Presentation by Dr Sultan Ahmed, Director of Natural Resources Management and Research, Department of Environment, Government of Bangladesh at CCAFS webinar 'Exploring GHG mitigation potential in rice production' on 18 September 2014.
Climate Smart Agriculture Project: using policy and economic analysis as a ba...FAO
www.fao.org/climatechange/epic
This presentation was prepared as background to the FAO TCI Investment Days 2013 held at IFAD on 17-18 December. The presentation provides an overview of the theory of change of the FAO-EC Climate-Smart Agriculture project and highlights the contribution of the project in providing sound evidence for investment proposals.
Crop modeling has been applied at various scales in agriculture, from precision farming, to farm planning, to watershed or regional policy development. Crop models are mechanistic process-based models in response to daily weather inputs, predict soil traits, daily photosynthesis, growth, and crop management.
Presentation by Dr Sultan Ahmed, Director of Natural Resources Management and Research, Department of Environment, Government of Bangladesh at CCAFS webinar 'Exploring GHG mitigation potential in rice production' on 18 September 2014.
Climate Smart Agriculture Project: using policy and economic analysis as a ba...FAO
www.fao.org/climatechange/epic
This presentation was prepared as background to the FAO TCI Investment Days 2013 held at IFAD on 17-18 December. The presentation provides an overview of the theory of change of the FAO-EC Climate-Smart Agriculture project and highlights the contribution of the project in providing sound evidence for investment proposals.
Crop modeling has been applied at various scales in agriculture, from precision farming, to farm planning, to watershed or regional policy development. Crop models are mechanistic process-based models in response to daily weather inputs, predict soil traits, daily photosynthesis, growth, and crop management.
What is Climate-Smart Agriculture? Background, opportunities and challengesCIFOR-ICRAF
This presentation by Alexandre Meybeck of the FAO was given at a session titled "Using climate-smart technologies to scale up climate-smart agriculture practices" at the Global Landscapes Forum in Lima, Peru, on December 7, 2014.
The panel presentation and discussion focused on how these climate-smart technologies can be scaled-up to benefit smallholder farmers. This was followed by a public debate.
Presentation by Mei Xie, Ph.D working for the World Bank - Climate Change Group. Presented during a pre - SBSTA meeting on CSA Alliance: Building Climate Change Resilience in Africa held on 30th May 2014 in Bonn, Germany http://ccafs.cgiar.org/csa-alliance-building-climate-change-resilience-africa#.U42GUihCCTs
Crop is defined as an “Aggregation of individual plant species grown in a unit area for economic purpose”.
Growth is defined as an “Irreversible increase in size and volume and is the consequence of differentiation and distribution occurring in the plant”.
Simulation is defined as “Reproducing the essence of a system without reproducing the system itself”. In simulation the essential characteristics of the system are reproduced in a model, which is then studied in an abbreviated time scale.
Presentation by Alan Nicol from IWMI at the Land and Water Advantage event on the sidelines of COP23.
More information about the event series: https://bit.ly/AgAdvantage
How to achieve climate-smart agriculture and the potential triple-win that can be achieved from these practices such as adaptation, mitigation and increasing livelihoods.
Free webinar on " Agroforestry to soil and Water conservation "
Soil conservation is key to environmental sustainability: It helps protect natural resources and watersheds, restores habitats for plants and wildlife, improves water quality and makes soil healthier. Soil conservation also creates economic opportunity.
Purpose:
The purpose of this webinar is to bring new knowledge on soil and water conservation under changing climate. Best management practices must be revised and developed to expected changes in climate.
EPIC - Environmental Policy Integrated Model
This is a crop model used to access all the future output prior to the yield of a crop.
It analyzes all the parameters through the input which we provide.
It is highly useful for farmers to prevent crop losses by using such technologies.
Bio-physical impact analysis of climate change with EPIC
Presented by Christine Heumesser at the AGRODEP Workshop on Analytical Tools for Climate Change Analysis
June 6-7, 2011 • Dakar, Senegal
For more information on the workshop or to see the latest version of this presentation visit: http://www.agrodep.org/first-annual-workshop
MOSAICC: An inter-disciplinary system of models to evaluate the impact of cli...FAO
MOSAICC: An inter-disciplinary system of models to evaluate the impact of climate change on agriculture, By Francois Delobel and Oscar Rojas ,Land and Water Days in Near East & North Africa, 15-18 December 2013, Amman, Jordan
What is Climate-Smart Agriculture? Background, opportunities and challengesCIFOR-ICRAF
This presentation by Alexandre Meybeck of the FAO was given at a session titled "Using climate-smart technologies to scale up climate-smart agriculture practices" at the Global Landscapes Forum in Lima, Peru, on December 7, 2014.
The panel presentation and discussion focused on how these climate-smart technologies can be scaled-up to benefit smallholder farmers. This was followed by a public debate.
Presentation by Mei Xie, Ph.D working for the World Bank - Climate Change Group. Presented during a pre - SBSTA meeting on CSA Alliance: Building Climate Change Resilience in Africa held on 30th May 2014 in Bonn, Germany http://ccafs.cgiar.org/csa-alliance-building-climate-change-resilience-africa#.U42GUihCCTs
Crop is defined as an “Aggregation of individual plant species grown in a unit area for economic purpose”.
Growth is defined as an “Irreversible increase in size and volume and is the consequence of differentiation and distribution occurring in the plant”.
Simulation is defined as “Reproducing the essence of a system without reproducing the system itself”. In simulation the essential characteristics of the system are reproduced in a model, which is then studied in an abbreviated time scale.
Presentation by Alan Nicol from IWMI at the Land and Water Advantage event on the sidelines of COP23.
More information about the event series: https://bit.ly/AgAdvantage
How to achieve climate-smart agriculture and the potential triple-win that can be achieved from these practices such as adaptation, mitigation and increasing livelihoods.
Free webinar on " Agroforestry to soil and Water conservation "
Soil conservation is key to environmental sustainability: It helps protect natural resources and watersheds, restores habitats for plants and wildlife, improves water quality and makes soil healthier. Soil conservation also creates economic opportunity.
Purpose:
The purpose of this webinar is to bring new knowledge on soil and water conservation under changing climate. Best management practices must be revised and developed to expected changes in climate.
EPIC - Environmental Policy Integrated Model
This is a crop model used to access all the future output prior to the yield of a crop.
It analyzes all the parameters through the input which we provide.
It is highly useful for farmers to prevent crop losses by using such technologies.
Bio-physical impact analysis of climate change with EPIC
Presented by Christine Heumesser at the AGRODEP Workshop on Analytical Tools for Climate Change Analysis
June 6-7, 2011 • Dakar, Senegal
For more information on the workshop or to see the latest version of this presentation visit: http://www.agrodep.org/first-annual-workshop
MOSAICC: An inter-disciplinary system of models to evaluate the impact of cli...FAO
MOSAICC: An inter-disciplinary system of models to evaluate the impact of climate change on agriculture, By Francois Delobel and Oscar Rojas ,Land and Water Days in Near East & North Africa, 15-18 December 2013, Amman, Jordan
Linking satellite imagery and crop modeling for integrated assessment of clim...ICRISAT
Crop simulation models are valuable tools for evaluating potential effects of environmental, biological and management factors on crop growth and developments. These models need to be applied at larger scales in order to be economically useful so that the effects of various alternate management strategies across the watershed or the region could be analyzed. Linking crop models with Remote sensing and Geographical Information System (GIS) have demonstrated a strong feasibility of crop modeling applications at a spatial scale.
ICBA-IAEA - Training on water management and use of crop simulation model- ri...ICBA - ag4tomorrow
International Center for Biosaline Agriculture (ICBA) with International Atomic Energy Agency (IAEA) organized a two weeks Water management and use of crop simulation model (Aqua Crop) training from October 02- 12, 2016 in Dubai, UAE
Crops yield estimation through remote sensingCIMMYT
Remote sensing –Beyond images
Mexico 14-15 December 2013
The workshop was organized by CIMMYT Global Conservation Agriculture Program (GCAP) and funded by the Bill & Melinda Gates Foundation (BMGF), the Mexican Secretariat of Agriculture, Livestock, Rural Development, Fisheries and Food (SAGARPA), the International Maize and Wheat Improvement Center (CIMMYT), CGIAR Research Program on Maize, the Cereal System Initiative for South Asia (CSISA) and the Sustainable Modernization of the Traditional Agriculture (MasAgro)
Agriculture crop yield prediction using inertia based cat swarm optimizationIJECEIAES
Crop yield prediction is among the most important and main sources of income in the Indian economy. In this paper, the improved cat swarm optimization (ICSO) based recurrent neural network (RNN) model is proposed for crop yield prediction using time series data. The inertia weight parameter is added to position equation that is selected randomly, and a new velocity equation is produced which enhances the searching ability in the best cat area. By using inertia weight, the ICSO enhances performance of feature selection and obtains better convergence in minimum iteration. The RNN is applied to produce direct graph using sequence of data and decides current layer output by involving all other existing calculations. The performance of the model is estimated using coefficient of determination (R2), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE) on the yield from the years 2011 to 2021 with an annual prediction for 120 records of approximately 8 million nuts. The evaluated result shows that the proposed ICSO-RNN model delivers metrics such as R2, MAE, MSE, and RMSE values of 0.99, 0.77, 0.68, and 0.82 correspondingly, which ensures accurate yield prediction when compared with the existing methods which are hybrid reinforcement learning-random forest (RL-RF) and machine learning (ML) methods.
This is about survey the crop yield prediction using some data mining classification methods namely prdiction with classification,residue climate control, feature selection extraction, crop classification models,evaluation metrics, accuracy level,classification decision, result analysis,rain fall pH, principal component analysis, information gain
This paper is the continuation of the paper published by the authors Arun Balaji and Baskaran [2].
Multiple linear regression (MLR) equations were developed between the years of rice cultivation and Feed
Forward Back Propagation Neural Network (FFBPNN) method of predicted area of rice cultivation / rice
production for different districts pertaining to Kuruvai, Samba and Kodai seasons in Tamilnadu. The
average r2 value in area of cultivation is 0.40 in Kuruvai season, 0.42 in Samba season and 0.46 in Kodai
season, where as the r2 value in rice production is 0.31 in Kuruvai season, 0.23 in Samba season and
0.42 in Kodai season. The Rice Data Simulator (RDS) predicted the area of rice cultivation and rice
production using the MLR equations developed in this research. The range of average predicted area for
Kuruvai, Samba and Kodai seasons varies from 12052.52 ha to 13595.32 ha, 48998.96 ha to 53324.54 ha
and 4241.23 ha to 6449.88 ha respectively whereas the range of average predicted rice production varies
from 45132.88 tonnes to 46074.48 tonnes in Kuruvai, 128619 tonnes to 139693.29 tonnes in Samba and
15446.07 to 20573.50 tonnes in Kodai seasons. The mean absolute relative error (ARE) between the
FFBPNN and multiple regression methods of prediction of area of rice cultivation was found to be 15.58%,
8.04% and 26.34% for the Kuruvai, Samba and the Kodai seasons respectively. The ARE for the rice
production was found to be 17%, 11.80% and 24.60% for the Kuruvai, Samba and the Kodai seasons
respectively. The paired t test between the FFBPNN and MLR methods of predicted area of cultivation in
Kuruvai shows that there is no significant difference between the two types of prediction for certain
districts.
Extension of grid soil sampling technology; application of extended Technolog...researchagriculture
Grid soil sampling technology is one of the most important information technologies in agriculture. Application of these technologies is a way to understand the extent of needed nutrient elements of soil. The purpose of this research is to investigate the attitude and intention to the extension of grid soil sampling technologies among agricultural specialists in Iran. A survey was used to collect data from 249 specialists. The results using Structural Equation Modeling (SEM) showed that attitude to use is the most important determinant of intention to extension. Attitude of confidence, observability and triability positively affect intention to extension of these technologies. Perceived ease of use indirectly influences the intention to extension through attitude to use.
Article Citation:
Kurosh. Rezaei-Moghaddam, Saeid. Salehi, Abdol-azim. Ajili.
Extension of grid soil sampling technology: application of extended Technology Acceptance Model (TAM).
Journal of Research in Agriculture (2012) 1(1): 078-087.
Full Text:
http://www.jagri.info/documents/AG0013.pdf
Extension of grid soil sampling technology: application of extended Technolog...researchagriculture
Grid soil sampling technology is one of the most important information
technologies in agriculture. Application of these technologies is a way to understand
the extent of needed nutrient elements of soil. The purpose of this research is to
investigate the attitude and intention to the extension of grid soil sampling
technologies among agricultural specialists in Iran. A survey was used to collect data
from 249 specialists. The results using Structural Equation Modeling (SEM) showed
that attitude to use is the most important determinant of intention to extension.
Attitude of confidence, observability and triability positively affect intention to
extension of these technologies. Perceived ease of use indirectly influences the
intention to extension through attitude to use.
Crop yield prediction using ridge regression.pdfssuserb22f5a
Crop yield prediction using deep neural networks with data mining concepts by applying multi model ensembles using ridge regression to increase accuracy, precision, recall,and f measure. Combining neural networks with regression increase high satisfactory crop yield prediction.the support vector regression is slow convergence , stuck in local minima. But ridge regression analyse multicollinearity in multiple regression.
FEED FORWARD BACK PROPAGATION NEURAL NETWORK COUPLED WITH RICE DATA SIMULATOR...ijcseit
This paper is the continuation of the paper published by the authors Arun Balaji and Baskaran [2].
Multiple linear regression (MLR) equations were developed between the years of rice cultivation and Feed
Forward Back Propagation Neural Network (FFBPNN) method of predicted area of rice cultivation / rice
production for different districts pertaining to Kuruvai, Samba and Kodai seasons in Tamilnadu. The
average r
2
value in area of cultivation is 0.40 in Kuruvai season, 0.42 in Samba season and 0.46 in Kodai
season, where as the r2
value in rice production is 0.31 in Kuruvai season, 0.23 in Samba season and
0.42 in Kodai season. The Rice Data Simulator (RDS) predicted the area of rice cultivation and rice
production using the MLR equations developed in this research. The range of average predicted area for
Kuruvai, Samba and Kodai seasons varies from 12052.52 ha to 13595.32 ha, 48998.96 ha to 53324.54 ha
and 4241.23 ha to 6449.88 ha respectively whereas the range of average predicted rice production varies
from 45132.88 tonnes to 46074.48 tonnes in Kuruvai, 128619 tonnes to 139693.29 tonnes in Samba and
15446.07 to 20573.50 tonnes in Kodai seasons. The mean absolute relative error (ARE) between the
FFBPNN and multiple regression methods of prediction of area of rice cultivation was found to be 15.58%,
8.04% and 26.34% for the Kuruvai, Samba and the Kodai seasons respectively. The ARE for the rice
production was found to be 17%, 11.80% and 24.60% for the Kuruvai, Samba and the Kodai seasons
respectively. The paired t test between the FFBPNN and MLR methods of predicted area of cultivation in
Kuruvai shows that there is no significant difference between the two types of prediction for certain
districts.
FEED FORWARD BACK PROPAGATION NEURAL NETWORK COUPLED WITH RICE DATA SIMULATOR...ijcseit
This paper is the continuation of the paper published by the authors Arun Balaji and Baskaran [2].
Multiple linear regression (MLR) equations were developed between the years of rice cultivation and Feed
Forward Back Propagation Neural Network (FFBPNN) method of predicted area of rice cultivation / rice
production for different districts pertaining to Kuruvai, Samba and Kodai seasons in Tamilnadu. The
average r
2
value in area of cultivation is 0.40 in Kuruvai season, 0.42 in Samba season and 0.46 in Kodai
season, where as the r2
value in rice production is 0.31 in Kuruvai season, 0.23 in Samba season and
0.42 in Kodai season. The Rice Data Simulator (RDS) predicted the area of rice cultivation and rice
production using the MLR equations developed in this research. The range of average predicted area for
Kuruvai, Samba and Kodai seasons varies from 12052.52 ha to 13595.32 ha, 48998.96 ha to 53324.54 ha
and 4241.23 ha to 6449.88 ha respectively whereas the range of average predicted rice production varies
from 45132.88 tonnes to 46074.48 tonnes in Kuruvai, 128619 tonnes to 139693.29 tonnes in Samba and
15446.07 to 20573.50 tonnes in Kodai seasons. The mean absolute relative error (ARE) between the
FFBPNN and multiple regression methods of prediction of area of rice cultivation was found to be 15.58%,
8.04% and 26.34% for the Kuruvai, Samba and the Kodai seasons respectively. The ARE for the rice
production was found to be 17%, 11.80% and 24.60% for the Kuruvai, Samba and the Kodai seasons
respectively. The paired t test between the FFBPNN and MLR methods of predicted area of cultivation in
Kuruvai shows that there is no significant difference between the two types of prediction for certain
districts.
Yield Forecasting to Sustain the Agricultural Transportation UnderStochastic ...IJRESJOURNAL
ABSTRACT: Agricultural transportation is a major part of the United States’ transportation systems. This system follows a complex multimodal network consisting of highway, railway, and waterways which are mostly based on the yield of the agricultural commodities and their market values. The yield of agricultural commodities is dependent on stochastic environment such as weather conditions, rainfall, soil type and natural disasters. Different techniques such as leaf growth index, Normalized Difference Vegetation Index (NDVI), and regression analysis are used to forecast the yield for the end of harvest season. The yield forecasting techniques are used to predict the agricultural transportation needs and improve the cost minimization. This study provides a model for yield forecasting using NDVI data, Geographical Information System (GIS), and statistical analysis. A case study is presented to demonstrate this model with a novel tool for collecting NDVI data.
Selection of crop varieties and yield prediction based on phenotype applying ...IJECEIAES
In India, agriculture plays an important role in the nation’s gross domestic product (GDP) and is also a part of civilization. Countries’ economies are also influenced by the amount of crop production. All business trading involves farming as a major factor. In order to increase crop production, different technological advancements are developed to acquire the information required for crop production. The proposed work is mainly focused on suitable crop selection across districts in Tamil Nadu, considering phenotype factors such as soil type, climatic factors, cropping season, and crop region. The key objective is to predict the suitable crop for the farmers based on their locations, soil types, and environmental factors. This results in less financial loss and a shorter crop production timeframe. Combined feature selection (CFS)-based machine regression helps increase crop production rates. A brief comparative analysis was also made between various machine learning (ML) regression algorithms, which majorly contributed to the process of crop selection considering phenotype factors. Stacked long short-term memory (LSTM) classifiers outperformed other decision tree (DT), k-nearest neighbor (KNN), and logistic regression (LR) with a prediction accuracy of 93% with the lowest classification accuracy metrics. The proposed method can help us select the perfect crop for maximum yield.
The Common Agricultural Policy of the EU (CAP) contains incentives for consolidation of resources and integration of organizations. The entire CAP follows the concept of efficiency achieved through product structure optimization and economies of scale. CAP imposes an advantage for the concept of efficiency at each level of the economic system. For this reason, integration-horizontal and vertical has been imposed as more important for farmers than competition. Such an approach stimulates large-scale exchange and consolidation of resources, at the same time being a prerequisite for problems for market competition. The Chicago School addresses the concept of efficiency in relation to the “antitrust paradox”.
Crop yield prediction using data mining techniques.pdfssuserb22f5a
Agriculture is the main source of occupation which forms the backbone of our country. It involves the production of crops which may be either food crops or commercial crops. The productivity of crop yield is significantly influenced by various parameters such as rainfall, farm capacity, temperature, crop population density, humidity, irrigation, fertilizer application, solar radiation, type of soil, depth, tillage and soil organic matter. An accurate crop yield prediction support decision-makers in the agriculture sector to predict the yield effectively. Machine learning techniques and deep learning techniques play a significant role in the analysis of data for crop yield prediction. However, the selection of appropriate techniques from the pool of available techniques imposes challenges to the researchers concerning the chosen crop. In this paper, an analysis has been performed on various deep learning and machine learning techniques. To know the limitations of each technique, a comparative analysis is carried out in this paper. In addition to this, a suggestion is provided to further improve the performance of crop yield prediction.
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Jianqiang Ren_Simulation of regional winter wheat yield by EPIC model.ppt
1. 2011-7-29 Jianqiang REN 1,2 , Zhongxin CHEN 1,2 , Huajun TANG 1,2 , Fushui YU 1,2 , Qing HUANG 1,2 Simulation of regional winter wheat yield by combining EPIC model and remotely sensed LAI based on global optimization algorithm 1 Key Laboratory of Resources Remote-Sensing & Digital Agriculture, Ministry of Agriculture, China 2 Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences 1/24
2. Outline 2/24 Introduction 1 Study area 2 Method 3 3 Data preparation 4 4 Results and analysis 5 Conclusions and future work 6
