scholarly journals Operational seasonal forecasting of crop performance

2005 ◽  
Vol 360 (1463) ◽  
pp. 2109-2124 ◽  
Author(s):  
Roger C Stone ◽  
Holger Meinke
Keyword(s):  
Decision Making ◽  
Agricultural Production ◽  
Crop Production ◽  
Prediction Models ◽  
Weather Prediction ◽  
Seasonal Forecast ◽  
Seasonal Forecasting ◽  
Operational Management ◽  
Crop Performance

Integrated, interdisciplinary crop performance forecasting systems, linked with appropriate decision and discussion support tools, could substantially improve operational decision making in agricultural management. Recent developments in connecting numerical weather prediction models and general circulation models with quantitative crop growth models offer the potential for development of integrated systems that incorporate components of long-term climate change. However, operational seasonal forecasting systems have little or no value unless they are able to change key management decisions. Changed decision making through incorporation of seasonal forecasting ultimately has to demonstrate improved long-term performance of the cropping enterprise. Simulation analyses conducted on specific production scenarios are especially useful in improving decisions, particularly if this is done in conjunction with development of decision-support systems and associated facilitated discussion groups. Improved management of the overall crop production system requires an interdisciplinary approach, where climate scientists, agricultural scientists and extension specialists are intimately linked with crop production managers in the development of targeted seasonal forecast systems. The same principle applies in developing improved operational management systems for commodity trading organizations, milling companies and agricultural marketing organizations. Application of seasonal forecast systems across the whole value chain in agricultural production offers considerable benefits in improving overall operational management of agricultural production.

scholarly journals On the Statistical Reflection of Long-Term Growth in Agricultural Production

2020 ◽  
Vol 27 (5) ◽  
pp. 36-47
Author(s):  
D. S. Ternovsky ◽  
V. Ya. Uzun
Keyword(s):  
Theoretical Model ◽  
Agricultural Production ◽  
Crop Production ◽  
Complex Structure ◽  
Livestock Production ◽  
Chain Indices ◽  
Statistical Reflection ◽  
The Relationship ◽  
Constant Prices

The article presents the results of a study aimed at proving the existence of systematic error in traditional calculations of long-term growth rates of agricultural production based on chain indices of agricultural production. According to the authors, the article also introduces a more accurate assessment of its dynamics with the account to the structure of the relationship between prices and the volume of agricultural production. The paper describes a theoretical model that is a methodological basis for the study and explains the discrepancy in assessing the dynamics of agricultural production using chain indices and indexes at constant prices. It allows establishing differences in the ratios of the Laspeyres, Paasche, and Lowe indices for crop and livestock production, due to factors in the formation of demand and the complex structure of the relationship between the price level and the volume of production. The adequacy of the constructed theoretical model is proved based on aggregated data that eliminated the influence of incompleteness of the initial information. As a result, it was established that livestock production is characterized by time-distributed changes in prices and quantity of products, which makes it possible to assess its dynamics using both chain indices and symmetric indices. It is proved that the dynamics of crop production cannot be adequately described using chain indices, since a positive correlation of prices of the previous period and production volumes of the current period causes an overstatement of the index in comparable prices of the previous year. Based on calculations within the proposed aggregated model, it is proved that the use of constant prices as the Lowe index weights, updated every five years, is an acceptable approximation of the Fisher symmetric index. Application of the indicated methodology for calculating the index to the data on Russian agricultural production by main types of products in 1990-2018 allowed to establish an overstatement of dynamics by 11.9%. The main difference falls on crop production (+ 19.6%), while for livestock - the differences are insignificant (-0.7%).

scholarly journals The International Soil Moisture Network: an open-source data hosting facility in support of meteorology and climate science

2021 ◽  
Author(s):  
Ivana Petrakovic ◽  
Irene Himmelbauer ◽  
Daniel Aberer ◽  
Lukas Schremmer ◽  
Philippe Goryl ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
International Cooperation ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Source Surface ◽  
Federal Institute

<p>The International Soil Moisture Network (ISMN, https://ismn.earth) is international cooperation to establish and maintain a unique centralized global data hosting facility, making in-situ soil moisture data easily and freely accessible (Dorigo et al., 2021). Initiated in 2009 as a community effort through international cooperation (ESA, GEWEX, GTN-H, GCOS, TOPC, HSAF, QA4SM, C3S, etc.), the ISMN is an essential means for validating and improving global satellite soil moisture products, land surface-, climate-, and hydrological models. <br><br>The ISMN is a widely used, reliable, and consistent in-situ data source (surface and sub-surface) collected by a myriad of data organizations on a voluntary basis.  The in-situ soil moisture measurements are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free. Currently, 71 networks are participating with more than 2800 stations distributed on a global scale and a steadily increasing number of user communities. Long term time series with mainly hourly timestamps from 1952 – up to near-real-time are stored in the database, including daily near-real-time updates. Besides soil moisture in our database are stored other meteorological variables as well (air temperature, soil temperature, precipitation, snow depth, etc.).<br><br>The ISMN provides benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S) and Global Land Service (CGLS), and the online validation tool QA4SM. ISMN data is widely used in a variety of scientific fields (e.g., climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc).<br><br>To validate the land surface representations of meteorological forecasting models soil moisture from the ISMN has often been used. The development of various generations of TESSEL models used both in the Integrated Forecasting Systems and reanalysis products of ECMWF, greatly profited from soil moisture and temperature data from the ISMN. Using ISMN data several studies assessed the soil moisture skill of the Weather Research and Forecasting Model (WRF) and assessed the forecast skill or new implementations of numerical weather prediction models.<br><br>We greatly acknowledge the financial support provided by ESA through various projects: SMOSnet International Soil Moisture Network, IDEAS+, and QA4EO.<br><br>To ensure a long-term funding for the ISMN operations, several ideas were perused together with ESA. A partner for this task could be found within the International Center for Water Resources and Global Change (ICWRGC) hosted by the German Federal Institute of Hydrology (BfG). <br><br>In this session, we want to give an overview and future outlook of the ISMN, highlighting its unique features and discuss challenges in supporting the hydrological research community in need of freely available, standardized, and quality-controlled datasets. </p>

scholarly journals Impact of the structure of agricultural production to the financial health of farms

2013 ◽  
Vol 61 (7) ◽  
pp. 2317-2325
Author(s):  
Daniel Kopta
Keyword(s):  
Cash Flow ◽  
Agricultural Production ◽  
Crop Production ◽  
Animal Production ◽  
Production Yield ◽  
Production Structure ◽  
Financial Health ◽  
Different Types ◽  
Production Losses

The first part of the paper deals with the influence of individual commodities on the profitability and risks of farms. Production structure was given thought share of twelve basic crops in total agricultural production yield. Volume of accumulated profit for five-year income was chosen as viability criterion. The research did not show that specialization in one of the commodities had significantly influenced achieved profitability. The only exception is the production of milk, which clearly lead to lower profitability. Production structure determined the risk of farms. Farms were constantly threatened by both negative profitability, and also steep fluctuations of cash flow (in other of long-term positive profitability), leading to temporary loss of solvency. The analysis showed that different types of production structures lead to different types of threats. The probability of falling into production losses, or that the loss is so great that not even cover variable costs (a farm finds itself under the point of termination of production) was calculated using the EaR method. The results again supported previous findings. Loss is highly likely to be achieved in commodities of animal production. For commodities of crop production the probability of loss was roughly a half, but the probability of exceeding a period of variable costs is higher.

Precipitation Nowcasting using Deep Neural Network

2020 ◽  
Author(s):  
Mohamed Chafik Bakey ◽  
Mathieu Serrurier
Keyword(s):  
Traffic Management ◽  
Prediction Models ◽  
Short Term Memory ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Proposed Model ◽  
Anticipation Time ◽  
Memory Network ◽  
Convolutional Encoder

<p>Precipitation nowcasting is the prediction of the future precipitation rate in a given geographical region with an anticipation time of a few hours at most. It is of great importance for weather forecast users, for activitites ranging from outdoor activities and sports competitions to airport traffic management. In contrast to long-term precipitation forecasts which are traditionally obtained from numerical weather prediction models, precipitation nowcasting needs to be very fast. It is therefore more challenging to obtain because of this time constraint. Recently, many machine learning based methods had been proposed. In this work, we develop an original deep learning approach. We formulate precipitation nowcasting issue as a video prediction problem where both input and prediction target are image sequences. The proposed model combines a Long Short-Term Memory network (LSTM) with a convolutional encoder-decoder network (U-net). Experiments show that our method captures spatiotemporal correlations and yields meaningful forecasts</p>

Seasonal forecasting and the predictability of the rainy and dry seasons for Peru, Tanzania and India

2020 ◽  
Author(s):  
Thomas Möller ◽  
Lydia Gates
Keyword(s):  
East Africa ◽  
Weather Conditions ◽  
Forecast Model ◽  
Seasonal Forecast ◽  
Climate Impact ◽  
Seasonal Forecasting ◽  
Mean Deviation ◽  
Local Agriculture ◽  
Forecast Models

<p>With seasonal forecast models we investigate whether it is possible to give the people in Tanzania, Peru and India time to adapt and prepare to different weather conditions. In recent years, these countries have repeatedly experienced devastating droughts or floods, such as in East Africa in November 2019.</p><p>Under the framework of the research project EPICC (East Africa Peru India Climate Capacities) supported by the BMU (Federal Ministry for the Environment, Nature Conservation and Nuclear Safety), we aim to set up a seasonal forecast system. The goal is to make the data useful for the hydrologists at the project partner from PIK (Potsdam Institute for Climate Impact Research) for integration in a tool for adaption in local agriculture in the affected countries (India, Peru and Tanzania). In this study, we validate a number of variables of predicted anomalies in seasonal forecast models as well as of a multimodel product.</p><p>There are different methods of seasonal predictability, based on slow variations of boundary conditions, coupled ocean-atmosphere model simulations as well as the concept of ensembles, multi-model ensembles and uncertainties. The focus in this study is on the intercomparison of the single models and the multimodel in a forecast range between 1 and 6 months. In particular, we investigate three-month mean deviation from the long-term mean. It is important for the population (especially for the agriculture industry) in the focus region to know whether in a certain period (rainy season, dry season, El Nino etc.) the next 3 months will be colder, warmer, drier or even wetter compared to the long-term mean.</p><p>Due to the fact, that various seasonal forecasting models perform differently, it is the challenge, to find the best fitting seasonal forecast model for each of the affected countries.</p>

scholarly journals Long-Term and High-Resolution Global Time Series of Brightness Temperature from Copula-Based Fusion of SMAP Enhanced and SMOS Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1842 ◽  
Author(s):  
Christof Lorenz ◽  
Carsten Montzka ◽  
Thomas Jagdhuber ◽  
Patrick Laux ◽  
Harald Kunstmann
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
High Resolution ◽  
Brightness Temperature ◽  
Spatial Resolution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Brightness Temperatures ◽  
Climate Analysis

Long and consistent soil moisture time series at adequate spatial resolution are key to foster the application of soil moisture observations and remotely-sensed products in climate and numerical weather prediction models. The two L-band soil moisture satellite missions SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) are able to provide soil moisture estimates on global scales and in kilometer accuracy. However, the SMOS data record has an appropriate length of 7.5 years since late 2009, but with a coarse resolution of ∼25 km only. In contrast, a spatially-enhanced SMAP product is available at a higher resolution of 9 km, but for a shorter time period (since March 2015 only). Being the fundamental observable from passive microwave sensors, reliable brightness temperatures (Tbs) are a mandatory precondition for satellite-based soil moisture products. We therefore develop, evaluate and apply a copula-based data fusion approach for combining SMAP Enhanced (SMAP_E) and SMOS brightness Temperature (Tb) data. The approach exploits both linear and non-linear dependencies between the two satellite-based Tb products and allows one to generate conditional SMAP_E-like random samples during the pre-SMAP period. Our resulting global Copula-combined SMOS-SMAP_E (CoSMOP) Tbs are statistically consistent with SMAP_E brightness temperatures, have a spatial resolution of 9 km and cover the period from 2010 to 2018. A comparison with Service Soil Climate Analysis Network (SCAN)-sites over the Contiguous United States (CONUS) domain shows that the approach successfully reduces the average RMSE of the original SMOS data by 15%. At certain locations, improvements of 40% and more can be observed. Moreover, the median NSE can be enhanced from zero to almost 0.5. Hence, CoSMOP, which will be made freely available to the public, provides a first step towards a global, long-term, high-resolution and multi-sensor brightness temperature product, and thereby, also soil moisture.

scholarly journals Fast Numerical Wind Turbine Candidate Site Evaluation

2021 ◽  
Vol 11 (7) ◽  
pp. 2953
Author(s):  
Matija Perne ◽  
Primož Mlakar ◽  
Boštjan Grašič ◽  
Marija Zlata Božnar ◽  
Juš Kocijan
Keyword(s):  
Wind Turbine ◽  
Prediction Models ◽  
Weather Prediction ◽  
Measurement Data ◽  
Experimental Modelling ◽  
Numerical Weather ◽  
Nonlinear Regression Models ◽  
Wind Speeds ◽  
Long Time

A long-term measured wind speed time series from the location is typically used when deciding on placing a small wind turbine at a particular location. These data take a long time to collect. The presented novel method of measuring for a shorter time, using the measurement data for training an experimental model, and predicting the wind in a longer time period enables one to avoid most of the wait for the data collection. As the model inputs, the available long-term signals that consist of measurements from the meteorological stations in the vicinity and numerical weather predictions are used. Various possible experimental modelling methods that are based on linear or nonlinear regression models are tested in the field sites. The study area is continental with complex terrain, hilly topography, diverse land use, and no prevailing wind. It is shown that the method gives good results, showing linear regression is most advantageous, and that it is easy enough to use to be practically applicable in small wind projects of limited budget. The method is better suited to small turbines than to big ones because the turbines sited at low heights and in areas with low average wind speeds, where numerical weather prediction models are less accurate, tend to be small.

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