critical for governments and planners to obtain timely and accurate rice yield estimates. The proposed work develops a rice crop yield forecasting model based on soil nutrients. Soil nutrients and crop production statistics are taken as an input for the proposed method. In ensemble learning, there are three categories, they are Boosting, Bagging and Stacking. In the proposed method, Boosting technique called Adaboost with Convolutional Neural Network is used to achieve the High accuracy by converting weak classifiers to strong classifiers. Adaptive data cleaning and imputation using frequent values are used as pre-processing approaches in the projected technique. A novel technique known as Convolutional neural network with adaptive boosting (Adaboost) technique is projected and can precisely handle more imbalanced datasets. The data weights are initialized; also the initial CNN is trained utilizing original weights of data. The weights of the second CNN are then modified utilizing the first CNN. These actions will be performed sequentially for all weak classifiers. An optimization algorithm called Horse Herd (HOA) is passed down in the proposed technique to find the optimal weights of the links in the classifier. The proposed method attains 95% accuracy, 87% precision, 85% recall, 5% error, 96% specificity, 87% F1-Score, 97% NPV and 12% FNR value.Thus the designed model as predicted the crop yield prediction in the effective manner

IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY

This study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems

Optimización de la gestión de redes de riego a presión a diferentes escalas mediante Inteligencia Artificial

Factors such as climate change, world population growth or the competition for the water resources make freshwater availability become an increasingly large and complex global challenge. Under this scenario of reduced water availability, increasing droughts frequency and uncertainties associated with a changing climate, the irrigated agriculture sector, particularly in the Mediterranean region, will need to be even more efficient in the use of the water resources. In Spain, many irrigation districts have been modernized in recent years, replacing the obsolete open channels by pressurized water distribution networks towards improvements in water use efficiency. Thanks to this, water use has reduced but the energy demand and the water costs have dramatically increased. Thus, strategies to reduce simultaneously water and energy uses in irrigation districts are required. This thesis consists of nine chapters, which include several models to optimize the management of the irrigation districts and increase the efficiency of water and energy use.Factores tales como el cambio climático, el crecimiento de la población mundial o la competencia por los recursos hídricos hacen que la disponibilidad de agua se esté convirtiendo en un desafío global cada vez más grande y complejo. En este escenario de reducción de la disponibilidad de agua, aumento de la frecuencia de las sequías y de las incertidumbres asociadas a un cambio climático, el sector de la agricultura de regadío, en particular en la región mediterránea, tendrá que ser aún más eficiente en el uso de los recursos hídricos. En España, muchas comunidades de regantes se han modernizado en los últimos años, sustituyendo los obsoletos canales abiertos por redes de distribución de agua a presión con el objetivo de mejorar la eficiencia en el uso del agua. Gracias a esto, el uso del agua se ha reducido, pero la demanda de energía y los costos del agua se han incrementado drásticamente. Por lo tanto, se requieren estrategias para reducir simultáneamente el uso de agua y energía en las comunidades de regantes. Esta tesis consta de nueve capítulos que incluyen varios modelos para optimizar la gestión de las comunidades de regantes y aumentar la eficiencia en el uso del agua y la energía

Trained neural network to predict paddy yield for various input parameters in Tamil Nadu, India

The major objective of the present study was to explore if Artificial Neural Network (ANN) models with back propagation could efficiently predict the rice yield under various climatic conditions; ground-specific rainfall, ground-specific weather variables and historic yield data. The back propagation algorithm will calculate each expected weight using the error rate as the activity level of a unit was altered.  The errors in the model during the training phase were solved during the back-propagation. The paddy yield prediction took various parameters like rainfall, soil moisture, solar radiation, expected carbon, fertilizers, pesticides, and the long-time paddy yield recorded using Artificial Neural Networks. The R2 value on the test set was found to be 93% and it showed that the model was able to predict the paddy yield better for the given data set. The ANN model was tested with learning rates of 0.25 and 0.5. The number of hidden layers in the first layer was 50 and in the second hidden layer was 30. From this, the testing value of R square was 0.97. The observations with the ANN Model showed that i) the best result for the test set was  R2 value of 0.98, ii) the two hidden layers kept with 50 neurons in the first layer and 30 neurons in the second one, iii) the learning rate was of 0.25. With all these configurations, maximum yield is possible from the paddy crop

The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting

The numerous recent breakthroughs in machine learning (ML) make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in space weather. The purpose is twofold. On one hand, we will discuss previous works that use ML for space weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the space weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray-box.Comment: under revie

Predicting Agricultural Commodities Prices with Machine Learning: A Review of Current Research

Agricultural price prediction is crucial for farmers, policymakers, and other stakeholders in the agricultural sector. However, it is a challenging task due to the complex and dynamic nature of agricultural markets. Machine learning algorithms have the potential to revolutionize agricultural price prediction by improving accuracy, real-time prediction, customization, and integration. This paper reviews recent research on machine learning algorithms for agricultural price prediction. We discuss the importance of agriculture in developing countries and the problems associated with crop price falls. We then identify the challenges of predicting agricultural prices and highlight how machine learning algorithms can support better prediction. Next, we present a comprehensive analysis of recent research, discussing the strengths and weaknesses of various machine learning techniques. We conclude that machine learning has the potential to revolutionize agricultural price prediction, but further research is essential to address the limitations and challenges associated with this approach