Agricultural Crop Forecasting for Large Geographical Areas

2019 ◽  
Vol 6 (1) ◽  
pp. 173-196
Author(s):  
Linda J. Young
Keyword(s):  
Remote Sensing ◽  
International Trade ◽  
Food Security ◽  
Survey Data ◽  
Crop Yield ◽  
Government Agencies ◽  
Agricultural Crop ◽  
Official Statistics ◽  
Crop Forecasting ◽  
Response Burden

Crop forecasting is important to national and international trade and food security. Although sample surveys continue to have a role in many national crop forecasting programs, the increasing challenges of list frame undercoverage, declining response rates, increasing response burden, and increasing costs are leading government agencies to replace some or all of survey data with data from other sources. This article reviews the primary approaches currently being used to produce official statistics, including surveys, remote sensing, and the integration of these with meteorological, administrative, or other data. The research opportunities for improving current methods of forecasting crop yield and quantifying the uncertainty associated with the prediction are highlighted.

scholarly journals Water Stress Regime of Irrigated Crops Based on Remote Sensing and Ground-Based Data

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1117
Author(s):  
Anatoly Mikhailovich Zeyliger ◽  
Olga Sergeevna Ermolaeva
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Crop Yield ◽  
Satellite Monitoring ◽  
Agricultural Crop ◽  
Crop Water ◽  
Stress Regime ◽  
Irrigation Water Use ◽  
Crop Water Stress ◽  
Crop Irrigation

In the past few decades, combinations of remote sensing technologies with ground-based methods have become available for use at the level of irrigated fields. These approaches allow an evaluation of crop water stress dynamics and irrigation water use efficiency. In this study, remotely sensed and ground-based data were used to develop a method of crop water stress assessment and analysis. Input datasets of this method were based on the results of ground-based and satellite monitoring in 2012. Required datasets were collected for 19 irrigated alfalfa crops in the second year of growth at three study sites located in Saratovskoe Zavolzhie (Saratov Oblast, Russia). Collected datasets were applied to calculate the dynamics of daily crop water stress coefficients for all studied crops, thereby characterizing the efficiency of crop irrigation. Accordingly, data on the crop yield of three harvests were used. An analysis of the results revealed a linear relationship between the crop yield of three cuts and the average value of the water stress coefficient. Further application of this method may be directed toward analyzing the effectiveness of irrigation practices and the operational management of agricultural crop irrigation.

scholarly journals Association between drought and agricultural productivity using remote sensing data: a case study of Gujarat state of India

2020 ◽  
Vol 11 (S1) ◽  
pp. 189-202 ◽  
Author(s):  
Koyel Sur ◽  
M. M. Lunagaria
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Standardized Precipitation Index ◽  
Remote Sensing Data ◽  
Meteorological Drought ◽  
Drought Indices ◽  
Small Scale ◽  
Agricultural Crop ◽  
Vegetation Condition ◽  
Spatio Temporal

Abstract Drought is a complex hazard which directly affects the water balance of any region. It impacts agricultural, ecological and socioeconomical spheres. It is a global concern. The occurrence of drought is triggered by climatic phenomena which cannot be eliminated. However, its effect can be well managed if actual spatio-temporal information related to crop status influenced by drought is available to decision-makers. This study attempted to assess the efficiency of remote sensing products from space sensors for monitoring the spatio-temporal status of meteorological drought in conjunction with impact on vegetation condition and crop yield. Time series (2000–2019) datasets of the Tropical Rainfall Measuring Mission (TRMM) were used to compute Standardized Precipitation Index (SPI) and MODIS (MODerate resolution Imaging Spectroradiometer) was used to compute Vegetation Condition Index (VCI). Association between SPI and VCI was explored. YAI was calculated from the statistical data records. Final observations are that the agricultural crop yield changed as per the climate variability specific to location. The study indicates drought indices derived from remote sensing give a synoptic view because of the course resolution of the satellite images. It does not reveal the precise relationship to the small-scale crop yield. Remote sensing can be an effective way to monitor and understand the dynamics of the drought and agriculture pattern over any region.

scholarly journals Selection of Independent Variables for Crop Yield Prediction Using Artificial Neural Network Models with Remote Sensing Data

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 609
Author(s):  
Patryk Hara ◽  
Magdalena Piekutowska ◽  
Gniewko Niedbała
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crop Yield ◽  
Climatic Data ◽  
Yield Prediction ◽  
Agricultural Crop ◽  
Independent Variables ◽  
Artificial Neural ◽  
Selection Of

Knowing the expected crop yield in the current growing season provides valuable information for farmers, policy makers, and food processing plants. One of the main benefits of using reliable forecasting tools is generating more income from grown crops. Information on the amount of crop yielding before harvesting helps to guide the adoption of an appropriate strategy for managing agricultural products. The difficulty in creating forecasting models is related to the appropriate selection of independent variables. Their proper selection requires a perfect knowledge of the research object. The following article presents and discusses the most commonly used independent variables in agricultural crop yield prediction modeling based on artificial neural networks (ANNs). Particular attention is paid to environmental variables, such as climatic data, air temperature, total precipitation, insolation, and soil parameters. The possibility of using plant productivity indices and vegetation indices, which are valuable predictors obtained due to the application of remote sensing techniques, are analyzed in detail. The paper emphasizes that the increasingly common use of remote sensing and photogrammetric tools enables the development of precision agriculture. In addition, some limitations in the application of certain input variables are specified, as well as further possibilities for the development of non-linear modeling, using artificial neural networks as a tool supporting the practical use of and improvement in precision farming techniques.

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Analysis of agricultural crop yield prediction using statistical techniques of machine learning

2021 ◽  
Author(s):  
Janmejay Pant ◽  
R.P. Pant ◽  
Manoj Kumar Singh ◽  
Devesh Pratap Singh ◽  
Himanshu Pant
Keyword(s):  
Machine Learning ◽  
Crop Yield ◽  
Statistical Techniques ◽  
Yield Prediction ◽  
Agricultural Crop

How Voters Respond to Currency Crises: Evidence From Turkey

2021 ◽  
pp. 001041402110602
Author(s):  
David A. Steinberg
Keyword(s):  
International Trade ◽  
Public Opinion ◽  
Survey Data ◽  
Currency Crises ◽  
Political Attitudes ◽  
Economic Situation ◽  
Financial Shocks ◽  
The Government ◽  
Original Survey ◽  
And Migration

A burgeoning literature shows that international trade and migration shocks influence individuals’ political attitudes, but relatively little is known about how international financial shocks impact public opinion. This study examines how one prevalent type of international financial shock—currency crises—shapes mass political attitudes. I argue that currency crises reduce average citizens’ support for incumbent governments. I also expect voters’ concerns about their own pocketbooks to influence their response to currency crises. Original survey data from Turkey support these arguments. Exploiting exogenous variation in the currency’s value during the survey window, I show that currency depreciations strongly reduce support for the government. This effect is stronger among individuals that are more negatively affected by depreciation, and it is moderated by individuals’ perceptions of their personal economic situation. This evidence suggests that international financial shocks can strongly influence the opinions of average voters, and it provides further support for pocketbook theories.

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