Academic literature on the topic 'Crop yields – Methodology'
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Journal articles on the topic "Crop yields – Methodology"
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Shirsath, Paresh B., Vinay Kumar Sehgal, and Pramod K. Aggarwal. "Downscaling Regional Crop Yields to Local Scale Using Remote Sensing." Agriculture 10, no. 3 (March 2, 2020): 58. http://dx.doi.org/10.3390/agriculture10030058.
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Local-scale crop yield datasets are not readily available in most of the developing world. Local-scale crop yield datasets are of great use for risk transfer and risk management in agriculture. In this article, we present a simple method for disaggregation of district-level production statistics over crop pixels by using a remote sensing approach. We also quantified the error in the disaggregated statistics to ascertain its usefulness for crop insurance purposes. The methodology development was attempted in Parbhani district of Maharashtra state with wheat and sorghum crops in the winter season. The methodology uses the ratio of Enhanced Vegetation Index (EVI) of pixel to total EVI of the crop pixels in that district corresponding to the growth phase of the crop. It resulted in the generation of crop yield maps at the 500 m resolution pixel (grid) level. The methodology was repeated to generate time-series maps of crop yield. In general, there was a good correspondence between disaggregated crop yield and sub-district level crop yields with a correlation coefficient of 0.9.
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Dmytrenko, V. P., L. P. Odnolyetok, О. О. Kryvoshein, and A. V. Krukivska. "Development of the methodology of estimating of agricultural crop yield potential with consideration of climate and agrophytotechnology impact." Ukrainian hydrometeorological journal, no. 20 (October 29, 2017): 52–60. http://dx.doi.org/10.31481/uhmj.20.2017.06.
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In the paper it is outlined the main methodological positions and the results of the approbation of new approaches to the integrated assessment of the potential of crop yields.
There are considered the theoretical foundations of a joint assessment of the biological, ecological and anthropogenic components of the yield potential of agricultural crops which are based on the ecosystem concept and the mathematical model "Weather-Crop Yield" developed by V. P. Dmytrenko. In the considered approaches the peculiarities of the influence of various environmental factors on the formation of crop yields are determined by indicators of various potential yields - general, climatic and trend (agrotechnological). Each type of yield potential can be used for evaluation of the effectiveness of the conditions of field crop growing for each factor taken into account, as well as the optimality criterion in the agrometeorological adaptation strategies and also as a criterion for the degree of sensitivity of the yield level to the conditions of crops cultivating.
The developed approaches are tested on the example of estimation of long-term dynamics of winter wheat yield potential in Ukraine. According to the results of the evaluation of different factors of the potential of the productivity of winter wheat for the periods 1961-1990 and 1991-2010 the dominant importance of organizational and technological processes in comparison with the contribution of changes of agroclimatic conditions has been determined in both periods.
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Neill, D. E., and G. B. Follas. "Use of crop sensing technology in crop protection research." New Zealand Plant Protection 64 (January 8, 2011): 287. http://dx.doi.org/10.30843/nzpp.2011.64.5993.
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Crop sensing technology is a new tool being rapidly adopted by farmers as a key component of precision agriculture This technology uses sensors to calculate normalized difference vegetative index (NDVI) by emitting red and near infrared light towards the crop and measuring the crops reflectance NDVI is used to evaluate canopy greenness plant biomass and as an indicator of plant health and vigour The methodology relevance and benefits of using this technology in crop protection trials are currently unclear A handheld Greenseeker (Ntech Industries USA) was used to record NDVI on a range of trials from 20082011 to establish whether crop sensing could replace visual assessments for disease and enable yield prediction NDVI readings were compared against other parameters measured in the trials such as disease scores green leaf area percentage and yields In some trials the NDVI followed similar trends to disease green leaf retention and yields However in other cases where clear treatment effects were recorded through visual or yield assessments there were no differences in NDVI between the treatments As NDVI can be affected by a number of factors it was concluded that crop sensing technology can be used as an additional objective measurement in conjunction with standard assessment practice but without further investigation cannot replace traditional assessment methods
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Narayan, Kale Jaydeep. "Review of Crop Yield Prediction using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4626–28. http://dx.doi.org/10.22214/ijraset.2021.36058.
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Machine learning (ML) could be a helpful decision-making tool for predicting crop yields, in addition as for deciding what crops to plant and what to try throughout the crop's growth season. To help agricultural yield prediction studies, variety of machine learning techniques are used. I performed a literature review (LR) to extract and synthesize the algorithms and options employed in crop production prediction analysis. Temperature, rainfall, and soil types are most common measure used in the prediction as per my knowledge, whereas Artificial Neural Networks is the foremost normally used methodology in these models.
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Kirthiga, S. M., and N. R. Patel. "In-Season Wheat Yield Forecasting at High Resolution Using Regional Climate Model and Crop Model." AgriEngineering 4, no. 4 (October 30, 2022): 1054–75. http://dx.doi.org/10.3390/agriengineering4040066.
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In-season crop production forecasts at the regional or sub-regional scale are essential to aid in food security through early warning of harvest shortfall/surplus, tailoring crop management decisions and addressing climatic shock. Considering the efforts to establish a framework towards quantifying the crop yield prediction at regional scales are limited, we investigated the utility of combining crop model with the regional weather prediction model to forecast winter wheat yields over space. The exercise was performed for various lead-times in the regions of Punjab and Haryana for the years 2008–2009. A numerical weather prediction (NWP) model was used to generate micro-meteorological variables at different lead times (1-week, 2-weeks, 3-weeks and 5-weeks) ahead of crop harvest and used within the CERES-Wheat crop simulation model gridded framework at a spatial resolution of 10 km. Various scenarios of the yield forecasts were verified with district-wide reported yield values. Average deviations of −12 to 3% from the actual district-wise wheat yields were observed across the lead times. The 3-weeks-ahead yield forecasts yielded a maximum agreement index of 0.86 with a root mean squared error (RMSE) of 327.75 kg/ha and a relative deviation of −5.35%. The critical crop growth stages were found to be highly sensitive to the errors in the weather forecast, and thus made a huge impact on the predicted crop yields. The 5-weeks-ahead weather forecasts generated anomalous meteorological data during flowering and grain-filling crop growth stages, and thus had the highest negative impact on the simulated yields. The agreement index of the 5-week-ahead forecasts was 0.41 with an RMSE of 415.15 kg ha−1 and relative deviation of −2.77 ± 5.01. The proposed methodology showed significant forecast skill for extended space and time scale crop yield forecasting, offering scope for further research and practical applicability.
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Eser, Adnan, Hajnalka Kató, Laura Kempf, and Márton Jolánkai. "Water footprint of yield protein content of twelve field crop species on a Hungarian crop site." Agrokémia és Talajtan 68, Supplement (December 2019): 53–60. http://dx.doi.org/10.1556/0088.2019.00041.
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Abstract Water availability is one of the major physiological factors influencing plant growth and development. An assessment study has been done at the Szent István University, Gödöllő to evaluate and identify the water footprint of protein yield of field crop species. Twelve field crop species (Sugar beet Beta vulgaris, spring and winter barley Hordeum vulgare, winter wheat Triticum aestivum, maize Zea mays, sunflower Helianthus annuus, peas Pisum sativum, potato Solanum tuberosum, alfalfa Medicago sativa, oilseed rape Brassica napus, rye Secale cereale and oats Avena sativa) were involved in the study. Evapotranspiration patterns of the crops studied have been identified by the regular agroclimatology methodology and physiologically reliable protein ranges within crop yields were evaluated. The results obtained suggest, that water footprint of cereals proved to be the lowest, however maize values were highly affected by the high variability of protein yield. Oilseed crops had considerably high protein yield with medium water efficiency. Alfalfa, potato and sugar beet water footprints were in accordance with their evapotranspiration patterns. Protein based water footprint assessment seems to be more applicable in crop species evaluations than that of yield based methodologies.
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Shevchenko, M. S., L. M. Decyatnik, and K. A. Derevenets-Shevchenko. "Modern systems of agriculture and a new interpretation of crop rotation value of agricultural crops." Scientific Journal Grain Crops 4, no. 2 (December 11, 2020): 319–29. http://dx.doi.org/10.31867/2523-4544/0141.
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Based on a broad experimental study of crop rotation productivity in different locations of the Steppe zone, a correlative model for estimating the role of predecessors in the formation of post-crop yields has been developed. The connection between quality of agrotechnologies and change of degree of crop rotation competitiveness of culture is presented. A retrospective analysis of the efficiency of farming and crop rotation systems showed that the constant improvement of varieties and hybrids of crops and technologies for their cultivation created objective agrobiological grounds for reassessment of predecessors in crop rotation. The main motive for this transformation was that in modern agricultural systems, high-potential biotechnological resources allow to obtain higher crop yields on the worst predecessors than on the best in the past. In order to universalize the evaluation of crop rotation efficiency and model their productivity, it is proposed to introduce a crop rotation depression coefficient, which shows the share of yield remaining after individual predecessors compared to its baseline level after black fallow. The most favorable conditions developed after crops with a coefficient above 0,80 – winter wheat, barley, rape, rye, spring barley, oats. At the same time, the development of post-rotational crops was significantly inhibited by sunflower, corn for grain and silage, beets, sorghum and soybeans, their depression coef-ficient was 0,66–0,78. The proposed methodology of system analysis for the assessment of predecessors opens wider opportunities for the formation of adapted crop rotations, optimization the set of crops to market requirements, formation important adjustments to crop rotations in extreme conditions, regulation crop rotation productivity taking into account agrotechnological modernization. Keywords: crop rotation, tillage, fertilizers, crops, grain, predecessors, harvest, minimization.
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Arumugam, Surendran, Ashok K.R., Suren N. Kulshreshtha., Isaac Vellangany, and Ramu Govindasamy. "Yield variability in rainfed crops as influenced by climate variables." International Journal of Climate Change Strategies and Management 7, no. 4 (November 16, 2015): 442–59. http://dx.doi.org/10.1108/ijccsm-08-2013-0096.
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Purpose – This paper aims to explore the impact of climate change on yields and yield variances in major rainfed crops and measure possible changes in yields under projected climate changes in different agro-climatic zones of Tamil Nadu, India. Although many empirical studies report the influence of climate change on crop yield, only few address the effect on yield variances. Even in such cases, the reported yield variances were obtained through simulation studies rather than from actual observations. In this context, the present study analyzes the impact of climate change on crops yield and yield variance using the observed yields. Design/methodology/approach – The Just-Pope yield function (1978) is used to analyze the impact of climate change on mean yield and variance. The estimated coefficient from Just-Pope yield function and the projected climatic data for the year 2030 are incorporated to capture the projected changes in crop yield and variances. Findings – By the year 2030, the yield of pulses is estimated to decline in all the zones (Northeast, Northwest, Western, Cauvery delta, South and Southern zones), with significant declines in the Northeast zone (6.07 per cent), Cauvery delta zone (3.55 per cent) and South zone (3.54 per cent). Sorghum yield may suffer more in Western zone (2.63 per cent), Southern zone (1.92 per cent) and Northeast zone (1.62 per cent). Moreover, the yield of spiked millet is more likely to decrease in the Southern zone (1.39 per cent), Northeast zone (1.21 per cent) and Cauvery delta zone (0.24 per cent), and the yield of cotton may also decline in the Northeast zone (12.99 per cent), Northwest zone (8.05 per cent) and Western zone (2.10 per cent) of Tamil Nadu, India. Originality/value – The study recommends introducing appropriate crop insurance policies to address possible financial losses to the farmers. Prioritizing area-specific stress-tolerant crop varieties without complementing yield would sustain crops cultivation further.
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Delbridge, Timothy A., and Robert P. King. "How important is the transitional yield (t-yield)? An analysis of reforms to organic crop insurance." Agricultural Finance Review 79, no. 2 (April 1, 2019): 234–54. http://dx.doi.org/10.1108/afr-03-2017-0022.
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PurposeThe USDA’s Risk Management Agency (RMA) made several changes to the crop insurance products available to organic growers for the 2014 crop year. Most notably, a 5 percent premium surcharge was removed and organic-specific transitional yields (t-yields) were issued for the first time. The purpose of this paper is to use farm-level organic crop yield data to analyze the impact of these reforms on producer insurance outcomes and compare the insurance options for new organic growers.Design/methodology/approachThis study uses a unique panel data set of organic corn and soybean yields to analyze the impact of organic crop insurance reforms. Actual Production History values and premium rates are calculated for each farm and crop yield sequence. Producer loss ratios and subsidized premium wedges are compared for yield, revenue and area-risk products before and after the instituted reforms.FindingsResults indicate that RMA succeeded in improving the actuarial soundness of the organic insurance program, though further refinement of organic t-yields may be necessary to accurately reflect the yield potential of organic producers and avoid reductions in program participation.Originality/valueThis paper provides insight into the effectiveness of reforms intended to improve the actuarial soundness of organic crop insurance and demonstrates the effect that the reforms are likely to have on new and existing organic farms. Because this analysis uses data collected independently of RMA and includes farms that may or may not have purchased crop insurance, it avoids the self-selection problems that might affect analyses using crop insurance program data.
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SHARIFIFAR, Amin, Hadi GHORBANI, and Fereydoon SARMADIAN. "Soil suitability evaluation for crop selection using fuzzy sets methodology." Acta agriculturae Slovenica 107, no. 1 (April 6, 2016): 159. http://dx.doi.org/10.14720/aas.2016.107.1.16.
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In this study appraisal of four different agricultural land evaluation methods including the so-called Storie method, square root method, maximum limitation method and fuzzy sets method, was done. The study was performed in Bastam region, located in Semnan province at the north east of Iran.<strong> </strong>Three crops including tomato, wheat and potato were assessed for the purpose of this research. Soil characteristics assessed were rooting depth, CaCo<sub>3, </sub>organic carboncontent, clay content, pH and slope gradient. Statistical analyses were done at significance levels of <em>α </em>= 0.1 and <em>α</em> = 0.05. Results of regression between land indices, calculated through the four methods, with observed yields of the crops, showed that the regression were significant in fuzzy sets method for all of the assessed crops at <em>p </em>= 0.05 but not significant in maximum limitation method for any of the crops. The Storie and square root methods also showed a significant correlation with wheat yield at <em>p </em>= 0.1. This study was a demonstrative test of fuzzy sets theory in land suitability evaluation for agricultural uses, which revealed that this methodology is the most correct method in given circumstances.
Dissertations / Theses on the topic "Crop yields – Methodology"
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Chen, Xiangtuo. "Statistical Learning Methodology to Leverage the Diversity of Environmental Scenarios in Crop Data : Application to the prediction of crop production at large-scale." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC055.
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La prévision du rendement des cultures est toujours une question primordiale. De nombreuses recherches ont été menées avec cet objectif en utilisant diverses méthodologies. Généralement, les méthodes peuvent être classées en approches basées sur les modèles et en approches basées sur les données.Les approches basées sur les modèles reposent sur la modélisation mécaniste des cultures. Ils décrivent la croissance des cultures en interaction avec leur environnement comme systèmes dynamiques. Comme ces modèles sont basés sur la description mécanique des processus biophysiques, ils impliquent potentiellement un grand nombre de variables d'état et de paramètres, dont l'estimation n'est pas simple. En particulier, les problèmes d'estimation des paramètres résultant sont généralement non linéaires et conduisent à des problèmes d'optimisation non-convexes dans un espace multidimensionnel. De plus, l’acquisition de données est très difficile et nécessite un travail expérimental lourd afin d’obtenir les données appropriées pour l’identification du modèle.D'un autre côté, les approches basées sur les données pour la prévision du rendement nécessitent des données provenant d'un grand nombre de scénarios environnementaux, mais les données sont plus simples à obtenir: (données climatiques et rendement final). Cependant, les perspectives de ce type de modèles se limitent principalement à la prévision de rendement.La première contribution originale de cette thèse consiste à proposer une méthodologie statistique pour calibrer les modèles mécanistes potentiellement complexes, lorsque des ensembles de données avec différents scénarios environnementaux et rendements sont disponibles à grande échelle. Nous l'appellerons Méthodologie d'estimation de paramètres multi-scénarios (MuScPE). Les principales étapes sont les suivantes:Premièrement, nous tirons parti des connaissances préalables sur les paramètres pour leur attribuer des distributions a priori pertinentes et effectuons une analyse de sensibilité globale sur les paramètres du modèle afin de sélectionner les paramètres les plus importants à estimer en priorité.Ensuite, nous mettons en œuvre une méthode d’optimisation efficace non convexe, l’optimisation parallèle des essaims de particules, pour rechercher l’estimateur MAP (maximum a posteriori) des paramètres;Enfin, nous choisissons la meilleure configuration en ce qui concerne le nombre de paramètres estimés par les critères de sélection de modèles. Il y a en effet un compromis à trouver entre d’un côté l'ajustement aux données, et d'un autre côté la variance du modèle et la complexité du problème d'optimisation à résoudre.Cette méthodologie est d'abord testée avec le modèle CORNFLO, un modèle de culture fonctionnel pour le maïs.La seconde contribution de la thèse est la comparaison de cette méthode basée sur un modèle mécaniste avec des méthodes classiques d'apprentissage statistique basées sur les données. Nous considérons deux classes de méthodes de régression: d'une part, les méthodes statistiques dérivées de la régression linéaire généralisée qui permettent de simplifier le modèle par réduction dimensionnelle (régressions Ridge et Lasso, Régression par composantes principales ou régression partielle des moindres carrés) et d'autre part les méthode de régression de machine learning basée sur des modèles non-linéaires ou des techniques de ré-échantillonnage comme la forêt aléatoire, le réseau de neurones et la régression SVM.Enfin, une régression pondérée est appliquée pour prédire la production à grande échelle. La production de blé tendre, une culture de grande importance économique en France, est prise en exemple. Les approches basées sur les modèles et sur les données ont également été comparées pour déterminer leur performance dans la réalisation de cet objectif, ce qui est finalement la troisième contribution de cette thèseCrop yield prediction is a paramount issue in agriculture. Considerable research has been performed with this objective relying on various methodologies. Generally, they can be classified into model-driven approaches and data-driven approaches.The model-driven approaches are based on crop mechanistic modelling. They describe crop growth in interaction with their environment as dynamical systems. Since these models are based on the mechanical description of biophysical processes, they potentially imply a large number of state variables and parameters, whose estimation is not straightforward. In particular, the resulting parameter estimation problems are typically non-linear, leading to non-convex optimisation problems in multi-dimensional space. Moreover, data acquisition is very challenging and necessitates heavy specific experimental work in order to obtain the appropriate data for model identification.On the other hand, the data-driven approaches for yield prediction necessitate data from a large number of environmental scenarios, but with data quite easy to obtain: climatic data and final yield. However, the perspectives of this type of models are mostly limited to prediction purposes.An original contribution of this thesis consists in proposing a statistical methodology for the parameterisation of potentially complex mechanistic models, when datasets with different environmental scenarios and large-scale production records are available, named Multi-scenario Parameter Estimation Methodology (MuScPE). The main steps are the following:First, we take advantage of prior knowledge on the parameters to assign them relevant prior distributions and perform a global sensitivity analysis of the model parameters to screen the most important ones that will be estimated in priority;Then, we implement an efficient non-convex optimisation method, the parallel particle swarm optimisation, to search for the MAP (maximum a posterior) estimator of the parameters;Finally, we choose the best configuration regarding the number of estimated parameters by model selection criteria. Because when more parameters are estimated, theoretically, the calibrated model could explain better the variance of the output. Meanwhile, it increases also difficulty for optimization, which leads to uncertainty in calibration.This methodology is first tested with the CORNFLO model, a functional crop model for the corn.A second contribution of the thesis is the comparison of this model-driven method with classical data-driven methods. For this purpose, according to their different methodology in fitting the model complexity, we consider two classes of regression methods: first, Statistical methods derived from generalized linear regression that are good at simplifying the model by dimensional reduction, such as Ridge and Lasso Regression, Principal Components Regression or Partial Least Squares Regression; second, Machine Learning Regression based on re-sampling techniques like Random Forest, k-Nearest Neighbour, Artificial Neural Network and Support Vector Machine (SVM) regression.At last, a weighted regression is applied to large-scale yield prediction. Soft wheat production in France is taken as an example. Model-driven and data-driven approaches have also been compared for their performances in achieving this goal, which could be recognised as the third contribution of this thesis
Books on the topic "Crop yields – Methodology"
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Eurostat. Crop Yield Forecasting Methods: Proceedings of the Seminar (Theme 0--Miscellaneous. Series D, Studies and Research). Statistical Office of European Communities, 1997.
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Statens planteavlsforsøg (Denmark). Afdeling for arealanvendelse., ed. Yield and farm survey in two agricultural regions in Denmark, 1994. [Vejle]: Ministry of Agriculture and Fisheries, Danish Institute of Plant and Soil Science, Dept. of Land Use, 1995.
Find full textBook chapters on the topic "Crop yields – Methodology"
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Durner, Edward F. "Simple linear regression." In Applied plant science experimental design and statistical analysis using the SAS® OnDemand for Academics, 80–145. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245981.0009.
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Abstract This chapter covers the methods for obtaining and expressing these mathematical equations and their confidence bands. The methodology is linear regression analysis. Four types of regression analysis are presented, including: simple linear regression with no repeated measures or replication; simple linear regression with repeated measures; simple linear regression with replication; and polynomial regression. The effects of nitrogen rate on crop yield were presented as example.
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Choudhary, Mahendra, Rohit Sartandel, Anish Arun, and Leena ladge. "Crop Recommendation System and Plant Disease Classification using Machine Learning for Precision Agriculture." In Artificial Intelligence and Communication Technologies, 39–49. Soft Computing Research Society, 2022. http://dx.doi.org/10.52458/978-81-955020-5-9-4.
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The Agriculture sector is the backbone of our country. It provides a living for the vast majority of India’s inhabitants, but it only accounts for 15% of the country’s GDP. In comparison to other countries, our country’s crop yield is quite poor. This could be one of the reasons for India’s increased suicide rate among marginal farmers. Another cause for this is that farmers do not plan their crops properly. Another reason for this situation is that farmers frequently make incorrect crop selection decisions, such as planting in the wrong season or picking a crop that would not yield much for the particular soil. Incorrect crop selection will always result in a lower yield. It is difficult to survive if the family is entirely dependent on this revenue. In this paper, we offer a model that addresses these concerns. The suggested methodology allows for crop selection based on economic and environmental factors, intending to boost crop yields to satisfy the country’s growing food demand. The proposed model predicts the crop yield by studying factors such as rainfall, temperature, humidity, soil nutrients, ph value of the soil. The model assists farmers in maintaining soil nutrient levels. In addition to that, the app will enable farmers to identify diseases in their plants.
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Kulyk, Maksym, Dmytro Dʼomin, and Іlona Rozhkо. "RECLAMATION OF MARGINAL LANDS USING RARE ENERGY CROPS." In European vector of development of the modern scientific researches. Publishing House “Baltija Publishing”, 2021. http://dx.doi.org/10.30525/978-9934-26-077-3-27.
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The purpose of the paper is to determine the impact of the species of energy crops on biomass yields and the possibility of their involvement in the reclamation of contaminated areas. This is especially important from the point of view of the rational use of land for energy crops cultivation. Methodology. The research object is the processes of growth and development of plants, the peculiarities of the yield formation of energy crops biomass depending on the species traits and growing conditions. The research subject is the following energy crops: Big Bluestem, Indiangrass and Columbus Grass as well as the plant biometric indicators, biomass yield and energy efficiency of biomass production of energy crops (2016-2020). The results of research showed the variability of biometric parameters of energy crops. Over the research years, the dry biomass yield of Indiangrass was 8.9 t/ha in the first year, 10.1 t/ha in the second year and 14.9 t/ha in the third year, Big Bluestem – varied within 4.4–9.3 t/ha. Columbus Grass dry biomass increased from 11.4 t/ha (1st year) to 14.9 t/ha (2nd year) to 18.0 t/ha (3rd year). The developed model for the creation of artificial phytocenoses will allow land reclamation using energy crops based on agroecological monitoring and justification when growing energy crops. Perennial cultivation of Columbus Grass and Indiangrass provided the highest coefficient of energy efficiency (at a level or more than 3.0), which is typical for average efficiency of biomass production. Therefore, Indiangrass and Columbus Grass are recommended to be grown in order to reclaim marginal lands and obtain sustainable plant raw materials. Big Bluestem is recommended to be grown only as a companion crop of stand of grass. Furthermore, energy crops must be cultivated on the basis of ecological and adaptive technology elements, taking into account the defined territorial conditions. For the conditions of Ukraine, this complex will make it possible to reduce the negative impact on the environment as well as to obtain the stable yields of various biomass for its further processing and energy conversion.
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M. Hatture, Sanjeevakumar, Pallavi V. Yankati, Rashmi Saini, and Rashmi P. Karchi. "Organic Farming for Sustainable Agriculture Using Water and Soil Nutrients." In New Generation of Organic Fertilizers. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.100319.
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The agricultural community/farmers are struggling to obtain higher rate of yield due to lack of poor knowledge about the soil and water nutrients and suitability of the organic crop for the soil. Most of the farmers use excessive chemical fertilizers in-order to increase productivity of their yield, without aware of side effects. The excess usage of chemical fertilizers by the farmers will have impact on the quality, fertility, and salinity of the soil. To overcome these issues and to promote Digital Agriculture concept we propose an IoT enabled sensor system for monitoring soil nutrient [NPK] and pH of irrigation water to reduce the manual laboratory method of testing and get the results via mobile application and to promote organic farming in the agricultural field. Smart organic farming based mobile application will further process these nutrients value to predict and suggests the suitable crop to grow and the usage of appropriate amount of fertilizer to maintain the soil fertility there by achieving optimum usage of chemical fertilizer because continuous and wrong usage of these chemical fertilizer have a harmful effect not only on soil but also on crops, we consume leading to unhealthy human life. The proposed mobile application also helps in establishing the connection between farmers and Agricultural Produce Market Committee (APMC) in order to avoid fragmentation of profit shares and attain Pricing uncertainty and marketing of the yields by avoiding the middle man. APMC is a state government body which ensures safeguard to the farmers from exploitation by large retailers and suggest the kind of crop to be grown with organic farming. India is well known to produce organic fertilizer which is produced by the waste of slaughterhouses, plant and animal residues, biological products and other natural resources. Thus, the proposed work helps the farmers in adopting stress-free organic farming practice by self-testing their field soil parameters for generating quick soil analysis reports and also helps in connecting with APMC to know the suitable crop for their agriculture land based on the soil and water analysis (SWA) report, dispensing the required amount of organic fertilizer to the soil based on soil and water nutrients analysis using IoT enabled sensor, funding/insurance to the crops in case of occurrence of unpredictable natural disaster in future and direct marketing facility without middle man and maintain sustainable agriculture. In the present era, the industry is at 5.0 levels but agricultural production is still at 2.0 levels. In this chapter a methodology for sustainable agriculture and increase the organic yield of the organic farming using the mobile and IoT technological approaches is presented. A former can obtain the advice and other information for growing the organic crop, organic certification, pricing for the organic yield, selling and other activities by using mobile application in his/her local language. By the proposed work with the ease of mobile application the farmers can perform self-test of their field parameters for generating quick soil and water analysis report, predicts and suggest the suitable organic crop, obtaining the suitable pricing by the APMC and organic certification and agreement to meet the sustainable agriculture. Further the soil fertility of the organic farm can be monitored using IoT enabled sensors which are remotely connected with the mobile application. The experimentation is performed at different agriculture fields with organic farming at six geographical separated villages at Bagalkot district of Karnataka state, India. The different agricultural lands with variety of soil samples is tested to measure the soil parameter such as moisture, temperature, humidity and NPK nutrient values. The pH value of the irrigation water is also determined including borewell, pond, rain, river water etc. available in the reservoirs and promising sustainability in the organic yield is obtained.
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Das, Sripriya, Manoj Kumar Singh, Sneha Kumari, and Manimala Mahato. "Recent Advances in Crop Establishment Methods in Rice-Wheat Cropping System-a Review." In Cereal Grains [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.98743.
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Traditional practices of growing rice and wheat in Asian countries involve a huge cost in establishment methods adopted by farmers which not only limit the yield and return but also degrade soil and require more water. Adaptation of improved crop establishment methods suitable under adverse climatic conditions is of utmost importance for scientific utilization of natural resources and to maintain the sustainability of rice- wheat cropping system Therefore, an attempt has been made in this chapter to review precision rice establishment methodology viz., direct seeding, non-puddle/unpuddled transplanting, bed transplanting, strip tilled and single pass shallow tilled rice, double transplanting and system of rice intensification (SRI) and wheat establishment methods viz., zero tilled, strip tilled and bed planted wheat. These are recent improved crop establishment techniques that can be used under specific agro-ecological conditions for enhancing yield and resource conservation in Indo-gangetic plains of Eastern India.
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Kulkarni, Arun, and Sara McCaslin. "Fuzzy Neural Network Models for Knowledge Discovery." In Intelligent Data Analysis, 103–19. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-982-3.ch006.
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This chapter introduces fuzzy neural network models as means for knowledge discovery from databases. It describes architectures and learning algorithms for fuzzy neural networks. In addition, it introduces an algorithm for extracting and optimizing classification rules from a trained fuzzy neural network. As an illustration, multispectral satellite images have been analyzed using fuzzy neural network models. The authors hope that fuzzy neural network models and the methodology for generating classification rules from data samples provide a valuable tool for knowledge discovery. The algorithms are useful in a variety of data mining applications such as environment change detection, military reconnaissance, crop yield prediction, financial crimes and money laundering, and insurance fraud detection.
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Civan, Peter, Renaud Rincent, Alice Danguy-Des-Deserts, Jean-Michel Elsen, and Sophie Bouchet. "Population Genomics Along With Quantitative Genetics Provides a More Efficient Valorization of Crop Plant Genetic Diversity in Breeding and Pre-breeding Programs." In Population Genomics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/13836_2021_97.
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AbstractThe breeding efforts of the twentieth century contributed to large increases in yield but selection may have increased vulnerability to environmental perturbations. In that context, there is a growing demand for methodology to re-introduce useful variation into cultivated germplasm. Such efforts can focus on the introduction of specific traits monitored through diagnostic molecular markers identified by QTL/association mapping or selection signature screening. A combined approach is to increase the global diversity of a crop without targeting any particular trait.A considerable portion of the genetic diversity is conserved in genebanks. However, benefits of genetic resources (GRs) in terms of favorable alleles have to be weighed against unfavorable traits being introduced along. In order to facilitate utilization of GR, core collections are being identified and progressively characterized at the phenotypic and genomic levels. High-throughput genotyping and sequencing technologies allow to build prediction models that can estimate the genetic value of an entire genotyped collection. In a pre-breeding program, predictions can accelerate recurrent selection using rapid cycles in greenhouses by skipping some phenotyping steps. In a breeding program, reduced phenotyping characterization allows to increase the number of tested parents and crosses (and global genetic variance) for a fixed budget. Finally, the whole cross design can be optimized using progeny variance predictions to maximize short-term genetic gain or long-term genetic gain by constraining a minimum level of diversity in the germplasm. There is also a potential to further increase the accuracy of genomic predictions by taking into account genotype by environment interactions, integrating additional layers of omics and environmental information.Here, we aim to review some relevant concepts in population genomics together with recent advances in quantitative genetics in order to discuss how the combination of both disciplines can facilitate the use of genetic diversity in plant (pre) breeding programs.
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Khyzhnyak, Svitlana, and Volodymyr Voitsitskiy. "BIOTESTING AS A METHOD FOR ASSESSING THE STIMULATING EFFECT OF HUMIC COMPOUNDS ON HIGHER PLANTSBIOTESTING AS A METHOD FOR ASSESSING THE STIMULATING EFFECT OF HUMIC COMPOUNDS ON HIGHER PLANTS." In Science, technology, and innovation: the experience of European countries and prospects for Ukraine. Publishing House “Baltija Publishing”, 2021. http://dx.doi.org/10.30525/978-9934-26-190-9-3.
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When improving agricultural technologies, considerable attention should be paid to the use of organic fertilizers, which involves the use of humic and fulvic acids. This will reduce the use of mineral fertilizers and increase the yield of agricultural products, as well as grow environmentally friendly products. Justification of the use of organic fertilizers to stimulate plant growth requires a preliminary study of their action under laboratory conditions. The article analyzes the wide application of biotesting procedures based on the reactions of living organisms, using plant test-objects. The availability of a wide range of plants allows their use for testing various factors, including the analysis of the stimulating effect of substances on higher plants. The purpose of the study was to study the effect of the organic fertilizer «Grееnat» on the initial growth processes of higher plants in the laboratory by biotesting. The methodology of the study was to apply the biotesting method using higher plants widely used in agriculture, namely barley (Hordeum vulgare L.), soybeans (Glycine max L.), wheat (Triticum aestivum L.), maize (Zea mays L.), cucumber (Cucumis sativus L.), tomato (Solanum lycopersicum L.). Іt is established that the declared organic fertilizer «Greenat» contains: humic acids (67,68 g/dm3), fulvic acids (24,37 g/dm3) and organic substances (53,39%). The stimulating effect of the studied fertilizer on the initial processes of growth and formation of the root system of representatives of the group of cereals – barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.) was revealed. Stimulating effect of organic fertilizer is also established for the initial growth processes of soybeans by estimating the length of shoots (increase by 38%) and for the initial growth processes of corn by estimating root length (increase by 22%). Root length of cucumber and tomato increased by 23 and 21% respectively, indicating the stimulating effect of the organic fertilizer «Greenаt» in the treatment of seeds of vegetable crops. The results of the research indicate the effectiveness of using the organic fertilizer «Grееnat» at the stage of seed treatment to stimulate the energy of germination and development of the root system of plants.
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Gutsalenko, Liubov, and Tetiana Mulyk. "ANALYTICAL PROVISION OF LAND RESOURCES MANAGEMENT OF THE ENTERPRISE: STATE AND IMPROVEMENT." In Theoretical and practical aspects of the development of modern scientific research. Publishing House “Baltija Publishing”, 2022. http://dx.doi.org/10.30525/978-9934-26-195-4-4.
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The purpose of the paper are issues concerning to the analytical provision of land resources. The methodological basis of the study are general scientific and special methods of phenomena cognition and processes in the system of analytical support of land management. The study of the current state of land relations, classification of land resources is based on the use of methods of theoretical generalization, grouping and analogy. Methods of comparison, analysis and synthesis were used to prepare proposals for improving the analytical support of land management. Results. The composition and structure of Ukraine’s land resources, their place in Europe, property relations and land use were evaluated in the course of research. It is determined that the land area of Ukraine as of 01.01.2020 according to the State Service of Ukraine for Geodesy, Cartography and Cadastre is 60.3 million hectares. The system of views on land resources as an object of analysis, tasks and purpose of analysis, sequence of analytical operations and procedures aimed at preparing for analysis, analytical data processing and generalization of analytical information are identified and formulated. The main sources of information for the analysis of land resources of agricultural enterprises are primary documents, synthetic and analytical data. Their combination in the process of analytical procedures will increase the value of information and management decisions made on its basis. The system of indicators of land resources analysis is considered. Particular attention is paid to indicators to assess the level of intensity of use and indicators that characterize the efficiency of land use. Analyzing the set of indicators that characterize the state of land resources of agricultural enterprises, it is emphasized the lack of a single methodology and uniform forms of documents to ensure a complete and comprehensive study of land resources. To eliminate this problem, a holistic methodological apparatus is proposed, in which the stages of analytical work are coordinated (obtaining, processing and analyzing information), sources and channels of information, as well as tools, methods, techniques of analysis that allow to obtain appropriate results. An important aspect that contributes to the effectiveness of such work is the choice of rational approaches and methods in accordance with the objects and needs of the analysis. SWOT-analysis of agricultural land use development is presented. Based on the SWOT analysis, it can be seen that development strategies in land use should be aimed at highly efficient use of land resources, to reproduce their fertility. Due to this, high yields of crops will be obtained with minimal costs per unit of output and preservation of productive properties of land. It is stated that it is necessary to anticipate ecological and economic responsibility of landowners and land users for deterioration of soil quality parameters, combination of public and private interests for sustainable land production, use of land resources taking into account community interests, prevision of measures to restore land productivity. Practical implication is the scientific validity and applied orientation of the provisions, approaches and recommendations given in this study, the use of which will improve the organizational and methodological foundations of the analysis of land resources. Value/originality. The use of SWOT analysis in the practice of land management entities, on the one hand, will help eliminate the weaknesses of land use while strengthening its strengths, on the other hand, will fully provide opportunities to take advantage of opportunities that may arise due to action of external factors avoiding threats. SWOT analysis has a significant impact on strategic management decisions aimed at the formation of rational agricultural land use.
Conference papers on the topic "Crop yields – Methodology"
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James M McKinion and Jeffrey L Willers. "Development of a Crop Yield Stability Methodology for a Field." In 2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2010. http://dx.doi.org/10.13031/2013.30024.
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Soria-Ruiz, Jesus, and Yolanda M. Fernandez-Ordonez. "Methodology to generate yield maps of maize crops." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5651696.
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Zhu, Jinxia, Ke Wang, Jinsong Deng, and Tom Harmon. "Quantifying Nitrogen Status of Rice Using Low Altitude UAV-Mounted System and Object-Oriented Segmentation Methodology." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-87107.
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Nitrogen deficiency can seriously reduce yield, while over-fertilization can result problems such as excess nutrient runoff and groundwater pollution. Hence, efficient methods for assessing crop nitrogen status are needed to enable more optimal fertilizer management. The ability to quantify the different nitrogen application rates by crops using digital images taken from an unmanned aerial vehicle (UAV) was investigated in comparison with ground-based hyperspectral reflectance and chlorophyll content data from 140 plots on a managed field. This research utilized new UAV system, comprised of remote-controlled helicopter (Hercules II) and digital camera (EOS 30D), was used to characterize spatial and temporal variation in crop production. Digital information was extracted based on an object-oriented segmentation method, and the color parameter was reduced and represented using principal component analysis (PCA). An estimating model was established after analyzing the relationship between the optimal color parameter and ground-based measurements. Model testing demonstrated that unknown samples could be associated with the controlled nitrogen application rates (0, 60, 90, and 120 kg N·hm−2): 91.6% %; N1 (60 kg N·hm−2): 70.83%; N2 (90 kg N·hm−2): 86.7%; N3 (120 kg N·hm−2): 95%. Overall, this result proved to provide a cost-effective and accurate way and the UAV was an exploratory and predictive tool when applied to quantify different status of nitrogen. In addition, it indicated that the application of digital image from UAV to the problem of estimating different nitrogen rates is promising.
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Santos, Thiago T., and Luciano Gebler. "A methodology for detection and localization of fruits in apples orchards from aerial images." In Congresso Brasileiro de Agroinformática. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/sbiagro.2021.18369.
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Computer vision methods based on convolutional neural networks (CNNs) have presented promising results on image-based fruit detection at ground-level for different crops. However, the integration of the detections found in different images, allowing accurate fruit counting and yield prediction, have received less attention. This work presents a methodology for automated fruit counting employing aerial-images. It includes algorithms based on multiple view geometry to perform fruits tracking, not just avoiding double counting but also locating the fruits in the 3-D space. Preliminary assessments show correlations above 0.8 between fruit counting and true yield for apples. The annotated dataset employed on CNN training is publicly available.
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Bushueva, Vera Ivanovna, Marina AVRAMENKO, Victoria Volyntseva, and Viktoriya BARDOVSKAYa. "Results of Galega orientalis breeding in the Republic of Belarus." In Multifunctional adaptive fodder production 29 (77). ru: Federal Williams Research Center of Forage Production and Agroecology, 2022. http://dx.doi.org/10.33814/mak-2022-29-77-95-104.
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The article describes the results of selection of Galega orientalis in the Republic of Belarus. A brief history, directions, methods and results of breeding work in Belarusian State Agricultural Academy are described. The methodology of Nesterka variety creation is presented, the theoretical basis for intensification and acceleration of the breeding process of a new variety samples of Galega orientalis of various species and their use in the selection of patentable varieties are highlighted. The methodology of creating the varieties of EGG-2 and BGSHA-2 and the results of their evaluation in the competitive and state variety trials in comparison with the control varieties are demonstrated. Analysis of the results of the state variety testing showed that the yield of dry matter of the variety BGSHA-2 significantly differed from year to year and was different at various testing stations. The highest yield was obtained at the "Lepelskaya SS", where Galega orientalis has demonstrated typical of this crop increase of herbage yield in each subsequent year. Thus, in 2017 the dry matter yield was 59.8 c/ha, in 2018 — 106.0, and in 2019 it was the maximum for the variety in the years of testing — 153.0 c/ha. It is noted that the methods of chemical mutagenesis (phosphemide) and polyploidy (colchicine) are used to create new varieties in the academy, and the created new source material is being evaluated in mutant and polyploid nurseries. Studies on the effect of irrigation on the yield of Galega orientalis have also been carried out at the Belarusian State Agricultural Academy.
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Kontokostas, Georgios, Ioannis Goulos, and Anastassios Stamatis. "Techno–Economic Evaluation of Recuperated Gas Turbine Cogeneration Cycles Utilizing Animal Manure and Energy Crops for Biogas Fuel." In ASME Turbo Expo 2014: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/gt2014-25308.
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This work presents the development of an integrated approach, targeting the techno-economic assessment of recuperated cogeneration gas turbine cycles, utilizing anaerobic digestion products of animal manure and energy crops for biogas fuel. The overall approach consists of a series of fundamental modeling theories applicable to; anaerobic digestion and biogas fuel yield, thermodynamic analysis of cogeneration gas turbine cycles, exergetic analysis of anaerobic digestion, and economic modeling of implementation and operation. The developed methodology is applied to the techno-economic analysis of a representative anaerobic digestion plant yielding biogas fuel which is supplied to a recuperated cogeneration gas turbine powerplant. The influence of employed thermodynamic cycle parameters along with the incorporated technology level, on the cycle performance parameters and economic sustainability of integrated digestion–cogeneration powerplant designs, is thoroughly investigated. The obtained results suggest that, the dominant thermodynamic cycle variables that affect the electrical performance of integrated digestion-cogeneration systems, are the gas/air temperatures at the combustor outlet and recuperator air side exit, respectively. It is shown that the profitability of the investment is highly depended on the electrical power output and the feed–in tariff for electrical energy. Optimization of the employed co-generation cycle for maximum electrical power output, is shown to be a crucial element in terms of securing investment sustainability. A general review of the results indicates that, anaerobic treatment of animal manure and energy crops may constitute a sustainable investment, primarily for cases that substantial volumes of substrates are available in order to secure biogas yield and stable operation of the AD–gas turbine power plant.
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VIESTURS, Dainis, Nikolajs KOPIKS, and Adolfs RUCINS. "RESEARCH ON THE DEVELOPMENT OF THE TRACTOR AND COMBINE FLEET IN LATVIA." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.183.
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The article offers an analysis of the development of the tractor and combine harvester fleet in 2001 - 2016. There are stated tractors and combines of the most common brands registered in the country. A methodology has been developed for the estimation of an adequate quantity of tractors and combines for timely cultivation of the sowing areas under agricultural crops. The methodology is based on the calculation of the annual increase in the summary engine capacity of the entire fleet of tractors and combine harvesters, and its comparison with the annual increase in the sowing areas. It is assumed that 10% of the sowing areas are cultivated by worn-out tractors, and 7.5% of the new tractor and combine capacity is required for the replacement (amortisation) of the worn-out tractors. We consider that the increase in the summary capacity should compensate for the increase in the sowing areas, taking into account also the impact of the total yield upon the productivity of the machinery. It has been found out that during the period the summary engine capacity of the tractors has grown 1.52 times, the summary engine capacity of the combine harvesters – 2.8 times; in the same period the area under agricultural crops has increased approximately 1.38 times but the area under cereals and canola – approximately 1.8 times. Several authors consider specific capacity kWh-1 as a criterion for the estimation of an adequate quantity of the machinery; therefore there are calculated also its changes in a 16-year period, with the specific capacity of both the tractors and combine harvesters increasing. The average specific power kWh-1 is considered as relatively high, but, due to the concentration of production, a decrease in this indicator is possible in the future. The average weighted capacity of the purchased new tractors and combine harvesters has also increased. The increase in the summary capacity of the tractor fleet compensates for the increase in the sowing areas and even exceeds it a little. In its turn, the increase in the summary capacity of the combine fleet allows timely harvesting the significantly increased sowing area, and more than twice the increased totals yield at the end of the period, and, compared to the beginning, slightly shorten the harvesting duration. The calculations do not include weather conditions during the harvesting period. As increase in the sowing areas and total yield is expected still further, upgrading of the tractor fleet should be continued at approximately the same rate – by purchasing, on the average, 600 to 700 new tractors and 80 to 100 new combine harvesters every year.
Reports on the topic "Crop yields – Methodology"
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Temple, Dorota S., Jason S. Polly, Meghan Hegarty-Craver, James I. Rineer, Daniel Lapidus, Kemen Austin, Katherine P. Woodward, and Robert H. Beach III. The View From Above: Satellites Inform Decision-Making for Food Security. RTI Press, August 2019. http://dx.doi.org/10.3768/rtipress.2019.rb.0021.1908.
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Despite notable progress in reducing global poverty and hunger in recent decades, about one out of nine people in the world suffers from hunger and malnutrition. Stakeholders charged with making decisions pertaining to agricultural production, development priorities, and policies at a region-to-country scale require quantitative and up-to-date information on the types of crops being cultivated, the acreage under cultivation, and crop yields. However, many low- and middle-income countries lack the infrastructure and resources for frequent and extensive agricultural field surveys to obtain this information. Technology supports a change of paradigm. Traditional methods of obtaining agricultural information through field surveys are increasingly being augmented by images of the Earth acquired through sensors placed on satellites. The continued improvement in the resolution of satellite images, the establishment of open-access infrastructure for processing of the images, and the recent revolutionary progress in artificial intelligence make it feasible to obtain the information at low cost and in near-to-real time. In this brief, we discuss the use of satellite images to provide information about agricultural production in low-income countries, and we comment on research challenges and opportunities. We highlight the near-term potential of the methodology in the context of Rwanda, a country in sub-Saharan Africa whose government has recognized early the value of information technology in its strategic planning for food security and sustainability.
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Miller, Gad, and Jeffrey F. Harper. Pollen fertility and the role of ROS and Ca signaling in heat stress tolerance. United States Department of Agriculture, January 2013. http://dx.doi.org/10.32747/2013.7598150.bard.
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The long-term goal of this research is to understand how pollen cope with stress, and identify genes that can be manipulated in crop plants to improve reproductive success during heat stress. The specific aims were to: 1) Compare heat stress dependent changes in gene expression between wild type pollen, and mutants in which pollen are heat sensitive (cngc16) or heat tolerant (apx2-1). 2) Compare cngc16 and apx2 mutants for differences in heat-stress triggered changes in ROS, cNMP, and Ca²⁺ transients. 3) Expand a mutant screen for pollen with increased or decreased thermo-tolerance. These aims were designed to provide novel and fundamental advances to our understanding of stress tolerance in pollen reproductive development, and enable research aimed at improving crop plants to be more productive under conditions of heat stress. Background: Each year crop yields are severely impacted by a variety of stress conditions, including heat, cold, drought, hypoxia, and salt. Reproductive development in flowering plants is highly sensitive to hot or cold temperatures, with even a single hot day or cold night sometimes being fatal to reproductive success. In many plants, pollen tube development and fertilization is often the weakest link. Current speculation about global climate change is that most agricultural regions will experience more extreme environmental fluctuations. With the human food supply largely dependent on seeds, it is critical that we consider ways to improve stress tolerance during fertilization. The heat stress response (HSR) has been intensively studied in vegetative tissues, but is poorly understood during reproductive development. A general paradigm is that HS is accompanied by increased production of reactive oxygen species (ROS) and induction of ROS-scavenging enzymes to protect cells from excess oxidative damage. The activation of the HSR has been linked to cytosolic Ca²⁺ signals, and transcriptional and translational responses, including the increased expression of heat shock proteins (HSPs) and antioxidative pathways. The focus of the proposed research was on two mutations, which have been discovered in a collaboration between the Harper and Miller labs, that either increase or decrease reproductive stress tolerance in a model plant, Arabidopsis thaliana (i.e., cngc16--cyclic nucleotide gated channel 16, apx2-1--ascorbate peroxidase 2,). Major conclusions, solutions, achievements. Using RNA-seq technology, the expression profiles of cngc16 and apx2 pollen grains were independently compared to wild type under favourable conditions and following HS. In comparison to a wild type HSR, there were 2,776 differences in the transcriptome response in cngc16 pollen, consistent with a model in which this heat-sensitive mutant fails to enact or maintain a normal wild-type HSR. In a comparison with apx2 pollen, there were 900 differences in the HSR. Some portion of these 900 differences might contribute to an improved HSR in apx2 pollen. Twenty-seven and 42 transcription factor changes, in cngc16 and apx2-1, respectively, were identified that could provide unique contributions to a pollen HSR. While we found that the functional HS-dependent reprogramming of the pollen transcriptome requires specific activity of CNGC16, we identified in apx2 specific activation of flavonol-biosynthesis pathway and auxin signalling that support a role in pollen thermotolerance. Results from this study have identified metabolic pathways and candidate genes of potential use in improving HS tolerance in pollen. Additionally, we developed new FACS-based methodology that can quantify the stress response for individual pollen in a high-throughput fashion. This technology is being adapted for biological screening of crop plant’s pollen to identify novel thermotolerance traits. Implications, both scientific and agricultural. This study has provided a reference data on the pollen HSR from a model plant, and supports a model that the HSR in pollen has many differences compared to vegetative cells. This provides an important foundation for understanding and improving the pollen HSR, and therefor contributes to the long-term goal of improving productivity in crop plants subjected to temperature stress conditions. A specific hypothesis that has emerged from this study is that pollen thermotolerance can be improved by increasing flavonol accumulation before or during a stress response. Efforts to test this hypothesis have been initiated, and if successful have the potential for application with major seed crops such as maize and rice.
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Aparicio, Gabriela, Vida Bobić, Fernando De Olloqui, María Carmen Fernández Diez, María Paula Gerardino, Oscar A. Mitnik, and Sebastian Vargas Macedo. Liquidity or Capital?: The Impacts of Easing Credit Constraints in Rural Mexico. Inter-American Development Bank, June 2021. http://dx.doi.org/10.18235/0003336.
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This paper evaluates the effectiveness of easing credit constraints for rural producers in Mexico through loans provided by a national public development finance institution. In contrast to most of the existing literature, the study focuses on the effect of medium-sized loans over a two- to four-year time horizon. This paper looks at the effects of such loans on production and investment decisions, input use, and yields. Using a multiple treatment methodology, it explores the differential impacts of providing liquidity for working capital versus providing credit for investments in fixed assets. It finds that loans increased the likelihood that producers grow and sell certain key annual crops, in particular among recipients of working capital loans. It also finds significant effects on production value and sales (per hectare), with similar impacts for recipients of both types of loans, with gains in yields driven by changes in labor quality and more intensive use of key inputs. There is no evidence of significant effects on the purchase of large machinery, but there are impacts on the acquisition of cattle. Overall, the results reported in this paper suggest that lack of liquidity is at least as important as lack of funding for new investment in capital for rural producers in Mexico. Producers benefit from easing their credit constraints, regardless of the type of loan used for that purpose.
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Agassi, Menahem, Michael J. Singer, Eyal Ben-Dor, Naftaly Goldshleger, Donald Rundquist, Dan Blumberg, and Yoram Benyamini. Developing Remote Sensing Based-Techniques for the Evaluation of Soil Infiltration Rate and Surface Roughness. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7586479.bard.
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The objective of this one-year project was to show whether a significant correlation can be established between the decreasing infiltration rate of the soil, during simulated rainstorm, and a following increase in the reflectance of the crusting soil. The project was supposed to be conducted under laboratory conditions, using at least three types of soils from each country. The general goal of this work was to develop a method for measuring the soil infiltration rate in-situ, solely from the reflectance readings, using a spectrometer. Loss of rain and irrigation water from cultivated fields is a matter of great concern, especially in arid, semi-arid regions, e.g. much of Israel and vast area in US, where water is a limiting factor for crop production. A major reason for runoff of rain and overhead irrigation water is the structural crust that is generated over a bare soils surface during rainfall or overhead irrigation events and reduces its infiltration rate (IR), considerably. IR data is essential for predicting the amount of percolating rainwater and runoff. Available information on in situ infiltration rate and crust strength is necessary for the farmers to consider: when it is necessary to cultivate for breaking the soil crust, crust strength and seedlings emergence, precision farming, etc. To date, soil IR is measured in the laboratory and in small-scale field plots, using rainfall simulators. This method is tedious and consumes considerable resources. Therefore, an available, non-destructive-in situ methods for soil IR and soil crusting levels evaluations, are essential for the verification of infiltration and runoff models and the evaluation of the amount of available water in the soil. In this research, soil samples from the US and Israel were subjected to simulated rainstorms of increasing levels of cumulative energies, during which IR (crusting levels) were measured. The soils from the US were studied simultaneously in the US and in Israel in order to compare the effect of the methodology on the results. The soil surface reflectance was remotely measured, using laboratory and portable spectrometers in the VIS-NIR and SWIR spectral region (0.4-2.5mm). A correlation coefficient spectra in which the wavelength, consisting of the higher correlation, was selected to hold the highest linear correlation between the spectroscopy and the infiltration rate. There does not appear to be a single wavelength that will be best for all soils. The results with the six soils in both countries indeed showed that there is a significant correlation between the infiltration rate of crusted soils and their reflectance values. Regarding the wavelength with the highest correlation for each soil, it is likely that either a combined analysis with more then one wavelength or several "best" wavelengths will be found that will provide useful data on soil surface condition and infiltration rate. The product of this work will serve as a model for predicting infiltration rate and crusting levels solely from the reflectance readings. Developing the aforementioned methodologies will allow increased utilization of rain and irrigation water, reduced runoff, floods and soil erosion hazards, reduced seedlings emergence problems and increased plants stand and yields.
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Seginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.
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Background Early detection and identification of faulty greenhouse operation is essential, if losses are to be minimized by taking immediate corrective actions. Automatic detection and identification would also free the greenhouse manager to tend to his other business. Original objectives The general objective was to develop a method, or methods, for the detection, identification and accommodation of faults in the greenhouse. More specific objectives were as follows: 1. Develop accurate systems models, which will enable the detection of small deviations from normal behavior (of sensors, control, structure and crop). 2. Using these models, develop algorithms for an early detection of deviations from the normal. 3. Develop identifying procedures for the most important faults. 4. Develop accommodation procedures while awaiting a repair. The Technion team focused on the shoot environment and the Cornell University team focused on the root environment. Achievements Models: Accurate models were developed for both shoot and root environment in the greenhouse, utilizing neural networks, sometimes combined with robust physical models (hybrid models). Suitable adaptation methods were also successfully developed. The accuracy was sufficient to allow detection of frequently occurring sensor and equipment faults from common measurements. A large data base, covering a wide range of weather conditions, is required for best results. This data base can be created from in-situ routine measurements. Detection and isolation: A robust detection and isolation (formerly referred to as 'identification') method has been developed, which is capable of separating the effect of faults from model inaccuracies and disturbance effects. Sensor and equipment faults: Good detection capabilities have been demonstrated for sensor and equipment failures in both the shoot and root environment. Water stress detection: An excitation method of the shoot environment has been developed, which successfully detected water stress, as soon as the transpiration rate dropped from its normal level. Due to unavailability of suitable monitoring equipment for the root environment, crop faults could not be detected from measurements in the root zone. Dust: The effect of screen clogging by dust has been quantified. Implications Sensor and equipment fault detection and isolation is at a stage where it could be introduced into well equipped and maintained commercial greenhouses on a trial basis. Detection of crop problems requires further work. Dr. Peleg was primarily responsible for developing and implementing the innovative data analysis tools. The cooperation was particularly enhanced by Dr. Peleg's three summer sabbaticals at the ARS, Northem Plains Agricultural Research Laboratory, in Sidney, Montana. Switching from multi-band to hyperspectral remote sensing technology during the last 2 years of the project was advantageous by expanding the scope of detected plant growth attributes e.g. Yield, Leaf Nitrate, Biomass and Sugar Content of sugar beets. However, it disrupted the continuity of the project which was originally planned on a 2 year crop rotation cycle of sugar beets and multiple crops (com and wheat), as commonly planted in eastern Montana. Consequently, at the end of the second year we submitted a continuation BARD proposal which was turned down for funding. This severely hampered our ability to validate our findings as originally planned in a 4-year crop rotation cycle. Thankfully, BARD consented to our request for a one year extension of the project without additional funding. This enabled us to develop most of the methodology for implementing and running the hyperspectral remote sensing system and develop the new analytical tools for solving the non-repeatability problem and analyzing the huge hyperspectral image cube datasets. However, without validation of these tools over a ful14-year crop rotation cycle this project shall remain essentially unfinished. Should the findings of this report prompt the BARD management to encourage us to resubmit our continuation research proposal, we shall be happy to do so.
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Mevarech, Moshe, Jeremy Bruenn, and Yigal Koltin. Virus Encoded Toxin of the Corn Smut Ustilago Maydis - Isolation of Receptors and Mapping Functional Domains. United States Department of Agriculture, September 1995. http://dx.doi.org/10.32747/1995.7613022.bard.
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Ustilago maydis is a fungal pathogen of maize. Some strains of U. maydis encode secreted polypeptide toxins capable of killing other susceptible strains of U. maydis. Resistance to the toxins is conferred by recessive nuclear genes. The toxins are encoded by genomic segments of resident double-strande RNA viruses. The best characterized toxin, KP6, is composed of two polypeptides, a and b, which are not covalently linked. It is encoded by P6M2 dsRNA, which has been cloned, sequenced and expressed in a variety of systems. In this study we have shown that the toxin acts on the membranes of sensitive cells and that both polypeptides are required for toxin activity. The toxin has been shown to function by creating new pores in the cell membrane and disrupting ion fluxes. The experiments performed on artificial phospholipid bilayers indicated that KP6 forms large voltage-independent, cation-selective channels. Experiments leading to the resolution of structure-function relationship of the toxin by in vitro analysis have been initiated. During the course of this research the collaboration also yielded X-ray diffracion data of the crystallized a polypeptide. The effect of the toxin on the pathogen has been shown to be receptor-mediated. A potential receptor protein, identified in membrane fractions of sensitive cells, was subjected to tryptic hydrolysis followed by amino-acid analysis. The peptides obtained were used to isolate a cDNA fragment by reverse PCR, which showed 30% sequence homology to the human HLA protein. Analysis of other toxins secreted by U. maydis, KP1 and KP4, have demonstrated that, unlike KP6, they are composed of a single polypeptide. Finally, KP6 has been expressed in transgenic tobacco plants, indicating that accurate processing by Kex2p-like activity occurs in plants as well. Using tobacco as a model system, we determined that active antifungal toxins can be synthesized and targeted to the outside of transgenic plant cells. If this methodology can be applied to other agronomically crop species, then U. maydis toxins may provide a novel means for biological control of pathogenic fungi.
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Naim, Michael, Gary R. Takeoka, Haim D. Rabinowitch, and Ron G. Buttery. Identification of Impact Aroma Compounds in Tomato: Implications to New Hybrids with Improved Acceptance through Sensory, Chemical, Breeding and Agrotechnical Techniques. United States Department of Agriculture, October 2002. http://dx.doi.org/10.32747/2002.7585204.bard.
Full textAbstract:
The tomato, a profitable vegetable crop in both the USA and Israel, has benefited significantly from intensive breeding efforts in both countries, and elsewhere (esp. Holland). : Modem hybrids are highly prolific and resistant to a variety of major pests. They produce attractive, firm fruit for both processing and fresh-marketing. In all cases, however, reduction in flavor and aroma have occurred concomitantly with the increase in yield. Sugars-acids ratio dominate fruit taste, whereas aroma volatiles (potent at minute ppb and ppt levels) contribute to the total characteristic tomato flavor. An increase in sugars (1-2%) contributes significantly to tomato fruit taste. However, because of energy reasons, an increase in fruit sugars is immediately compensated for by a decrease in yield. Our main objectives were to: (a) pinpoint and identify the major impact aroma components of fresh tomato; (b) study the genetic and environmental effects on fruit aroma; (c) determine precursors of appealing (flavors) and repelling (off-flavors) aroma compounds in tomato. Addition of saturated salts blocked all enzymatic activities prior to isolation of volatiles by dynamic and static headspace, using solvent assisted flavor evaporation (SAFE) and solid phase micro-extraction (SPME) from highly favored (FA-612 and FA-624) and less preferred (R 144 and R 175) tomato genotypes. Impact aroma components were determined by gas chromatography-olfactometry (GC-O), gas chromatography-mass spectrometry (GC- MS) and aroma extract dilution analysis (AEDA). The potent odorant (Z)-1,5-octadien-3-one, was identified for the first time in fresh tomato. From the ca. 400 volatile compounds in the headspace of fresh tomato, the following compounds are proposed to be impact aroma compounds: (Z)-3-hexenal, hexanal, 1-penten-3-one, 2-phenylethanol, (E)-2-hexenal, phenyl acetaldehyde, b-ionone, b-damascenone, 4-hydroxy-2,5-dimethyl-3-(2H)-furanone (FuraneolR), (Z)-l,5-octadien-3-one, methional, 1-octen-3-one, guaiacol, (E,E)- and (E,Z)- 2,4-decadienal and trans- and cis-4,5-EPOXY -(E)-2-decenal. This confirms the initial hypothesis that only a small number of volatiles actually contribute to the sensation of fruit aroma. Tomato matrix significantly affected the volatility of certain impact aroma components and thus led to the conclusion that direct analysis of molecules in the headspace . may best represent access of tomato volatiles to the olfactory receptors. Significant differences in certain odorants were found between preferred and less-preferred cultivars. Higher consumer preference was correlated with higher concentrations of the following odorants: l-penten-3-one, (Z)-3-hexenal, (E,E)- and (E,Z)-2,4-decadienal and especially Furaneol, whereas lower consumer preference was associated with higher concentrations of methional, 3-methylbutyric acid, phenylacetaldehyde, 2-phenylethanol, and 2-isobutylthiazole. Among environmental factors (salinity, N source, growth temperature), temperature had significant effects on the content of selected aroma compounds (e.g., 3-methylbutanal, 1- penten-3-one, hexanal, (Z)-3-hexenal, (E)-2-hexenal, 2-isobutylthiazole, 6-methyl-5-hepten- 2-one, 1-octen-3-one, methional, 2-phenylethanal, phenyl acetaldehyde, and eugenol) in fresh tomatoes. Salt stress (20 mM NaCl) increased the content of odorants such as (Z)-3-hexenal, 2-phenylethanol and 3-methylbutanal in the R-144 cultivar whereas salinity had minor effects on 1-pentene-3-one, 2-isobutylthiazole and b-ionone. This fundamental knowledge obtained by comprehensive investigation, using modem chemical, sensory and agrotechnical methodology will assist future attempts to genetically modify the concentrations of key odorants in fresh tomatoes, and thus keep the tomato production of Israel and the USA competitive on the world market.