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Journal articles on the topic 'Crop forecasting'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Guenthner, Joseph F. "Forecasting Annual Vegetable Plantings." HortTechnology 2, no. 1 (January 1992): 89–91. http://dx.doi.org/10.21273/horttech.2.1.89.

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Vegetable producers and marketers make business decisions based on supply estimates. The U.S. Dept. of Agriculture provides estimates of planting intentions for field crops but not for most vegetable crops. This study developed models that can be used to forecast vegetable crop plantings. Multiple linear regression analysis was used to determine the factors that influence plantings of potatoes and onions. Field crop planting intentions, industry structure, lagged values of plantings, prices received, price volatility, and the price of sugar beets were found to be significant factors. The models and/or methods used in this study should be useful to those interested in forecasting vegetable plantings.

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2

Veenadhari, Dr S. "Crop Advisor: A Software Tool for Forecasting Paddy Yield." Bonfring International Journal of Data Mining 6, no. 3 (July 31, 2016): 34–38. http://dx.doi.org/10.9756/bijdm.10461.

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3

Buklagin, D. S. "Agricultural crop yield forecasting methods." Machinery and Equipment for Rural Area, no. 12 (December 20, 2020): 25–28. http://dx.doi.org/10.33267/2072-9642-2020-12-25-28.

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The main areas of the development and use of digital technologies and systems for forecasting the yield of agricultural crops based on satellite data are described. Proposals are given for the development of research in the field of the use of space technologies and their widespread use in agriculture.

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4

Mariño, Miguel A., John C. Tracy, and S. Alireza Taghavi. "Forecasting of reference crop evapotranspiration." Agricultural Water Management 24, no. 3 (November 1993): 163–87. http://dx.doi.org/10.1016/0378-3774(93)90022-3.

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5

MOHAN, S., and N. ARUMUGAM. "Forecasting weekly reference crop evapotranspiration series." Hydrological Sciences Journal 40, no. 6 (December 1995): 689–702. http://dx.doi.org/10.1080/02626669509491459.

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6

Stone, Roger C., and Holger Meinke. "Operational seasonal forecasting of crop performance." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2109–24. http://dx.doi.org/10.1098/rstb.2005.1753.

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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.

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7

Gotsch, N., and P. Rieder. "Forecasting future developments in crop protection." Crop Protection 9, no. 2 (April 1990): 83–89. http://dx.doi.org/10.1016/0261-2194(90)90083-j.

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8

Ben Dhiab, Ali, Mehdi Ben Mimoun, Jose Oteros, Herminia Garcia-Mozo, Eugenio Domínguez-Vilches, Carmen Galán, Mounir Abichou, and Monji Msallem. "Modeling olive-crop forecasting in Tunisia." Theoretical and Applied Climatology 128, no. 3-4 (January 13, 2016): 541–49. http://dx.doi.org/10.1007/s00704-015-1726-1.

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9

Jain, R. C., and Ranjana Agrawal. "Probability Model for Crop Yield Forecasting." Biometrical Journal 34, no. 4 (1992): 501–11. http://dx.doi.org/10.1002/bimj.4710340410.

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10

Bojar, W., L. Knopik, J. Żarski, and R. Kuśmierek-Tomaszewska. "Integrated assessment of crop productivity based on the food supply forecasting." Agricultural Economics (Zemědělská ekonomika) 61, No. 11 (June 6, 2016): 502–10. http://dx.doi.org/10.17221/159/2014-agricecon.

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11

Reddy, A. Srinivasa, P. CHAKRADHAR, PAVAN KUMAR P, and Teja Santosh. "Demand Forecasting and Demand Supply Management of Vegetables in India: A Review and Prospect." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 1 (January 16, 2018): 7170–78. http://dx.doi.org/10.24297/ijct.v17i1.7305.

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Vegetable quantity arrival to market varies every day with which its prices also changes rapidly. This paper analyses the factors that affect the rapid change in prices of vegetables such as demand forecasting, demand supply management, erroneous statistics of vegetables, storage facilities, and supply chain system, etc. Government of India has no control over the production of horticultural and agricultural crops, sometimes under produced and sometimes over produced, which makes demand supply management hard-won. This paper mainly focuses on advantages of demand forecasting and demand supply management of vegetable crops and their effects on farmers and consumers. The Government should find a novel method or a system which gets crop data from farmer, does demand forecasting on day to day basis, control crop acreages, generate accurate statistics and do demand supply management of crops.

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12

Sowmya Shree, K. M., and M. N. Veena. "Review on Different Traditional and Machine Learning Techniques for Irrigation Planning for Crop Yield Prediction." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 3831–38. http://dx.doi.org/10.1166/jctn.2020.9084.

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Agriculture is one of the major factors of Indian economy which involves production of crops. Production crops may be food crops or commercial crops like wheat, maize, grams, rice, millets, cotton etc. The productivity of the crops is administered by its weather conditions. Forecasting the crop yields is a challenging task which needs to be addressed. Several data mining technologies are explored for forecasting the crop yields, yet, solutions are complex and infeasible. This paper presents a review of machine learning techniques for irrigation planning to forecast the crop yields are discussed. Various machine learning methods like prediction, classification, regression, clustering are discussed. This study brings a need for an enhancement in irrigation planning using machine learning techniques. To increase the productivity rate of the crops, variable analysis also play a significant part in defining predictive models. Comparative analysis is done on machine learning techniques and its benefits are explored.

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13

Young, Linda J. "Agricultural Crop Forecasting for Large Geographical Areas." Annual Review of Statistics and Its Application 6, no. 1 (March 7, 2019): 173–96. http://dx.doi.org/10.1146/annurev-statistics-030718-105002.

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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.

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14

SAITO, Genya. "Crop Yield Forecasting Using Remote Sensing Technique." JOURNAL OF THE BREWING SOCIETY OF JAPAN 86, no. 1 (1991): 2–7. http://dx.doi.org/10.6013/jbrewsocjapan1988.86.2.

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15

Singh, R. K., T. R. Singh, and U. Kaushal. "Note on the Crop Yield Forecasting Methods." Asian Journal of Agricultural Research 13, no. 1 (December 15, 2018): 1–5. http://dx.doi.org/10.3923/ajar.2019.1.5.

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16

Mayer, D. G., and R. A. Stephenson. "Statistical forecasting of the Australian macadamia crop." Acta Horticulturae, no. 1109 (February 2016): 265–70. http://dx.doi.org/10.17660/actahortic.2016.1109.43.

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17

Saengseedam, Panudet, and Nantachai Kantanantha. "Spatio-temporal model for crop yield forecasting." Journal of Applied Statistics 44, no. 3 (April 21, 2016): 427–40. http://dx.doi.org/10.1080/02664763.2016.1174197.

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18

Gardner, A. S., I. M. D. Maclean, K. J. Gaston, and L. Bütikofer. "Forecasting future crop suitability with microclimate data." Agricultural Systems 190 (May 2021): 103084. http://dx.doi.org/10.1016/j.agsy.2021.103084.

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19

Guo, William W., and Heru Xue. "Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/857865.

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Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to predict the wheat yield with respect to a given plantation area with a high accuracy compared with the temporal NARNN and NARXNN models. However, the high accuracy of the spatial NN model in crop yield forecasting is limited to the forecasting of crop yield only within normal ranges. Users must be cautious when using either NARNN or NARXNN for crop yield forecasting due to their inconsistency between the results of training and forecasting.

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20

C. R, Manjunath. "Crop Mandi-Demand and Price Forecasting of Agricultural Crops through Mobile Application." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 4512–20. http://dx.doi.org/10.22214/ijraset.2018.4741.

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21

Bicknell, K., G. Greer, and D. A. J. Teulon. "The value of forecasting BYDV in autumn sown cereals." New Zealand Plant Protection 53 (August 1, 2000): 87–92. http://dx.doi.org/10.30843/nzpp.2000.53.3618.

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Crop Food Research has developed a model to forecast the occurrence of severe barley yellow dwarf virus (BYDV) incidence in autumnsown wheat crops in Canterbury based on alate aphid flights This information is potentially valuable to growers in Canterbury who can modify their inputs each year according to the risk of BYDV This paper uses expected gross margins to estimate the financial benefit of the Crop Food Research BYDV Forecast Service to arable growers in Canterbury Results suggest that the value of the BYDV Forecast Service is positive but varies greatly depending upon forecast accuracy and adoption rate

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22

Li, Yaling, Fujin Yi, Yanjun Wang, and Richard Gudaj. "The Value of El Niño-Southern Oscillation Forecasts to China’s Agriculture." Sustainability 11, no. 15 (August 2, 2019): 4184. http://dx.doi.org/10.3390/su11154184.

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This study aims to estimate the value of El Niño-Southern Oscillation (ENSO) forecasting to China’s agricultural sector. This study applies the Weibull distribution to model crop yields under different ENSO phases. Under the framework of Bayesian decision theory, this research pioneers the application of China’s Agricultural Sector Model to translate the yield effects resulting from ENSO variations into economic effects. Results show that ENSO exerts noticeable and heterogeneous effects on crop yields over selected crops across different regions. In addition, ENSO forecasting is useful for farmers’ cropping decisions and positively impacts economic surplus. The findings present that the value of this information is generally positive and rises with improved forecast accuracy, with the value of perfect forecasting estimated to be as substantial as CNY 3168 million. However, the value of ENSO forecasting is relatively small in the context of China’s tremendous agricultural output. This study is the first to evaluate the value of ENSO forecasting to China’s agriculture sector and has critical implications for the promotion of a Chinese ENSO forecast system.

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23

Balaji Prabhu B. V. and M. Dakshayini. "Computational Performance Analysis of Neural Network and Regression Models in Forecasting the Societal Demand for Agricultural Food Harvests." International Journal of Grid and High Performance Computing 12, no. 4 (October 2020): 35–47. http://dx.doi.org/10.4018/ijghpc.2020100103.

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Demand forecasting plays an important role in the field of agriculture, where a farmer can plan for the crop production according to the demand in future and make a profitable crop business. There exist a various statistical and machine learning methods for forecasting the demand, selecting the best forecasting model is desirable. In this work, a multiple linear regression (MLR) and an artificial neural network (ANN) model have been implemented for forecasting an optimum societal demand for various food crops that are commonly used in day to day life. The models are implemented using R toll, linear model and neuralnet packages for training and optimization of the MLR and ANN models. Then, the results obtained by the ANN were compared with the results obtained with MLR models. The results obtained indicated that the designed models are useful, reliable, and quite an effective tool for optimizing the effects of demand prediction in controlling the supply of food harvests to match the societal needs satisfactorily.

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24

de Wit, A. J. W., and C. A. van Diepen. "Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs." International Journal of Applied Earth Observation and Geoinformation 10, no. 4 (December 2008): 414–25. http://dx.doi.org/10.1016/j.jag.2007.10.004.

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25

Bolton, Douglas K., and Mark A. Friedl. "Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics." Agricultural and Forest Meteorology 173 (May 2013): 74–84. http://dx.doi.org/10.1016/j.agrformet.2013.01.007.

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26

T. S. G. Peiris. "FORECASTING THE CROP YIELD OF A COCONUT ESTATE." CORD 5, no. 02 (June 1, 1989): 34. http://dx.doi.org/10.37833/cord.v5i02.226.

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Seasonal Autoregressive Integrated Moving Average (ARIMA) process of (0,1,2) x (0,1,1) x 6 that best fits a set of crop‑wise coconut yield data, in Bandirippuwa, Lunuwila is identified without using variance stabilization transformation. In this process the present value of the series may be described as a linear function of the past observation of the series and past disturbances. The physical factors such as rainfall, temperature, day length etc. are not required for this method, however the past crop figures in the estate is needed. While such model is useful for short term forecasting, it also gives the upper and lower limits of the forecasts at a given probability. These intervals would provide the quantified information on the degree of duration of the forecasts.

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27

Ghaly, Hanan. "FORECASTING WHEAT CROP PRODUCTION IN THE DESERT GOVERNORATES." Arab Universities Journal of Agricultural Sciences 24, no. 2 (September 1, 2016): 387–98. http://dx.doi.org/10.21608/ajs.2016.14333.

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28

Toreti, A., A. Maiorano, G. De Sanctis, H. Webber, A. C. Ruane, D. Fumagalli, A. Ceglar, S. Niemeyer, and M. Zampieri. "Using reanalysis in crop monitoring and forecasting systems." Agricultural Systems 168 (January 2019): 144–53. http://dx.doi.org/10.1016/j.agsy.2018.07.001.

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29

Matis, J. H., T. Saito, W. E. Grant, W. C. Iwig, and J. T. Ritchie. "A Markov chain approach to crop yield forecasting." Agricultural Systems 18, no. 3 (January 1985): 171–87. http://dx.doi.org/10.1016/0308-521x(85)90030-7.

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30

Dharmaraja, S., Vidyottama Jain, Priyanka Anjoy, and Hukum Chandra. "Empirical Analysis for Crop Yield Forecasting in India." Agricultural Research 9, no. 1 (May 18, 2019): 132–38. http://dx.doi.org/10.1007/s40003-019-00413-x.

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31

Gautam, Ratnesh, and Anand K. Sinha. "Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India." Journal of Water and Land Development 30, no. 1 (September 1, 2016): 51–56. http://dx.doi.org/10.1515/jwld-2016-0021.

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AbstractEvapotranspiration is the one of the major role playing element in water cycle. More accurate measurement and forecasting of Evapotranspiration would enable more efficient water resources management. This study, is therefore, particularly focused on evapotranspiration modelling and forecasting, since forecasting would provide better information for optimal water resources management. There are numerous techniques of evapotranspiration forecasting that include autoregressive (AR) and moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), Thomas Feiring, etc. Out of these models ARIMA model has been found to be more suitable for analysis and forecasting of hydrological events. Therefore, in this study ARIMA models have been used for forecasting of mean monthly reference crop evapotranspiration by stochastic analysis. The data series of 102 years i.e. 1224 months of Bokaro District were used for analysis and forecasting. Different order of ARIMA model was selected on the basis of autocorrelation function (ACF) and partial autocorrelation (PACF) of data series. Maximum likelihood method was used for determining the parameters of the models. To see the statistical parameter of model, best fitted model is ARIMA (0, 1, 4) (0, 1, 1)12.

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32

Backhouse, D. "Forecasting the risk of crown rot between successive wheat crops." Australian Journal of Experimental Agriculture 46, no. 11 (2006): 1499. http://dx.doi.org/10.1071/ea04189.

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Published data from long-term trials at Moree, New South Wales (1986–1996), and Billa Billa, Queensland (1986–1993), were analysed to determine the factors that influence the incidence of crown rot, caused by Fusarium pseudograminearum, in successive stubble-retained, no-till wheat crops and to examine the feasibility of developing a forecasting system for the disease. Polyetic progress of the epidemics could be described by a form of the logistic growth model with a carrying capacity (K) about 5% higher than the maximum recorded incidence at each site. Infection rate between seasons was positively correlated with yield and in-crop rainfall in the previous season, both of which were indicators of biomass. Infection rate was negatively correlated with rainfall parameters during the summer fallows, which were indicators of conditions favouring residue decomposition. In-crop rainfall, stored soil moisture and temperature parameters were not significantly correlated with infection rates. Multiple regressions based on incidence in the previous season, summer rainfall and either yield or in-crop rainfall in the previous season accounted for 65–81% of the variation in disease incidence at Moree and 86% of the variation in incidence at Billa Billa. Simplified parameters for use in on-farm forecasting systems were explored. The most useful of these was the square root of the product of incidence and either yield or in-crop rainfall, which gave sufficiently accurate predictions at each site to estimate the qualitative risk of crown rot in the following crop. This could be used to decide whether management options such as resistant varieties, rotations or burning were required.

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33

Challinor, Andrew Juan. "Assessing crop genetic resources for adaptation using ensemble climate and crop yield forecasting." IOP Conference Series: Earth and Environmental Science 6, no. 37 (February 1, 2009): 372011. http://dx.doi.org/10.1088/1755-1307/6/37/372011.

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34

Lalić, B., A. Firanj Sremac, J. Eitzinger, R. Stričević, S. Thaler, I. Maksimović, M. Daničić, D. Perišić, and Lj Dekić. "Seasonal forecasting of green water components and crop yield of summer crops in Serbia and Austria." Journal of Agricultural Science 156, no. 5 (February 14, 2018): 658–72. http://dx.doi.org/10.1017/s0021859618000047.

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AbstractA probabilistic crop forecast based on ensembles of crop model output estimates, presented here, offers an ensemble of possible realizations and probabilistic forecasts of green water components, crop yield and green water footprints (WFs) on seasonal scales for selected summer crops. The present paper presents results of an ongoing study related to the application of ensemble forecasting concepts in crop production. Seasonal forecasting of crop water use indicators (evapotranspiration (ET), water productivity, green WF) and yield of rainfed summer crops (maize, spring barley and sunflower), was performed using the AquaCrop model and ensemble weather forecast, provided by The European Centre for Medium-range Weather Forecast. The ensemble of estimates obtained was tested with observation-based simulations to assess the ability of seasonal weather forecasts to ensure that accuracy of the simulation results was the same as for those obtained using observed weather data. Best results are obtained for ensemble forecast for yield, ET, water productivity and green WF for sunflower in Novi Sad (Serbia) and maize in Groß-Enzersdorf (Austria) – average root mean square error (2006–2014) was <10% of observation-based values of selected variables. For variables yielding a probability distribution, capacity to reflect the distribution from which their outcomes will be drawn was tested using an Ignorance score. Average Ignorance score, for all locations, crops and variables varied from 1.49 (spring barley ET in Groß-Enzersdorf) to 3.35 (sunflower water productivity in Groß-Enzersdorf).

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35

Strashnaya, A. I., O. V. Bereza, and P. S. Klang. "Forecasting grain crop yield based on the integration of ground and satellite data in the subjects of the Southern Federal District." Hydrometeorological research and forecasting 2 (June 23, 2021): 110–37. http://dx.doi.org/10.37162/2618-9631-2021-2-111-137.

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Forecasting grain crop yield based on the integration of ground and satellite data in the subjects of the Southern Federal District / Strashnaya A.I.., Bereza O.V., Klang P.S. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 111-137. The results of research on the effect of agrometeorological conditions on the yield of grain and leguminous crops are presented. The role of farming culture in increasing productivity and the importance of meteorological factors in the yield variability are demonstrated. The frequency of droughts of various intensities in the subjects of the Southern Federal District in 2001–2020 is calculated as compared to 1981–2000. The NDVI vegetation index highly correlates with the grain crop yield. The average long-term dynamics of NDVI for the vegetation weeks is calculated, which allows assessing conditions for the yield formation in a particular year in comparison with the average long-term ones. The periods of the most effective use of NDVI in yield forecasts are determined. The developed regression models for yield forecasting based on the joint use of ground-based and satellite data are presented. Keywords: agrometeorological conditions, drought, grain crops, yield, satellite information, forecast

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Rana, Ranbir Singh, Bhosale Arjun Vaijinath, Sanjay Kumar, and Ranu Pathania. "Forecasting phenology of mustard crop in North-western Himalayas." Journal of Applied and Natural Science 9, no. 1 (March 1, 2017): 230–36. http://dx.doi.org/10.31018/jans.v9i1.1178.

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Field experiments were conducted during rabiseason of 2007-08 and 2008-09 to study the phenology, thermal indices and its subsequent effect on dry matter accumulation of mustard (Brassica juncea L.) varieties viz., RCC-4, Kranti and Varuna grown under varying environmental conditions of Himachal Pradesh. The early sown (10th October) crop varieties took maximum average growing degree days for flower initiation (492±1), 50% flower-ing (682±1), pod initiation (742±1), 90% pod formation (811±4) and maturity (1394±8) which decreased with subse-quent delay in sowing time and recorded lowest under late sown (9th November) crop. The accumulated helio-thermal units and photo-thermal units decreased from 9824 to 7467 oC day hour and 19074 to 15579 oC day hour, respectively. High heat-use efficiency was obtained under late sown condition on 30th October. The heat-use efficiency (HUE) was high at 90% pod formation stage as compared to other stages in all the varieties and sowing dates (except 9th November sowing). The early sown (10th October) crop had maximum calendar days and cumula-tive pan evaporation (158 days and 448.2 mm) followed by normal (20th and 30th October) (153 days and 434 mm) and late (9th November) (138 days and 403.1 mm) sown crop indicating higher water requirement under early sow-ing. The predictive regression models explained 83-85% variation in dry matter yield in three varieties of mustard. The agro climatic indices are important determinants for temperature, radiations and photoperiods behaviors of crop. The accurate predictions of crop phenology are useful inputs for crop simulation modeling and crop management, and used for climate change assessment and simulated adaptations in present scenarios.

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37

V, Dr Bindhu. "Design and Development of Automatic Micro Controller based Weather Forecasting Device." March 2020 2, no. 1 (March 5, 2020): 1–9. http://dx.doi.org/10.36548/jei.2020.1.001.

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The entailment for weather forecasting to take the essential pre-cautious measures in our regular routines and elude the unwanted fatalities has made this more attractive area of research. Particularly in the rural areas the weather forecasting enables the farmers to have an effective crop management, avoiding the destruction in the crops and increasing the yield. In order to have a real time weather forecasting the proposed method in the paper tries to develop an automatic weather forecasting device based on the microcontroller. The proposed method utilizes the sensors to monitor the weather changes and engages the raspberry pi to process the information gathered and convey it to the end user. The proposed system was tested by implementing it in the Indian delta districts and the accuracy, precision and flexibility in the forecasting was evinced by the data output observed over and done with the Thinkspeak .Web

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38

Medar, Ramesh A., Vijay S. Rajpurohit, and Anand M. Ambekar. "Sugarcane Crop Yield Forecasting Model Using Supervised Machine Learning." International Journal of Intelligent Systems and Applications 11, no. 8 (August 8, 2019): 11–20. http://dx.doi.org/10.5815/ijisa.2019.08.02.

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39

Narasimhamurthy, Venkata. "Rice Crop Yield Forecasting Using Random Forest Algorithm SML." International Journal for Research in Applied Science and Engineering Technology V, no. X (October 23, 2017): 1220–25. http://dx.doi.org/10.22214/ijraset.2017.10176.

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40

Мельник, С. І., О. І. Присяжнюк, Є. М. Стариченко, К. М. Мажуга, В. В. Бровкін, О. М. Мартинов, and В. В. Маслечкін. "Model of adaptive information system for forecasting crop productivity." Plant varieties studying and protection 16, no. 1 (April 12, 2020): 63–77. http://dx.doi.org/10.21498/2518-1017.16.1.2020.201349.

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41

Musayev, T. I. "Application Polynomial Time Series Models for Grapes Crop Forecasting." Economy of agricultural and processing enterprises, no. 4 (April 2020): 34–38. http://dx.doi.org/10.31442/0235-2494-2020-0-4-34-38.

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42

Han, Suk-Ho. "A Study of Building Rice Crop Yield Forecasting Model." Journal of Agriculture & Life Science 50, no. 3 (June 30, 2016): 219–29. http://dx.doi.org/10.14397/jals.2016.50.3.219.

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Rana, Amit Kumar. "Comparative Study on Fuzzy Models for Crop Production Forecasting." Mathematics and Statistics 8, no. 4 (July 2020): 451–57. http://dx.doi.org/10.13189/ms.2020.080412.

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Garg, Bindu, Shubham Aggarwal, and Jatin Sokhal. "Crop yield forecasting using fuzzy logic and regression model." Computers & Electrical Engineering 67 (April 2018): 383–403. http://dx.doi.org/10.1016/j.compeleceng.2017.11.015.

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Kantanantha, Nantachai, Nicoleta Serban, and Paul Griffin. "Yield and Price Forecasting for Stochastic Crop Decision Planning." Journal of Agricultural, Biological, and Environmental Statistics 15, no. 3 (March 24, 2010): 362–80. http://dx.doi.org/10.1007/s13253-010-0025-7.

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Jain, R. C., Ranjana Agrawal, and K. N. Singh. "A Within Year Growth Model for Crop Yield Forecasting." Biometrical Journal 34, no. 7 (1992): 789–99. http://dx.doi.org/10.1002/bimj.4710340705.

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Fedotova, E. V., Yu A. Maglinets, R. V. Brezhnev, and A. I. Starodubtsev. "THE EXPERIENCE IN CROP YIELDS FORECASTING USING SIMULATION MODELS." Bulletin of KSAU, no. 8 (2020): 43–48. http://dx.doi.org/10.36718/1819-4036-2020-8-43-48.

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Garde, Y. A., B. S. Dhekale, and S. Singh. "Different approaches on pre harvest forecasting of wheat yield." Journal of Applied and Natural Science 7, no. 2 (December 1, 2015): 839–43. http://dx.doi.org/10.31018/jans.v7i2.693.

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Agriculture is backbone of Indian economy, contributing about 40 per cent towards the Gross National Product and provide livelihood to about 70 per cent of the population. According to the national income published in Economic survey 2014-15, by the CSO, the share of agriculture in total GDP is 18 percent in 2013-14. The Rabi crops data released by the Directorate of Economics and Statistics recently indicates that the total area coverage has declined; area under wheat has gone down by 2.9 per cent. Therefore needs to be do research to study weathersituation and effect on crop production. Pre harvest forecasting is true essence, is a branch of anticipatory sciences used for identifying and foretelling alternative feasible future. Crop yield forecast provided useful information to farmers, marketers, government agencies and other agencies. In this paper Multiple Linear Regression (MLR) Technique and discriminant function analysis were derived for estimating wheat productivity for the district of Varanasi in eastern Uttar Pradesh. The value of Adj. R2 varied from 0.63 to 0.94 in different models. It is observed that high value of Adj. R2 in the Model-2 which indicated that it is appropriate forecast model than other models, also the value of RMSE varied from minimum 1.17 to maximum 2.47. The study revealed that MLR techniques with incorporating technical and statistical indicators (Model 2) was found to be better for forecasting of wheat crop yield on the basis of both Adjusted R2 and RMSE values.

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Abou Ali, H., D. Delparte, and L. M. Griffel. "FROM PIXEL TO YIELD: FORECASTING POTATO PRODUCTIVITY IN LEBANON AND IDAHO." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W11 (February 14, 2020): 1–7. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w11-1-2020.

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Abstract. Idaho and Lebanon rely on potatoes as an economically important crop. NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and MSAVI2 (Modified Soil Adjusted Vegetation Index 2) indices were calculated from PlanetScope satellite imagery for the 2017 growing season cloud free days. Variations in vegetation health were tracked over time and correlated to yield data provided by growers in Idaho. Based on ordinary least squares regression an Idaho yield forecast model was developed. Vegetation response during the growth stage at which potato tubers were filling out was significant in predicting yield for both the Norkotah and Russet potato variety. This corresponded to a week with high recorded temperatures that impacted the health status of the crops. The yield forecasting model was validated with a cross validation approach and then applied to potato fields in Lebanon. The Idaho model successfully displayed yield variation in crops for Lebanon. Spectral indices along with field topography allow the prediction of yield based on the crop type and variety.

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Banerjee, Swagata “Ban”, and Babatunde A. Obembe. "Econometric Forecasting of Irrigation Water Demand Conserves a Valuable Natural Resource." Journal of Agricultural and Applied Economics 45, no. 3 (August 2013): 557–68. http://dx.doi.org/10.1017/s107407080000506x.

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Natural causes (such as droughts), non-natural causes (such as competing uses), and government policies limit the supply of water for agriculture in general and irrigating crops in particular. Under such reduced water supply scenarios, existing physical models reduce irrigation proportionally among crops in the farmer's portfolio, disregarding temporal changes in economic and/or institutional conditions. Hence, changes in crop mix resulting from expectations about risks and returns are ignored. A method is developed that considers those changes and accounts for economic substitution and expansion effects. Forecasting studies based on this method with surface water in Georgia and Alabama demonstrate the relative strength of econometric modeling vis-à-vis physical methods. Results from a study using this method for ground water in Mississippi verify the robustness of those findings. Results from policy-induced simulation scenarios indicate water savings of 12% to 27% using the innovative method developed. Although better irrigation water demand forecasting in crop production was the key objective of this pilot project, conservation of a valuable natural resource (water) has turned out to be a key consequence.

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