Academic literature on the topic 'Crop yields – Statistical methods'
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Journal articles on the topic "Crop yields – Statistical methods"
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Zhao, Chuang, Bing Liu, Shilong Piao, Xuhui Wang, David B. Lobell, Yao Huang, Mengtian Huang, et al. "Temperature increase reduces global yields of major crops in four independent estimates." Proceedings of the National Academy of Sciences 114, no. 35 (August 15, 2017): 9326–31. http://dx.doi.org/10.1073/pnas.1701762114.
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Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.
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Bischokov, Ruslan M. "Analysis, modelling and forecasting of crop yields using artificial neural networks." RUDN Journal of Agronomy and Animal Industries 17, no. 2 (June 16, 2022): 146–57. http://dx.doi.org/10.22363/2312-797x-2022-17-2-146-157.
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The article gives information about the attempt made to select configurations, train and test artificial neural networks for predicting yields of grain crops considering of climate changes. Peculiarities of agricultural production require constant improvement of methods for analyzing crop yields, time series, and longterm climatic characteristics. Preliminary statistical evaluation of the considered time series made it possible to identify certain patterns. Time series were divided into four intervals: for building a network, its training, testing and control. During the construction of artificial neural networks, three models were used: MLP - multilayer perceptron, RBF - r adial basis functions and GRNN - g eneralized regression neural network. Based on the results of the construction, the best model was chosen. The sum of active air temperatures and the sum of precipitation for the growing season was used for artificial neural networks at the input, and the crop yield was used at the output. The use of sets of neural systems, generated automatically, contributed to the effective forecasting of crop yields based on the analysis of climate data. As a result, according to the selected model, a yield forecast was made for the coming years considering climatic characteristics.
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Afshar, Mehdi H., Timothy Foster, Thomas P. Higginbottom, Ben Parkes, Koen Hufkens, Sanjay Mansabdar, Francisco Ceballos, and Berber Kramer. "Improving the Performance of Index Insurance Using Crop Models and Phenological Monitoring." Remote Sensing 13, no. 5 (March 2, 2021): 924. http://dx.doi.org/10.3390/rs13050924.
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Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.
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Storchak, Irina Gennadyevna, and Fedor Vladimirovich Eroshenko. "Use of remote methods for monitoring formation of yield of spring barley." Agrarian Scientific Journal, no. 11 (November 23, 2020): 58–61. http://dx.doi.org/10.28983/asj.y2020i11pp58-61.
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When cultivating barley, there is a need to monitor the condition of crops and forecast yields using objective and inexpensive methods. Remote sensing data of the Earth is used to solve various problems in the agricultural sector related to monitoring vegetation, including monitoring the condition of agricultural crops throughout the growing season. The main advantages of this observation are: efficiency, objectivity, multi-scale and cost-effective. The question of the possibility of predicting crop yields in the scientific literature has not yet been adequately reflected. Therefore, the purpose of the research was to identify the relationship between the data of remote sensing of the Earth and the yield of spring barley for the conditions of the Stavropol Territory. The studies used data from the VEGA IKI RAS service (averaged NDVI values of spring crops in the Stavropol Territory) and the statistical office of the Stavropol Territory. In the analysis of materials, NDVI values were tied to the stages of organogenesis. It was found that the closest correlation between (0.64) NDVI and spring barley yield corresponds to the phase of the formation of the caryopsis. When analyzing yield data and values of the NDVI vegetation index on fixed calendar dates (weeks) of the year, it was shown that a statistically significant correlation appears between the 13th and 26th calendar weeks of the year. Therefore, the Stavropol Territory is characterized by the dependence of barley productivity on NDVI values of spring crops. The closest it is observed in the phase of the formation of the seed. Thus, for the conditions of the Stavropol Territory, it is possible to predict the yield of spring barley according to remote sensing data of the Earth.
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POSHYVALOVA, Olena. "Statistical model for evaluation of the impact of climatic conditions on the crops production: the regional aspects." Economics. Finances. Law, no. 10 (October 29, 2021): 23–28. http://dx.doi.org/10.37634/efp.2021.10.5.
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The work examines the statistical model for evaluation of the impact of climatic conditions on the crops production in Ukraine. The conducted content analysis of academic literary sources enables to arrive at conclusion that the majority of Ukrainian scholars consider changes in climatic zones of Ukraine a positive trend for crops production. It must be emphasized, nonetheless, that the increase in natural heat provision for crops production against the backdrop of a significant reduction in average annual precipitation considerably diminishes the sizes of cultivated and harvested areas, gross yield and overall crop yield of basic crops and perennial plantings. To perform calculations on key statistical indicators of crops production the following tools have been employed: methods of analysis of absolute, relative and average values; methods of elaboration and study of groupings; methods of analysis of the structure of statistical populations; methods of cross-impact analysis of indicators; methods of trend studies. The analysis concerned the dynamics of change in statistical indicators of crops production in Kherson oblast over the period of 1990–2019: gross yield of cereal and leguminous crops; total harvesting area of cereal and leguminous crops; wheat yields; cereal and leguminous crops production per capita. Periods of diverse degrees of occurrence of atmospheric precipitation in Kherson oblast according to the level of liquid saturation have been grouped: dry, medium, humid. It has been proved that winter wheat yields are affected by the following factors: size of the cultivation area and average annual precipitation. It is established that the digitalization of the agriculture contributes to the decrease in pressure on land and water resources, provision of conditions for “clean”, sustainable and eco-friendly agricultural products, increase in gross yield of crops, provision of conditions for efficient use of resources, capability of Big Data processing. Prospects for further research lie in elaboration of a multi-factor non-linear modeling of winter wheat yield with account for the factors of humus and soil pH; average annual atmospheric temperature, etc.
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Svotwa, Ezekia, Anxious J. Masuka, Barbara Maasdorp, Amon Murwira, and Munyaradzi Shamudzarira. "Remote Sensing Applications in Tobacco Yield Estimation and the Recommended Research in Zimbabwe." ISRN Agronomy 2013 (December 15, 2013): 1–7. http://dx.doi.org/10.1155/2013/941873.
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Tobacco crop area and yield forecasts are important in stabilizing tobacco prices at the auction floors. Tobacco yield estimation in Zimbabwe is currently based on statistical surveys and ground-based field reports. These methods are costly, time consuming, and are prone to large errors. Remote sensing can provide timely information on crop spectral characteristics which can be used to estimate crop yields. Remote sensing application on agriculture in Zimbabwe is still very limited. Research should focus on identifying suitable reflectance indices that are related to tobacco growth and yield. Varietal yield response to fertiliser and planting dates as well as suitable temporal windows for spectral data collection should be identified. The challenges of the different tobacco land sizes have to be overcome by identifying suitable satellite platform, with sufficient spectral resolution to separate the tobacco crop from the adjacent competing crops and noncrop vegetative surfaces. The identified suitable index should be strongly correlated with tobacco in season dry mass and yield. The suitable vegetative indices can be employed in establishing tobacco cropped area and then apply the long-term area yield relationship from government and nongovernmental statistical departments to estimate yield from remote sensing derived cropped area.
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Gong, Liyun, Miao Yu, Shouyong Jiang, Vassilis Cutsuridis, and Simon Pearson. "Deep Learning Based Prediction on Greenhouse Crop Yield Combined TCN and RNN." Sensors 21, no. 13 (July 1, 2021): 4537. http://dx.doi.org/10.3390/s21134537.
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Currently, greenhouses are widely applied for plant growth, and environmental parameters can also be controlled in the modern greenhouse to guarantee the maximum crop yield. In order to optimally control greenhouses’ environmental parameters, one indispensable requirement is to accurately predict crop yields based on given environmental parameter settings. In addition, crop yield forecasting in greenhouses plays an important role in greenhouse farming planning and management, which allows cultivators and farmers to utilize the yield prediction results to make knowledgeable management and financial decisions. It is thus important to accurately predict the crop yield in a greenhouse considering the benefits that can be brought by accurate greenhouse crop yield prediction. In this work, we have developed a new greenhouse crop yield prediction technique, by combining two state-of-the-arts networks for temporal sequence processing—temporal convolutional network (TCN) and recurrent neural network (RNN). Comprehensive evaluations of the proposed algorithm have been made on multiple datasets obtained from multiple real greenhouse sites for tomato growing. Based on a statistical analysis of the root mean square errors (RMSEs) between the predicted and actual crop yields, it is shown that the proposed approach achieves more accurate yield prediction performance than both traditional machine learning methods and other classical deep neural networks. Moreover, the experimental study also shows that the historical yield information is the most important factor for accurately predicting future crop yields.
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Nyéki, Anikó, and Miklós Neményi. "Crop Yield Prediction in Precision Agriculture." Agronomy 12, no. 10 (October 11, 2022): 2460. http://dx.doi.org/10.3390/agronomy12102460.
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Predicting crop yields is one of the most challenging tasks in agriculture. It plays an essential role in decision making at global, regional, and field levels. Soil, meteorological, environmental, and crop parameters are used to predict crop yield. A wide variety of decision support models are used to extract significant crop features for prediction. In precision agriculture, monitoring (sensing technologies), management information systems, variable rate technologies, and responses to inter- and intravariability in cropping systems are all important. The benefits of precision agriculture involve increasing crop yield and crop quality, while reducing the environmental impact. Simulations of crop yield help to understand the cumulative effects of water and nutrient deficiencies, pests, diseases, and other field conditions during the growing season. Farm and in situ observations (Internet of Things databases from sensors) together with existing databases provide the opportunity to both predict yields using “simpler” statistical methods or decision support systems that are already used as an extension, and also enable the potential use of artificial intelligence. In contrast, big data databases created using precision management tools and data collection capabilities are able to handle many parameters indefinitely in time and space, i.e., they can be used for the analysis of meteorology, technology, and soils, including characterizing different plant species.
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Portukhay, Oksana, Sergij Lyko, Oleksandr Mudrak, Halyna Mudrak, and Iryna Lohvynenko. "Agroecological Bases of Sustainable Development Strategy for the Rural United Territorial Communities of the Western Polissya Region." Scientific Horizons 24, no. 6 (November 24, 2021): 50–61. http://dx.doi.org/10.48077/scihor.24(6).2021.50-61.
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The article considers the influence of agroecological indicators on the sustainable development of the rural united territorial communities of the Western Polissya region (Ukraine) based on the current state analysis of crop production. To study the state of crop production and determine its role in the development of rural areas of the Western Polissya region, the authors used their field research, as well as data from the Main Departments of Statistics in Rivne and Volyn regions, the State Statistics Service of Ukraine, statistical collection “Crop Production of Ukraine” (2018). The following methods were applied throughout the research process: system analysis, comparison, graphical and statistical methods. The development of crop production was assessed taking into account the dynamics of the following indicators: sown areas of crops (thousand hectares), production volume (gross harvest) of crops (thousand centners), crop yields (thousand hectares-1), sown areas of crops in enterprises and households on the territory of the Western Polissya region in terms of Rivne and Volyn regions for the period from 1995 to 2019. During the study period, changes in the ratio of areas between different crops were discovered: a decrease in the sown area of sugar beet, fruit and berry crops, cereals and legumes, and an increase in sunflower, vegetable crops, etc. An increase in crop yields and a slight decrease in gross harvest were established only for sugar beet in the two regions and fruit and berry crops in the Volyn region. In the region, 51.6% of the sown area of crops is accounted for by households that supply the market with products included in the consumer basket of ordinary citizens: roots and tubers, vegetables, and melons. Enterprises are focused on growing profitable crops (technical, grain, and legumes) for export
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Papadavid, G., and L. Toulios. "The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting." Journal of Agricultural Science 156, no. 5 (October 9, 2017): 599–617. http://dx.doi.org/10.1017/s0021859617000594.
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AbstractRemote sensing can efficiently support the quantification of crop water requirements included in the goal of assessing water footprints, which is to analyse how human activities or specific products relate to issues of water scarcity and pollution and identify how activities and products can become more sustainable from a water perspective. Remote sensing techniques have become popular in estimating actual crop evapotranspiration and hence crop water requirements in recent decades due to the advantages they offer to users, e.g. low cost, regional data and use of maps instead of point measurements as well as saving time. The use of earth observation data supports models’ accuracy in the procedure for assessing water footprint, since no average values are used: instead, users have real values for the specific parameters.The present study provides two examples of how remote sensing techniques are used essentially for estimating evapotranspiration along with crop yield, two basic parameters, for assessing water footprint. Two different case studies have been illustrated to define the methodology proposed, which refers to Mediterranean conditions and can be applied after inferring the necessary field data of each crop. The first case study refers to the application of Surface Energy Balance Algorithm for Land (SEBAL) for estimating evapotranspiration, while the second refers to the Crop Yield prediction. Both elements, such as evapotranspiration and crop yield, are vital for water footprint accounting. Firstly, the SEBAL was adopted, under the essential adaptations for local soil and meteorological conditions for estimating groundnut water requirements. Landsat-5 TM, Landsat-7 Enhanced Thematic Mapper+ and Landsat 8 OLI images were used to retrieve the required spectral data. The SEBAL model is enhanced with empirical equations regarding crop canopy factors, in order to increase the accuracy of crop evapotranspiration estimation. Maps were created for evapotranspiration (ET) using the SEBAL modified model for the area of interest. The results were compared with measurements from an evaporation pan, used as a reference. Statistical comparisons showed that the modified SEBAL can predict ETc in a very effective and accurate way and provide water footprint modellers with high-level crop water data. Yield prediction plays a vital role in calculating water footprint. Having real values rather than taking reference (or averaged) values from FAO is an advantage that Earth Observation means can provide. This is very important in econometric or any other prediction models used for estimating water footprint because using average data reduces accuracy. In this context, crop and soil parameters along with remotely sensed data can be used to develop models that can provide users with accurate yield estimations. In a second step, crop and soil parameters along with the normalized difference vegetation index were correlated to examine whether crop yield can be predicted and to define the actual time-window to predict the yield. Statistical and remote sensing techniques were then applied to derive and map a model that can predict crop yield. The algorithm developed for this purpose indicates that remote sensing observations can predict crop yields effectively and accurately. Using the statistical Student's t test, it was found that there was no statistically significant difference between predicted and real values for crop yield.
Dissertations / Theses on the topic "Crop yields – Statistical methods"
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Adeyemi, Rasheed Alani. "Empirical statistical modelling for crop yields predictions: bayesian and uncertainty approaches." Master's thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/15533.
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Includes bibliographical referencesThis thesis explores uncertainty statistics to model agricultural crop yields, in a situation where there are neither sampling observations nor historical record. The Bayesian approach to a linear regression model is useful for predict ion of crop yield when there are quantity data issue s and the model structure uncertainty and the regression model involves a large number of explanatory variables. Data quantity issues might occur when a farmer is cultivating a new crop variety, moving to a new farming location or when introducing a new farming technology, where the situation may warrant a change in the current farming practice. The first part of this thesis involved the collection of data from experts' domain and the elicitation of the probability distributions. Uncertainty statistics, the foundation of uncertainty theory and the data gathering procedures were discussed in detail. We proposed an estimation procedure for the estimation of uncertainty distributions. The procedure was then implemented on agricultural data to fit some uncertainty distributions to five cereal crop yields. A Delphi method was introduced and used to fit uncertainty distributions for multiple experts' data of sesame seed yield. The thesis defined an uncertainty distance and derived a distance for a difference between two uncertainty distributions. We lastly estimated the distance between a hypothesized distribution and an uncertainty normal distribution. Although, the applicability of uncertainty statistics is limited to one sample model, the approach provides a fast approach to establish a standard for process parameters. Where no sampling observation exists or it is very expensive to acquire, the approach provides an opportunity to engage experts and come up with a model for guiding decision making. In the second part, we fitted a full dataset obtained from an agricultural survey of small-scale farmers to a linear regression model using direct Markov Chain Monte Carlo (MCMC), Bayesian estimation (with uniform prior) and maximum likelihood estimation (MLE) method. The results obtained from the three procedures yielded similar mean estimates, but the credible intervals were found to be narrower in Bayesian estimates than confidence intervals in MLE method. The predictive outcome of the estimated model was then assessed using simulated data for a set of covariates. Furthermore, the dataset was then randomly split into two data sets. The informative prior was later estimated from one-half called the "old data" using Ordinary Least Squares (OLS) method. Three models were then fitted onto the second half called the "new data": General Linear Model (GLM) (M1), Bayesian model with a non-informative prior (M2) and Bayesian model with informative prior (M3). A leave-one-outcross validation (LOOCV) method was used to compare the predictive performance of these models. It was found that the Bayesian models showed better predictive performance than M1. M3 (with a prior) had moderate average Cross Validation (CV) error and Cross Validation (CV) standard error. GLM performed worst with least average CV error and highest (CV) standard error among the models. In Model M3 (expert prior), the predictor variables were found to be significant at 95% credible intervals. In contrast, most variables were not significant under models M1 and M2. Also, The model with informative prior had narrower credible intervals compared to the non-information prior and GLM model. The results indicated that variability and uncertainty in the data was reasonably reduced due to the incorporation of expert prior / information prior. We lastly investigated the residual plots of these models to assess their prediction performance. Bayesian Model Average (BMA) was later introduced to address the issue of model structure uncertainty of a single model. BMA allows the computation of weighted average over possible model combinations of predictors. An approximate AIC weight was then proposed for model selection instead of frequentist alternative hypothesis testing (or models comparison in a set of competing candidate models). The method is flexible and easy to interpret instead of raw AIC or Bayesian information criterion (BIC), which approximates the Bayes factor. Zellner's g-prior was considered appropriate as it has widely been used in linear models. It preserves the correlation structure among predictors in its prior covariance. The method also yields closed-form marginal likelihoods which lead to huge computational savings by avoiding sampling in the parameter space as in BMA. We lastly determined a single optimal model from all possible combination of models and also computed the log-likelihood of each model.
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Pettersson, C. G. "Predicting malting barley protein concentration : based on canopy reflectance and site characteristics /." Uppsala : Dept. of Crop Production Ecology, Swedish University of Agricultural Sciences, 2007. http://epsilon.slu.se/200756.pdf.
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Gangloff, William J. "Spatial statistical analysis of soil properties and crop yields for precision agriculture applications." Access citation, abstract and download form; downloadable file 10.12 Mb, 2004. http://wwwlib.umi.com/dissertations/fullcit/3131671.
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Matthews-Pennanen, Neil. "Assessment of Potential Changes in Crop Yields in the Central United States Under Climate Change Regimes." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7017.
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Climate change is one of the great challenges facing agriculture in the 21st century. The goal of this study was to produce projections of crop yields for the central United States in the 2030s, 2060s, and 2090s based on the relationship between weather and yield from historical crop yields from 1980 to 2010. These projections were made across 16 states in the US, from Louisiana in the south to Minnesota in the north. They include projections for maize, soybeans, cotton, spring wheat, and winter wheat.
Simulated weather variables based on three climate scenarios were used to project future crop yields. In addition, factors of soil characteristics, topography, and fertilizer application were used in the crop production models. Two technology scenarios were used: one simulating a future in which crop technology continues to improve and the other a future in which crop technology remains similar to where it is today.
Results showed future crop yields to be responsive to both the different climate scenarios and the different technology scenarios. The effects of a changing climate regime on crop yields varied both geographically throughout the study area and from crop to crop. One broad geographic trend was greater potential for crop yield losses in the south and greater potential for gains in the north.
Whether or not new technologies enable crop yields to continue to increase as the climate becomes less favorable is a major factor in agricultural production in the coming century. Results of this study indicate the degree to which society relies on these new technologies will be largely dependent on the degree of the warming that occurs.
Continued research into the potential negative impacts of climate change on the current crop system in the United States is needed to mitigate the widespread losses in crop productivity that could result. In addition to study of negative impacts, study should be undertaken with an interest to determine any potential new opportunities for crop development with the onset of higher temperatures as a result of climate change. Studies like this one with a broad geographic range should be complemented by studies of narrower scope that can manipulate climatic variables under controlled conditions. Investment into these types of agricultural studies will give the agricultural sector in the United States greater tools with which they can mitigate the disruptive effects of a changing climate.
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Uno, Yoji. "Application of machine learning methods and airborne hyperspectral remote sensing for crop yield estimation." Thesis, McGill University, 2003. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=80890.
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This study investigated the potential of developing in-season crop yield forecasting and mapping systems based on interpretation of airborne hyperspectral remote sensing imagery by machine learning algorithms. The data used for this study was obtained over a corn (Zea mays L.) field in eastern Canada.The experimental plots were set up at the Emile A. Lods Agronomy Research Center, Montreal, Quebec. Corn was grown under the twelve combinations of three nitrogen application rates (60, 120, and 250 kg N/ha), and four weed control strategies (Broad leaf weed, Grass weed, Broad leaf and grass weed control, and no weed control). The images of the experimental field were taken with a Compact Airborne Spectrographic Imager (CASI) at three times (June 30 for early growth stage, August 5 for tassel stage, and Aug 25 for mature stage) during the year 2000 growing season.
Two machine learning algorithms, Artificial Neural Networks (ANN) and Decision Tree (DT) were evaluated. The performance of ANNs was compared with four conventional modeling methods. For the DT algorithms, two different aspects, (i) DT as a classification method, and (ii) DT as a feature selection tool, were explored in this study.
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Schmer, Marty R. "Switchgrass reestablishment on cropland evaluating net energy, spatial effects, temporal effects, and estimating switchgrass productivity using indirect methods /." 2008. http://0-proquest.umi.com/pqdweb?did=1584062001&sid=1&Fmt=2&clientId=14215&RQT=309&VName=PQD.
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Thesis (Ph.D.)--University of Nebraska-Lincoln, 2008.Title from title screen (site viewed Feb. 17, 2009). PDF text: 196 p. : ill. (some col.) ; 2 Mb. UMI publication number: AAT 3324854. Includes bibliographical references. Also available in microfilm and microfiche formats.
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"Optimising aspects of a soybean breeding programme." Thesis, 2008. http://hdl.handle.net/10413/738.
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Masupha, Elisa Teboho. "Drought analysis with reference to rain-fed maize for past and future climate conditions over the Luvuvhu River catchment in South Africa." Diss., 2017. http://hdl.handle.net/10500/23197.
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Recurring drought conditions have always been an endemic feature of climate in South Africa, limiting maize development and production. However, recent projections of the future climate by the Intergovernmental Panel on Climate Change suggest that due to an increase of atmospheric greenhouse gases, the frequency and severity of droughts will increase in drought-prone areas, mostly in subtropical climates. This has raised major concern for the agricultural sector, particularly the vulnerable small-scale farmers who merely rely on rain for crop production. Farmers in the Luvuvhu River catchment are not an exception, as this area is considered economically poor, whereby a significant number of people are dependent on rain-fed farming for subsistence. This study was therefore conducted in order to improve agricultural productivity in the area and thus help in the development of measures to secure livelihoods of those vulnerable small-scale farmers.
Two drought indices viz. Standardized Precipitation Evapotranspiration Index (SPEI) and Water Requirement Satisfaction Index (WRSI) were used to quantify drought. A 120-day maturing maize crop was considered and three consecutive planting dates were staggered based on the average start of the rainy season. Frequencies and probabilities during each growing stage of maize were calculated based on the results of the two indices. Temporal variations of drought severity from 1975 to 2015 were evaluated and trends were analyzed using the non-parametric Spearman’s Rank Correlation test at α (0.05) significance level. For assessing climate change impact on droughts, SPEI and WRSI were computed using an output from downscaled projections of CSIRO Mark3.5 under the SRES A2 emission scenario for the period 1980/81 – 2099/100. The frequency of drought was calculated and the difference of SPEI and WRSI means between future climate periods and the base period were assessed using the independent t-test at α (0.10) significance level in STATISTICA software.
The study revealed that planting a 120-day maturing maize crop in December would pose a high risk of frequent severe-extreme droughts during the flowering to the grain-filling stage at Levubu, Lwamondo, Thohoyandou, and Tshiombo; while planting in October could place crops at a lower risk of reduced yield and even total crop failure. In contrast, stations located in the low-lying plains of the catchment (Punda Maria, Sigonde, and Pafuri) were exposed to frequent moderate droughts following planting in October, with favorable conditions noted following the December planting date. Further analysis on the performance of the crop under various drought conditions revealed that WRSI values corresponding to more intense drought conditions were detected during the December planting date for all stations. Moreover, at Punda Maria, Sigonde and Pafuri, it was observed that extreme drought (WRSI <50) occurred once in five seasons, regardless of the planting date.
Temporal analysis on historical droughts in the area indicated that there had been eight agricultural seasons subjected to extreme widespread droughts resulting in total crop failure i.e. 1983/84, 1988/89, 1991/92, 1993/94, 2001/02, 2002/03, 2004/05 and 2014/15. Results of Spearman’s rank correlation test revealed weak increasing drought trends at Thohoyandou (ρ = of 0.5 for WRSI) and at Levubu and Lwamondo (ρ = of 0.4 for SPEI), with no significant trends at the other stations. The study further revealed that climate change would enhance the severity of drought across the catchment. This was statistically significant (at 10% significance level) for the near-future and intermediate-future climates, relative to the base period.
Drought remains a threat to rain-fed maize production in the Luvuvhu River catchment area of South Africa. In order to mitigate the possible effects of droughts under climate change, optimal planting dates were recommended for each region. The use of seasonal forecasts during drought seasons would also be useful for local rain-fed maize growers especially in regions where moisture is available for a short period during the growing season. It was further recommended that the Government ensure proper support such as effective early warning systems and inputs to the farmers. Moreover, essential communication between scientists, decision makers, and the farmers can help in planning and decision making ahead of and during the occurrence of droughts.Agriculture, Animal Health and Human Ecology
M. Sc. (Agriculture)
Books on the topic "Crop yields – Statistical methods"
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Fielder, Lonnie L. Measurement of price, yield, and revenue variability for Louisiana crops. Baton Rouge, La: Dept. of Agricultural Economics and Agribusiness, Louisiana Agricultural Experiment Station, Louisiana State University Agricultural Center, 1985.
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Sakamoto, Clarence M. The water satisfaction index for estimating crop yield and harvested/planted area ratio in Botswana. [Gaborone, Botswana]: Republic of Botswana, Dept. of Meteorological Services, Ministry of Works, Transport, and Communications, 1990.
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G, Gauch Hugh. Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Amsterdam: Elsevier, 1992.
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Manfred, Sievers, ed. Instability in world food production: Statistical analysis, graphical presentation, and interpretation. Kiel, West Germany: Wissenschaftsverlag Vauk Kiel, 1985.
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Lakshmi, K. R. Statistical methods for tropical tuber crop research. Thiruvananthapuram: Central Tuber Crops Research Institute, 2003.
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Maerz, Ulrich. Methods to simulate distributions of crop yields based on farmer interviews. Aleppo, Syria: International Center for Agricultural Research in the Dry Areas, 1987.
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M, Cupello James, and Meadows Becki, eds. Managing Six Sigma: A practical guide to understanding, assessing, and implementing the strategy that yields bottom line success. New York: John Wiley, 2001.
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Money and capital markets: Pricing, yields and analysis. 2nd ed. St. Leonards, N.S.W., Australia: Allen & Unwin, 1996.
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Maughan, Chris. Field studies on winter milling wheat: Fertiliser nitrogen programmes for yield and quality improvement : evaluation of methods of monitoring crop nitrogen status. Dublin: University College Dublin, 1997.
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Harrison, Scott. Productivity differences across New South Wales rice farms: Links to resource quality. Canberra, Australia: ABARE, 1999.
Find full textBook chapters on the topic "Crop yields – Statistical methods"
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Kawaye, Floney P., and Michael F. Hutchinson. "Maize, Cassava, and Sweet Potato Yield on Monthly Climate in Malawi." In African Handbook of Climate Change Adaptation, 617–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_120.
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AbstractClimate change and climate variability in Malawi have negatively affected the production of maize, a staple food crop. This has adversely affected food security. On the other hand, there have been increases in growing area, production, yield, consumption, and commercialization of both cassava and sweet potato. Factors behind these increases include the adaptive capacity of these crops in relation to climate change and variability, structural adjustment programs, population growth and urbanization, new farming technologies, and economic development. Cassava and sweet potato are seen to have the potential to contribute to food security and alleviate poverty among rural communities.This study used a simple generic growth index model called GROWEST to model observed yields of maize, cassava, and sweet potato across Malawi between 2001 and 2012. The method can be viewed as a hybrid approach between complex process-based crop models and typical statistical models. For each food crop, the GROWEST model was able to provide a robust correlation between observed yields and spatially interpolated monthly climate. The model parameters, which included optimum growing temperatures and growing seasons, were well determined and agreed with known values. This indicated that these models could be used with reasonable confidence to project the impacts of climate change on crop yield. These projections could help assess the future of food security in Malawi under the changing climate and assist in planning for this future.
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Kusumaningrum, Dian, Rahma Anisa, Valantino Agus Sutomo, and Ken Seng Tan. "Alternative Area Yield Index Based Crop Insurance Policies in Indonesia." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 285–90. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78965-7_42.
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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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Grignani, Carlo, Francesco Alluvione, Chiara Bertora, Laura Zavattaro, Massimo Fagnano, Nunzio Fiorentino, Fabrizio Quaglietta Chiarandà, Mariana Amato, Francesco Lupo, and Rocco Bochicchio. "Field Plots and Crop Yields Under Innovative Methods of Carbon Sequestration in Soil." In Carbon Sequestration in Agricultural Soils, 39–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23385-2_3.
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Castellani, Marco, and Emanuel A. dos Santos. "Prediction of Long-Term Government Bond Yields Using Statistical and Artificial Intelligence Methods." In Studies in Computational Intelligence, 341–67. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01866-9_11.
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Anisa, Rahma, Dian Kusumaningrum, Valantino Agus Sutomo, and Ken Seng Tan. "Potential of Reducing Crop Insurance Subsidy Based on Willingness to Pay and Random Forest Analysis." In Mathematical and Statistical Methods for Actuarial Sciences and Finance, 27–32. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78965-7_5.
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Passamani, Giuliana. "Time Series Convergence within I(2) Models: the Case of Weekly Long Term Bond Yields in the Four Largest Euro Area Countries." In Advanced Statistical Methods for the Analysis of Large Data-Sets, 217–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21037-2_20.
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Rowhani, Pedram, Navin Ramankutty, William J. Martin, Ana Iglesias, Thomas W. Hertel, and Syud A. Ahmed. "The Impacts of Climate Change on Crop Yields in Tanzania: Comparing an Empirical and a Process-Based Model." In Economic Tools and Methods for the Analysis of Global Change Impacts on Agriculture and Food Security, 149–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99462-8_9.
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Mkomwa, Saidi, Amir Kassam, Sjoerd W. Duiker, and Nouhoun Zampaligre. "Livestock integration in conservation agriculture." In Conservation agriculture in Africa: climate smart agricultural development, 215–29. Wallingford: CABI, 2022. http://dx.doi.org/10.1079/9781789245745.0012.
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Abstract Grazing livestock have been presented as an unsurmountable obstacle for Conservation Agriculture (CA) in Africa, because they consume organic cover. But grazing livestock can also make positive contributions to CA, while, if properly managed, sufficient organic cover can be left for soil erosion control and soil health improvement. Urine and manure improve soil fertility and soil health, and increase the agronomic efficiency of fertilizer nutrients. Grazing livestock increase options for crop diversity, such as crop rotations with perennial forages, increased use of cover crops and tree-crop associations. Further, as crop yields improve through application of sustainable intensification methods, greater amounts of above-ground residue become available for livestock nutrition, while greater quantities of below- and above-ground plant residues can be left to improve soil health than are currently returned to the soil. At the same time, in areas where extensive systems are still common, greater amounts of crop residue can be left for soil function because alternative feed sources are available. More research and education on proper integration of livestock in CA in the African context, and successful models of pastoralist-crop farmer collaboration are needed, so both livestock and soil needs can be met.
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Mba, Chikelu, and Hans Dreyer. "The conservation and sustainable use of plant genetic resources for food and agriculture and emerging biotechnologies." In Mutation breeding, genetic diversity and crop adaptation to climate change, 459–68. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249095.0047.
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Abstract The 50% increase in food production required to feed an ever-growing global population, and which must be attained under dire climate change scenarios and other constraints, will not be attained with a 'business as usual' mindset. For crops, the current cultivars will have to be replaced by ones that are more nutritious, stress tolerant and input-use efficient and that would produce higher yields with less external input. Generating such varieties requires significant efficiency enhancements to the conservation and characterization of plant genetic resources for food and agriculture and their use in plant breeding. Genome editing holds great promise in this regard. Its rapid adoption as a relatively cheap and rapid means to generate precise and predictable heritable variations and its universal applicability mirror the developments of the closely associated gene drive. Large amounts of digital sequence data are also increasingly available, while the field of synthetic biology has been expanding rapidly. This all holds great promise for improving and broadening the genetic base of crop varieties for the enhancement of crop productivity without damaging the environment. However, the pace of the scientific and technological developments for these methods has far outstripped that of the requisite policy regimes. The demonstrable potentials notwithstanding, the developments have not been universally accepted. The ongoing debates include whether the products of genome editing, with or without gene drive, should be considered living modified organisms and, if so, subject to the international framework, the Cartagena Protocol on Biosafety to the Convention on Biological Diversity. Another debate is whether digital sequence information should be subject to some access- and-benefit sharing regime, considering that, with the power of synthetic biology, products previously harnessed only from living organisms can now be produced in the laboratory once the DNA sequence is available. There are also debates about ethics. In order to avoid the mistakes of the past, a call is made for evidence-based multi-stakeholder (including especially intergovernmental) dialogues on the safety, fairness and ethics of the use of these emerging biotechnologies, as the stakes are extremely high.
Conference papers on the topic "Crop yields – Statistical methods"
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AMIROV, Marat, Igor SERZHANOV, Farid SHAYKHUTDINOV, and Nicolay SEMUSHKIN. "MAIN DIRECTIONS OF DEVELOPMENT OF SPRING WHEAT PRODUCTION AGRICULTURAL TECHNOLOGIES FOR SUSTAINABLE ARABLE FARMING IN THE FOREST-STEPPE BELT OF THE MIDDLE VOLGA REGION." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.254.
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The article presents results of studies of influence of controlled and environmental factors on production process of different varieties of spring wheat carried out in different soil and climatic conditions of Middle Volga region. The forest-steppe area of the Volga region is one of regions of Russia favorable for spring wheat growing by its natural and climatic conditions. Unbalance of nutrition elements in soil, acid soil and predominantly heavy-textured soil hamper the yield growth. Out of all factors vital for plants (light, heat, moisture and nutrition elements) under consideration, providing plants with nutrition elements and moisture are those limiting high crop yields. he objective of our studies is increasing the crop yield with the simultaneous decrease of the produced goods cost makes it necessary to calculate in advance the possible yield level depending on the influence of different factors: environmental, crop variety and etc. The aim of our studies was to develop methods of growing high yield and high quality crops of different varieties of wheat adjusted for conditions of the forest-steppe black soil in the Volga region. Methods of research using field and laboratory tests, the method of state variety tests of agricultural cultures, phenoldisulfonic acid method, finite difference method, disperse analysis method. A set of observations, records and analysis was carried out during the experiments with implementation Russian methodological guidelines and National State Standards. Statistical processing of the yield data, economic and energy estimates was carried out by the methods recommended by Russia Scientific Research Institute of Agricultural Economy and Union Academy of Agricultural Sciences. Having carried out the analysis of natural resources and genetic potential of the wheat varieties, systems of plant nutrition optimization and influence of their predecessors, we have obtained new data about possibility of increasing the spring durum wheat arable area. We have shown the role of different forms of using nitrogenous fertilizers (on the background of phosphorus – potassium ones) in the increase of productivity and improvement of the spring wheat grain quality. An established optimal norm for Gramma variety spring spelt corn seeding has been established for the conditions of the grey forest soil in the Fore-Kama region of the Republic of Tatarstan and the influence of their nutrition on yield has been found.
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ZVIRBULE, Andra, and Raivis ANDERSONS. "FACTORS INFLUENCING CHANGES OF BEEF CATTLE HERD QUANTITY AND SIZE: CASE OF LATVIA." In RURAL DEVELOPMENT. Aleksandras Stulginskis University, 2018. http://dx.doi.org/10.15544/rd.2017.147.
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Beef production volumes in Latvia have been different over a long period of time, beef output growth has been noted, as well as its sharp decrease, so it is important to analyze, what are the most important factors that are affecting beef production volumes, that will give an idea of the possibilities for beef market regulation. Consequently, the study objective can be defined: Identify factors affecting beef production in Latvia. For this research statistical methods, correlation analysis, induction, deduction, analysis and synthesis were used. These methods gave an accurate picture of factors that are affecting beef production volumes. According to the results, it can be concluded that beef production volumes are significantly affected by such factors as demand for beef. The results of this research indicate that Latvia beef production volumes are affected by the market demand. As an essential factor for increasing the number of beef cattle in Latvia, export opportunities are available where increasing amount of fresh or chilled beef exported in EUR is increasing the number of suckler cows, where there is a close positive relationship (r = 0.76), which indicates an increase in demand from Latvian meat beef holding output. The quality of the soil in the region and climatic conditions will affect the specialization of farms. The largest number of bovine animals is grown in regions of Vidzeme and Latgale, where average cereal production is lowest per hectare. Pierīga and Zemgale regions have high crop yields on average per hectare, so in this region the number of bovine animals is the smallest.
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Jiang, Z. H., J. Zhang, C. H. Yang, Y. Rao, and S. W. Li. "Comparison and Verification of Methods for Multivariate Statistical Analysis and Regression in Crop Modelling." In 2015 International Conference on Electrical, Automation and Mechanical Engineering. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/eame-15.2015.163.
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Kaneko, Daijiro, Peng Yang, and Toshiro Kumakura. "Carbon partitioning as validation methods for crop yields and CO 2 sequestration monitoring in Asia using a photosynthetic-sterility model." In Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2010. http://dx.doi.org/10.1117/12.864887.
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SUBIû, Jonel, Biljana GRUJIû, and Svetlana ROLJEVIû NIKOLIû. "ECOLOGICAL AGRICULTURAL PRODUCTION – OPINIONS AND PRACTICES OF PRODUCERS IN SERBIA." In Competitiveness of Agro-Food and Environmental Economy. Editura ASE, 2022. http://dx.doi.org/10.24818/cafee/2019/8/03.
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Vegetable growing is one of the most intensive branches of agriculture, which is characterized by a high level of consumption of inputs, primarily fertilizers and pesticides. However, vegetable production can also be successfully achieved with reduced use of agrochemicals, actually in a more ecologically acceptable way. The aim of this paper was to examine the attitudes of vegetable producers in the area of eight local government units about ecologically acceptable cultivation practices for these crops. For the purpose of the research, one hundred and sixty vegetable producers were surveyed by questionnaire, and the collected data were processed in the SPSS statistical package, using the descriptive statistics method. The results showed that for 66% of farmers the priority in production is controlled and reduced application of agrochemicals in relation to high yield of vegetables, and also even 90% of producers are ready to shift from conventional to the ecologically acceptable production of vegetables, with the condition of certain benefits, meaning greater incentives for that kind of production. On the other side, direct payments and rural development measures are used by about 60% of surveyed, which may indicate that for expanding the concept of ecologically acceptable production, encompassing greater incentives from the national level, it is necessary to improve knowledge of this concept of production, as well as better applying of existing incentives.
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Eltaher, Yahia, and Shouxiang Ma. "Carbon/Oxygen Spectral Data Processing, its Affiliation to Scintillation Detector Selectivity & their Impact on Reservoir Saturation Monitoring, Lessons Learnt and Recommended Workflow." In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212613-ms.
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Abstract For decades, it has been affirmed that pulsed neutron (PN) spectral Carbon/Oxygen (C/O) logging is the industry's most robust salinity-independent means for reservoir saturation monitoring (RSM); yet C/O logging still comes with considerable uncertainty that has to be identified and handled with ultimate care. In this paper we investigate two main aspects of such uncertainties and showcase some recommendations to enhance the accuracy of the measurement for improved reservoir saturation monitoring. Two fundamental factors affecting C/O measurement are the type of gamma ray (GR) scintillation detector crystals used and the method for C/O spectral data processing. Currently, there are mostly six types of crystals used as GR detectors in commercial PN logging tools for routine operations. Each detector type has its advantages and limitations. With respect to data processing, the most commonly adopted method is the Windows method, due to its simplicity and statistical robustness. Whereas the Yields method is much more complicated to develop and prone to statistical variation, though it tends to provide more accurate results. Similarly, each of these two methods has its own set of advantages and disadvantages. A comprehensive study involved different logging instruments and datasets acquired under various logging environments showed that both the physical properties of the detector, as well as the characteristics of the data processing method, have to be fully considered for optimum results. The Windows method, for instance, can be adequate for detectors of statistical nature. Unlike the Yields method, which requires an optimized set of detector and tool specifications. Where for certain GR detectors, significant differences in C/O data and consequently the calculated fluid saturation were observed when processed by using the Windows and the Yields methods. C/O data processing method selection is commonly fit for purpose; yet with the continuous advancement in GR detection technology, standardization is recommended for accurate and precise log measurement. Accuracy and precision are keys to C/O logging and consequently successful reservoir surveillance and oil field management. Accordingly, a new standard RSM workflow is recommended where all available elements are properly tailored, to enhance the quality of the answer product.
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Singh, Murari P. "Probabilistic HCF Life Estimation of a Mechanical Component." In ASME 2001 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/imece2001/pvp-25211.
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Abstract Goodman Diagram method or similar methods are used to estimate safety of a mechanical structure under high cycle fatigue loading for any combination of alternating and mean stresses. Magnitude of the factor of safety (FS) indicates margin from nominal design capable of desired performance. The value of larger than one of FS is desired to account for uncertainty and variability in loads and material properties. This FS based on stress does not provide any direct knowledge about the life of the mechanical structure. A FS based on life can be derived and used in conjunction with Goodman concept. This method yields an estimate of FS based on life (FN) for a given stress based FS for any combination of alternating and mean stresses. A procedure is described in this paper that helps in estimating reliability of a mechanical structure. Reliability depends on the magnitude of stresses and material properties. Usually variability in load and in material properties can be quantified by a statistical distribution. Methods of probabilistic theories can be used to determine the influence of these variations on the reliability. The procedure utilizes established methods and theories to yield practical evaluation of reliability. First, the modified Goodman equation of factor of safety is combined with the life equation proposed by Jo Dean Morrow (Dowling, 1999). This provides a relationship between calculated factor of safeties based on stress and life. Finally, the developed equations are utilized in a probabilistic approach that incorporates statistical distribution of uncertainties. This procedure yields reliability assessment of a mechanical structure to perform an expected task.
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Nelson, Jacob, G. Austin Marrs, Greg Schmidt, Joseph A. Donndelinger, and Robert L. Nagel. "Evaluating Sampling Methods for Reusing Knowledge From Large and Ill-Structured Qualitative Data Sets." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-67964.
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The desire to use ever growing qualitative data sets of user generated content in the engineering design process in a computationally effective manner makes it increasingly necessary to draw representative samples. This work investigated the ability of alternative sampling algorithms to draw samples with conformance to characteristics of the original data set. Sampling methods investigated included: random sampling, interval sampling, fixed-increment (or systematic) sampling method, and stratified sampling. Data collected through the Vehicle Owner’s Questionnaire, a survey administered by the U.S. National Highway Traffic Safety Administration, is used as a case study throughout this paper. The paper demonstrates that existing statistical methods may be used to evaluate goodness of fit for samples drawn from large bodies of qualitative data. Evaluation of goodness of fit not only provides confidence that a sample is representative of the data set from which it is drawn, but also yields valuable real-time feedback during the sampling process. This investigation revealed two interesting and counterintuitive trends in sampling algorithm performance. The first is that larger sample sizes do not necessarily lead to improved goodness of fit. The second is that depending on the details of implementation, data cleansing may degrade performance of data sampling algorithms rather than improving it. This work illustrates the importance of aligning sampling procedures to data structures and validating the conformance of samples to characteristics of the larger data set to avoid drawing erroneous conclusions based on unexpectedly biased samples of data.
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Japtap, Shubhangi Ramling, Ameeta Ravikumar, Gouri Raut, and Ravi Kumar. "Statistical Optimization of Media for Enhancing Intracellular Lipid Content in Yarrowia Lipolytica NCIM 3589 Grown on Waste Cooking Oil for Biodiesel Production." In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/yckc2922.
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The model oleaginous yeast Yarrowia lipolytica is capable of assimilating and metabolizing hydrophobic substrates and shown the ability to accumulate high amount of lipids for biodiesel production. In the present study, an effective hybrid RSM optimization strategy for media optimization for Yarrowia lipolytica NCIM 3589 grown on waste cooking oil (WCO) and glucose were undertaken to increase its lipid content for biodiesel production. Physico-chemical properties of varied WCO batches were determined and their effect on lipid accumulation in yeast studied. Low variation in lipid content was found in different batches ranging between 25-35% of dry biomass. As lipid content was dependent on extraction methods, different methods were evaluated to maximize yields and a modified acid hydrolysis method (42% lipid content of dry biomass) employed. Lipid accumulation media (LAM) components were initially chosen for a first level of statistical optimization by Plackett-Burman Design (PBD) of experiments using glucose or WCO as carbon sources. The WCO media gave significant increase (5.25 fold) in lipid accumulation when compared to glucose. The most significant factors identified media by PBD, namely, Na2HPO4, NH4Cl, yeast extract and chosen for a second level of optimization studies with a Box-Behnken Design (BBD). Response surface analysis and ANOVA were used to obtain the best-fit quadratic model and brought out the nature of the interactions amongst the three variables. Validation experiments of the model showed that lipid content in 120 h increased from 45.1% in the pre-optimized to 64.5% in optimized media. The FAME profile and fuel properties of the biodiesel were evaluated and found to be in accordance with international standards. The results obtained here after medium optimization hold promise in biodiesel production as the carbon source WCO used is a renewable substrate which not only is abundantly available and cheap, but also addresses the problem of waste disposal.
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GURSKIENĖ, Virginija, and Justina JATUŽYTĖ. "LAND USE IN ŽUVINTAS BIOSPHERE RESERVE." In Rural Development 2015. Aleksandras Stulginskis University, 2015. http://dx.doi.org/10.15544/rd.2015.053.
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The aim of the study – to assess the current land use and sustainable farming possibilities in the area of the Žuvintas Biosphere Reserve. Mathematical statistical analysis, graphing, interviews, induction and other methods were used during the research. Agricultural censuses, agricultural land and crop declaration (that had been carried out between the years 2012 and 2014) and some other data were analyzed. Intensive farming was established in the group of agrarian areas landscape management zones: conventional industrial farming in the landscape management zone. In the analyzed Simnas, Krosna and Igliauka subdistricts land is used quite extensively, therefore restructuring, in order to improve the ecological conditions, is possible not reducing the volume of production, but in accordance with the guidelines. In the territory of the Žuvintas Biosphere Reserve the declared crop area increased by 0.4 per cent from 2012 to 2014, perennial grass area increased by 4.01 per cent. Sustainable farming was set in the Amalvas polder and peat soils as well as in areas sensitive to surface and groundwater pollution. In the major part of the polder extensive agriculture is developed, it is mainly natural grasslands and pastures as well as cultivated grasslands. SWOT analysis was performed.
Reports on the topic "Crop yields – Statistical methods"
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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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Crowley, David E., Dror Minz, and Yitzhak Hadar. Shaping Plant Beneficial Rhizosphere Communities. United States Department of Agriculture, July 2013. http://dx.doi.org/10.32747/2013.7594387.bard.
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PGPR bacteria include taxonomically diverse bacterial species that function for improving plant mineral nutrition, stress tolerance, and disease suppression. A number of PGPR are being developed and commercialized as soil and seed inoculants, but to date, their interactions with resident bacterial populations are still poorly understood, and-almost nothing is known about the effects of soil management practices on their population size and activities. To this end, the original objectives of this research project were: 1) To examine microbial community interactions with plant-growth-promoting rhizobacteria (PGPR) and their plant hosts. 2) To explore the factors that affect PGPR population size and activity on plant root surfaces. In our original proposal, we initially prqposed the use oflow-resolution methods mainly involving the use of PCR-DGGE and PLFA profiles of community structure. However, early in the project we recognized that the methods for studying soil microbial communities were undergoing an exponential leap forward to much more high resolution methods using high-throughput sequencing. The application of these methods for studies on rhizosphere ecology thus became a central theme in these research project. Other related research by the US team focused on identifying PGPR bacterial strains and examining their effective population si~es that are required to enhance plant growth and on developing a simulation model that examines the process of root colonization. As summarized in the following report, we characterized the rhizosphere microbiome of four host plant species to determine the impact of the host (host signature effect) on resident versus active communities. Results of our studies showed a distinct plant host specific signature among wheat, maize, tomato and cucumber, based on the following three parameters: (I) each plant promoted the activity of a unique suite of soil bacterial populations; (2) significant variations were observed in the number and the degree of dominance of active populations; and (3)the level of contribution of active (rRNA-based) populations to the resident (DNA-based) community profiles. In the rhizoplane of all four plants a significant reduction of diversity was observed, relative to the bulk soil. Moreover, an increase in DNA-RNA correspondence indicated higher representation of active bacterial populations in the residing rhizoplane community. This research demonstrates that the host plant determines the bacterial community composition in its immediate vicinity, especially with respect to the active populations. Based on the studies from the US team, we suggest that the effective population size PGPR should be maintained at approximately 105 cells per gram of rhizosphere soil in the zone of elongation to obtain plant growth promotion effects, but emphasize that it is critical to also consider differences in the activity based on DNA-RNA correspondence. The results ofthis research provide fundamental new insight into the composition ofthe bacterial communities associated with plant roots, and the factors that affect their abundance and activity on root surfaces. Virtually all PGPR are multifunctional and may be expected to have diverse levels of activity with respect to production of plant growth hormones (regulation of root growth and architecture), suppression of stress ethylene (increased tolerance to drought and salinity), production of siderophores and antibiotics (disease suppression), and solubilization of phosphorus. The application of transcriptome methods pioneered in our research will ultimately lead to better understanding of how management practices such as use of compost and soil inoculants can be used to improve plant yields, stress tolerance, and disease resistance. As we look to the future, the use of metagenomic techniques combined with quantitative methods including microarrays, and quantitative peR methods that target specific genes should allow us to better classify, monitor, and manage the plant rhizosphere to improve crop yields in agricultural ecosystems. In addition, expression of several genes in rhizospheres of both cucumber and whet roots were identified, including mostly housekeeping genes. Denitrification, chemotaxis and motility genes were preferentially expressed in wheat while in cucumber roots bacterial genes involved in catalase, a large set of polysaccharide degradation and assimilatory sulfate reduction genes were preferentially expressed.
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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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Shani, Uri, Lynn Dudley, Alon Ben-Gal, Menachem Moshelion, and Yajun Wu. Root Conductance, Root-soil Interface Water Potential, Water and Ion Channel Function, and Tissue Expression Profile as Affected by Environmental Conditions. United States Department of Agriculture, October 2007. http://dx.doi.org/10.32747/2007.7592119.bard.
Full textAbstract:
Constraints on water resources and the environment necessitate more efficient use of water. The key to efficient management is an understanding of the physical and physiological processes occurring in the soil-root hydraulic continuum.While both soil and plant leaf water potentials are well understood, modeled and measured, the root-soil interface where actual uptake processes occur has not been sufficiently studied. The water potential at the root-soil interface (yᵣₒₒₜ), determined by environmental conditions and by soil and plant hydraulic properties, serves as a boundary value in soil and plant uptake equations. In this work, we propose to 1) refine and implement a method for measuring yᵣₒₒₜ; 2) measure yᵣₒₒₜ, water uptake and root hydraulic conductivity for wild type tomato and Arabidopsis under varied q, K⁺, Na⁺ and Cl⁻ levels in the root zone; 3) verify the role of MIPs and ion channels response to q, K⁺ and Na⁺ levels in Arabidopsis and tomato; 4) study the relationships between yᵣₒₒₜ and root hydraulic conductivity for various crops representing important botanical and agricultural species, under conditions of varying soil types, water contents and salinity; and 5) integrate the above to water uptake term(s) to be implemented in models. We have made significant progress toward establishing the efficacy of the emittensiometer and on the molecular biology studies. We have added an additional method for measuring ψᵣₒₒₜ. High-frequency water application through the water source while the plant emerges and becomes established encourages roots to develop towards and into the water source itself. The yᵣₒₒₜ and yₛₒᵢₗ values reflected wetting and drying processes in the rhizosphere and in the bulk soil. Thus, yᵣₒₒₜ can be manipulated by changing irrigation level and frequency. An important and surprising finding resulting from the current research is the obtained yᵣₒₒₜ value. The yᵣₒₒₜ measured using the three different methods: emittensiometer, micro-tensiometer and MRI imaging in both sunflower, tomato and corn plants fell in the same range and were higher by one to three orders of magnitude from the values of -600 to -15,000 cm suggested in the literature. We have added additional information on the regulation of aquaporins and transporters at the transcript and protein levels, particularly under stress. Our preliminary results show that overexpression of one aquaporin gene in tomato dramatically increases its transpiration level (unpublished results). Based on this information, we started screening mutants for other aquaporin genes. During the feasibility testing year, we identified homozygous mutants for eight aquaporin genes, including six mutants for five of the PIP2 genes. Including the homozygous mutants directly available at the ABRC seed stock center, we now have mutants for 11 of the 19 aquaporin genes of interest. Currently, we are screening mutants for other aquaporin genes and ion transporter genes. Understanding plant water uptake under stress is essential for the further advancement of molecular plant stress tolerance work as well as for efficient use of water in agriculture. Virtually all of Israel’s agriculture and about 40% of US agriculture is made possible by irrigation. Both countries face increasing risk of water shortages as urban requirements grow. Both countries will have to find methods of protecting the soil resource while conserving water resources—goals that appear to be in direct conflict. The climate-plant-soil-water system is nonlinear with many feedback mechanisms. Conceptual plant uptake and growth models and mechanism-based computer-simulation models will be valuable tools in developing irrigation regimes and methods that maximize the efficiency of agricultural water. This proposal will contribute to the development of these models by providing critical information on water extraction by the plant that will result in improved predictions of both water requirements and crop yields. Plant water use and plant response to environmental conditions cannot possibly be understood by using the tools and language of a single scientific discipline. This proposal links the disciplines of soil physics and soil physical chemistry with plant physiology and molecular biology in order to correctly treat and understand the soil-plant interface in terms of integrated comprehension. Results from the project will contribute to a mechanistic understanding of the SPAC and will inspire continued multidisciplinary research.