Journal articles on the topic 'Crop yields – Statistical methods'
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1
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.
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García-León, David, Raúl López-Lozano, Andrea Toreti, and Matteo Zampieri. "Local-Scale Cereal Yield Forecasting in Italy: Lessons from Different Statistical Models and Spatial Aggregations." Agronomy 10, no. 6 (June 5, 2020): 809. http://dx.doi.org/10.3390/agronomy10060809.
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Statistical, data-driven methods are considered good alternatives to process-based models for the sub-national monitoring of cereal crop yields, since they can flexibly handle large datasets and can be calibrated simultaneously to different areas. Here, we assess the influence of several characteristics on the ability of these methods to forecast cereal yields at the local scale. We look at two diverse agro-climatic Italian regions and analyze the most relevant types of cereal crops produced (wheat, barley, maize and rice). Models of different complexity levels are built for all species by considering six meteorological and remote sensing indicators as candidate predictive variables. Yield data at three different spatial aggregation scales were retrieved from a comprehensive, farm-level dataset over the period 2001–2015. Overall, our results suggest the better predictability of summer crops compared to winter crops, irrespective of the model considered, reflecting a more intricate relationship among winter cereals, their physiology and weather patterns. At higher spatial resolutions, more sophisticated modelling techniques resting on feature selection from multiple indicators outperformed more parsimonious linear models. These gains, however, vanished as data were further aggregated spatially, with the predictive ability of all competing models converging at the agricultural district and province levels. Feature-selection models tended to elicit more satellite-based than meteorological indicators, with a preference for temperature indicators in summer crops, whereas variables describing the water content of the soil/plant were more often selected in winter crops. The selected features were, in general, equally distributed along the plant growing cycle.
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Roloff, G., R. de Jong, R. P. Zentner, C. A. Campbell, and V. W. Benson. "Estimating spring wheat yield variability with EPIC." Canadian Journal of Soil Science 78, no. 3 (August 1, 1998): 541–49. http://dx.doi.org/10.4141/s97-063.
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The Environmental Policy Integrated Climate (EPIC) model has been used on the semiarid temperate Canadian Prairies to estimate crop yield, soil erosion loss, and water and nitrate dynamics. While its estimates of long-term average yields are accurate for most purposes, additional model development is desirable to fully reflect year-to-year variability. We tested the precision of EPIC (version 5300) in estimating mean yields and in replicating yearly yield variability as influenced by the potential evapotranspiration (PET) method, using field data from a 27-yr crop rotation experiment at Swift Current, Saskatchewan. Rotations tested ranged from continuous wheat (Triticum aestivum L.) to fallow-wheat-wheat. Mean estimated yields were compared with measured yields (MY) and detrended yields (DY). Estimated yields and MYs were further compared by regression, ratio of variances due to lack-of-fit and to experimental errors (R), and model efficiency (EF). Mean yields estimated using the Penman-Monteith and the Priestly-Taylor PET methods resulted in significant underestimations, associated with high annual PET values, and were not analysed further. The Hargreaves (H) and Baier-Robertson (BR) PET methods resulted in mean yields not different than MY or DY for most cases, especially the BR method. EPIC with the H method accounted for 18 to 66% of the variability in annual yield estimation, whereas the BR method accounted for 29 to 60%. These were slightly, but not significantly, lower than results obtained with regionally derived statistical crop models. Overall EPIC with the BR PET method provided yield estimates accurate and precise enough for long term studies. The relatively high R and low EF values obtained, though, suggest further improvements in EPIC are necessary to better replicate yearly yield variability. Analysis of yield residuals indicated that EPIC may not be simulating accurately enough the water balance and its effects throughout the off-season and in the early part of the growing season. Key words: Semiarid temperate climate, crop model, potential evapotranspiration
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Shammi, Sadia Alam, and Qingmin Meng. "Modeling the Impact of Climate Changes on Crop Yield: Irrigated vs. Non-Irrigated Zones in Mississippi." Remote Sensing 13, no. 12 (June 9, 2021): 2249. http://dx.doi.org/10.3390/rs13122249.
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Climate change and its impact on agriculture are challenging issues regarding food production and food security. Many researchers have been trying to show the direct and indirect impacts of climate change on agriculture using different methods. In this study, we used linear regression models to assess the impact of climate on crop yield spatially and temporally by managing irrigated and non-irrigated crop fields. The climate data used in this study are Tmax (maximum temperature), Tmean (mean temperature), Tmin (minimum temperature), precipitation, and soybean annual yields, at county scale for Mississippi, USA, from 1980 to 2019. We fit a series of linear models that were evaluated based on statistical measurements of adjusted R-square, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). According to the statistical model evaluation, the 1980–1992 model Y[Tmax,Tmin,Precipitation]92i (BIC = 120.2) for irrigated zones and the 1993–2002 model Y[Tmax,Tmean,Precipitation]02ni (BIC = 1128.9) for non-irrigated zones showed the best fit for the 10-year period of climatic impacts on crop yields. These models showed about 2 to 7% significant negative impact of Tmax increase on the crop yield for irrigated and non-irrigated regions. Besides, the models for different agricultural districts also explained the changes of Tmax, Tmean, Tmin, and precipitation in the irrigated (adjusted R-square: 13–28%) and non-irrigated zones (adjusted R-square: 8–73%). About 2–10% negative impact of Tmax was estimated across different agricultural districts, whereas about −2 to +17% impacts of precipitation were observed for different districts. The modeling of 40-year periods of the whole state of Mississippi estimated a negative impact of Tmax (about 2.7 to 8.34%) but a positive impact of Tmean (+8.9%) on crop yield during the crop growing season, for both irrigated and non-irrigated regions. Overall, we assessed that crop yields were negatively affected (about 2–8%) by the increase of Tmax during the growing season, for both irrigated and non-irrigated zones. Both positive and negative impacts on crop yields were observed for the increases of Tmean, Tmin, and precipitation, respectively, for irrigated and non-irrigated zones. This study showed the pattern and extent of Tmax, Tmean, Tmin, and precipitation and their impacts on soybean yield at local and regional scales. The methods and the models proposed in this study could be helpful to quantify the climate change impacts on crop yields by considering irrigation conditions for different regions and periods.
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Zymaroieva, A., T. Fedoniuk, S. Matkovska, A. Pinkin, and T. Melnychuk. "Analysis of the spatio-temporal trend of sugar beet yield in Polissya and forest steppe ecoregions within Ukraine." IOP Conference Series: Earth and Environmental Science 1049, no. 1 (June 1, 2022): 012073. http://dx.doi.org/10.1088/1755-1315/1049/1/012073.
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Abstract Ukraine has all the preconditions to increase the sugar beet yield, but, at present, comprehensive studies of spatio-temporal variation in the yield of sugar beet in the country have not been conducted. Though, such research is essential for the formation of crop management and yield forecasting in the future. The study aim is to analyze the general spatio-temporal dynamics of sugar beet yield within 10 regions of Ukraine, to identify the determinants of this trend and to characterize the areas of Ukraine regarding the sugar beet yield. Several statistical methods have been applied to the average sugar beet yields data which were provided by the State Statistics Service of Ukraine. The Akaike Information Criterion (AIC) was used to estimate the likelihood of a statistical model to the observed data. To calculate the global spatial autocorrelation coefficient, I-Moran statistics were computed using the Geoda095i program. A spatial database was created in ArcGIS 10.2. The average sugar beet yields within the study area ranged from 154.5 dt/ha to 495.7 dt/ha. The spatio-temporal trend of sugar beet yield has been described by a fourth-degree polynomial. It was determined that the overall trend of sugar beet yields is determined by agroeconomic and agro-technological factors, whose contribution to the yield variation is 72-96%. The areas where high sugar beet yields are ensured by favorable natural conditions, such as soil fertility, were identified, as well as areas with high crop yield potential provided that agricultural and breeding techniques are adequately used.
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Beula, D. Hebsiba, S. Srinivasan, and C. D. Nanda Kumar. "Crop Insurance Prediction Using R for Pradhan Mantri Fasal Bima Yojana in TamilNadu." International Journal of Risk and Contingency Management 10, no. 4 (October 2021): 46–57. http://dx.doi.org/10.4018/ijrcm.2021100104.
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Agriculture is the primary source of livelihood for farmers in many underdeveloped regions, so due to climate change or other risks, crop insurance is thought to be essential, but the research question answered in the current study pertains to insurance program performance. The government-administered crop insurance program was analysed using a mixed methods design. A multiple case study was conducted in the TamilNadu region (India) to analyse the program, identify the causal factors, and collect relevant claim secondary data. Then the R statistical program was applied to analyse crop performance by developing a linear model of crop actual yields versus threshold yields (rabi, paddy, and kharif) using claim payments as the dependent variable. R statistical regression model programming was explained in detail. Recommendations were provided to economic decision makers on how to enhance agricultural insurance and rural development.
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Tarariko, O., T. Ilienko, T. Kuchma, and V. Velychko. "Long-term prediction of climate change impact on the productivity of grain crops in Ukraine using satellite data." Agricultural Science and Practice 4, no. 2 (July 15, 2017): 3–13. http://dx.doi.org/10.15407/agrisp4.02.003.
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Aim. To analyze and predict the climate change impact on the crop structure, yield and gross collections of grain crops in short-term (2025), mid-term (2050) and long-term perspective. Methods. Analysis of long-term series of climatic parameters based on satellite data, climatic modeling, statistical analysis of crop yield and gross collection of grain crops. Results. The positive effect of historical and current climate change on grain crop yields in Ukraine is demonstrated. It is predicted that the preservation of this pattern and the implementation of an integrated system of measures for adapting agroecosystems to warming will promote further increase in the grain crop yield and thus its gross collection. Conclusions. According to the analysis of satellite data and climatic models, further climate warming is predicted and its positive impact on grain crop productivity is forecasted. In case of developing and implementing the measures to adapt agroecosystems to climate change, the grain yield in Ukraine may increase by 25 % in 2025 compared with the current period (2015) and by 29–30 % in 2050; the gross collection of grain crops will reach 75.0 million tons (in 2025) and 79.0–80.0 million tons (in 2050). On condition of effi cient material and technical, scientifi c and informational support, further development of technical means, the reproduction of soil fertility and the improvement of irrigation technologies in the long-term perspective (by 2100), the gross grain collection may reach 92–95 million tons.
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Al-Adhaileh, Mosleh Hmoud, and Theyazn H. H. Aldhyani. "Artificial intelligence framework for modeling and predicting crop yield to enhance food security in Saudi Arabia." PeerJ Computer Science 8 (September 30, 2022): e1104. http://dx.doi.org/10.7717/peerj-cs.1104.
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Predicting crop yields is a critical issue in agricultural production optimization and intensification research. Accurate foresights of natural circumstances a year in advance can have a considerable impact on management decisions regarding crop selection, rotational location in crop rotations, agrotechnical methods employed, and long-term land use planning. One of the most important aspects of precision farming is sustainability. The novelty of this study is to evidence the effective of the temperature, pesticides, and rainfall environment parameters in the influence sustainable agriculture and economic efficiency at the farm level in Saudi Arabia. Furthermore, predicting the future values of main crop yield in Saudi Arabia. The use of artificial intelligence (AI) to estimate the impact of environment factors and agrotechnical parameters on agricultural crop yields and to anticipate yields is examined in this study. Using artificial neural networks (ANNs), a highly effective multilayer perceptron (MLP) model was built to accurately predict the crop yield, temperature, insecticides, and rainfall based on environmental data. The dataset is collected from different Saudi Arabia regions from 1994 to 2016, including the temperature, insecticides, rainfall, and crop yields for potatoes, rice, sorghum, and wheat. For this study, we relied on five different statistical evaluation metrics: the mean square error (MSE), the root-mean-square error (RMSE), normalized root mean square error (NRMSE), Pearson’s correlation coefficient (R%), and the determination coefficient (R2). Analyses of datasets for crop yields, temperature, and insecticides led to the development of the MLP models. The datasets are randomly divided into separate samples, 70% for training and 30% for testing. The best-performing MLP model is characterized by values of (R = 100%) and (R2 = 96.33) for predicting insecticides in the testing process. The temperature, insecticides, and rainfall were examined with different crop yields to confirm the effectiveness of these parameters for increasing product crop yields in Saudi Arabia; we found that these items had highest relationships. The average values are R = 98.20%, 96.50, and 99.14% with for the temperature, insecticides, and rainfall, respectively. Based on these findings, it appeared that each of the parameter categories that are considered (temperature, pesticides, and rainfall) had a similar contribution to the accuracy of anticipated yield projection.
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Moretti de Souza, Jorge Luiz, Stefanie Lais Kreutz Rosa, Karla Regina Piekarski, and Rodrigo Yoiti Tsukahara. "Influence of the AquaCrop soil module on the estimation of soybean and maize crop yield in the State of Parana, Brazil." Agronomía Colombiana 38, no. 2 (May 1, 2020): 234–41. http://dx.doi.org/10.15446/agron.colomb.v38n2.78659.
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The values of the physical-water attributes of soils for use in agricultural simulation models are usually obtained using difficult and time-consuming methods. The objective of this study was to analyze the performance of the AquaCrop model to estimate soybean and maize crop productivity in the region of Campos Gerais (Brazil), with the option of including soil physical-water attributes in the model. Real crop productivities and input data (soil, climate, crop and soil management) were obtained from experimental stations of the ABC Foundation for the crop years 2006 to 2014. Sixty-four yield simulations were performed for soybean (four municipalities) and 42 for maize (three municipalities), evaluating input soil data scenarios of AquaCrop as follows: i) all soil physical-water attributes were measured (standard) and ii) the attributes were measured only using textural classification of the area (alternative). Real and simulated yields were verified by simple linear regression analyses and statistical indices (r, d, c). The standard scenario yielded performances between very good and excellent (0.75<c≤1.0) for soybean and between bad and excellent (0.40<c≤1.0) for maize. The alternative scenario was more variable, with performances between terrible and excellent (0.0<c≤1.0) for soybean and terrible and medium (0.0<c≤0.65) for maize. Using only the soil texture classification in AquaCrop indicated an easier way to estimate crop yields, but low performances may restrict estimates of soybean and maize yields in Campos Gerais.
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Myagkova, Elena Georgievna. "Application of statistical methods in the analysis of sweet pepper yield." Agrarian Scientific Journal, no. 10 (October 27, 2020): 50–55. http://dx.doi.org/10.28983/asj.y2020i10pp50-55.
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The results of studying sweet pepper hybrids in the soil and climate conditions of the Astrakhan region are presented. Studies were conducted in 2017-2019. sweet pepper was cultivated on light chestnut soils, in the mode of drip irrigation, using technology adapted to the climatic conditions of the zone of cultivation of the crop. The objects of research were sweet pepper hybrids of the agricultural firm "Sedek" and elements of the crop structure of these hybrids. The purpose of the study was to determine sweet pepper hybrids with high yield potential. The purpose of the study was to analyze the structure of the crop and identify the elements of the structure that had the greatest impact on productivity. The strength of the elements ' influence and its direction were determined by statistical methods, in particular, correlation analysis was applied. As a result of correlation analysis, elements (variables X) were determined to the greatest extent that affect the yield of sweet pepper. At the stage of regression analysis, these variables were included in the regression model. Using regression analysis, an equation (mathematical formula) was derived that explains how the yield of sweet peppers will change quantitatively depending on changes in variables. According to the results of data processing using statistical methods, the elements of the structure of the sweet pepper crop that had the greatest impact on productivity were determined: the mass of one fruit, g and the thickness of the fruit wall.
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Xie, Yi, and Jianxi Huang. "Integration of a Crop Growth Model and Deep Learning Methods to Improve Satellite-Based Yield Estimation of Winter Wheat in Henan Province, China." Remote Sensing 13, no. 21 (October 30, 2021): 4372. http://dx.doi.org/10.3390/rs13214372.
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Timely and accurate regional crop-yield estimates are crucial for guiding agronomic practices and policies to improve food security. In this study, a crop-growth model was integrated with time series of remotely sensed data through deep learning (DL) methods to improve the accuracy of regional wheat-yield estimations in Henan Province, China. Firstly, the time series of moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) were input into the long short-term memory network (LSTM) model to identify the wheat-growing region, which was further used to estimate wheat areas at the municipal and county levels. Then, the leaf area index (LAI) and grain-yield time series simulated by the Crop Environment REsource Synthesis for Wheat (CERES-Wheat) model were used to train and evaluate the LSTM, one-dimensional convolutional neural network (1-D CNN) and random forest (RF) models, respectively. Finally, an exponential model of the relationship between the field-measured LAI and MODIS NDVI was applied to obtain the regional LAI, which was input into the trained LSTM, 1-D CNN and RF models to estimate wheat yields within the wheat-growing region. The results showed that the linear correlations between the estimated wheat areas and the statistical areas were significant at both the municipal and county levels. The LSTM model provided more accurate estimates of wheat yields, with higher R2 values and lower root mean square error (RMSE) and mean relative error (MRE) values than the 1-D CNN and RF models. The LSTM model has an inherent advantage in capturing phenological information contained in the time series of the MODIS-derived LAI, which is important for satellite-based crop-yield estimates.
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Piasecka, Anna, Piotr Kachlicki, and Maciej Stobiecki. "Analytical Methods for Detection of Plant Metabolomes Changes in Response to Biotic and Abiotic Stresses." International Journal of Molecular Sciences 20, no. 2 (January 17, 2019): 379. http://dx.doi.org/10.3390/ijms20020379.
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Abiotic and biotic stresses are the main reasons of substantial crop yield losses worldwide. Research devoted to reveal mechanisms of plant reactions during their interactions with the environment are conducted on the level of genome, transcriptome, proteome, and metabolome. Data obtained during these studies would permit to define biochemical and physiological mechanisms of plant resistance or susceptibility to affecting factors/stresses. Metabolomics based on mass spectrometric techniques is an important part of research conducted in the direction of breeding new varieties of crop plants tolerant to the affecting stresses and possessing good agronomical features. Studies of this kind are carried out on model, crop and resurrection plants. Metabolites profiling yields large sets of data and due to this fact numerous advanced statistical and bioinformatic methods permitting to obtain qualitative and quantitative evaluation of the results have been developed. Moreover, advanced integration of metabolomics data with these obtained on other omics levels: genome, transcriptome and proteome should be carried out. Such a holistic approach would bring us closer to understanding biochemical and physiological processes of the cell and whole plant interacting with the environment and further apply these observations in successful breeding of stress tolerant or resistant crop plants.
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Rea, Ramón, Orlando De Sousa-Vieira, Alida Díaz, Miguel Ramón, Rosaura Briceño, José George, and Jhonny Demey. "Assessment of yield stability in sugarcane genotypes using non-parametric methods." Agronomía Colombiana 33, no. 2 (May 1, 2015): 131–38. http://dx.doi.org/10.15446/agron.colomb.v33n2.49324.
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The evaluation of performance stability and high yields is essential for yield trials in different environments. This study was carried out to identifysugarcane genotypesthat have both a high mean cane yield, mesured in tons of cane per hectare (TCH), and stability across seven different environments, using 11 non-parametric statistical methods: Si(1), Si(2), Si(3), Si(6), NPI(1), NPI(2), NPI(3), NPI(4), RS, TOP and DE. The data came from acane yield of 20 genotypes, as measured at seven locations over three crop-years in the sugarcane regional trials of the Instituto Nacional de Investigaciones Agrícolas (INIA) of Venezuela. The genotypes V99-213, V99-236 and V00-50 showed promising yields and stability according to all of the non-parametric statistics. The TCH presented a positive association with the TOP, NPI(2), NPI(3) and Si(6) statistics. The analysis distinguished two groups of statistics using a principal component analysis (PCA). The first group (G1) was composed of the TOP, NPI(4), NPI(2), NPI(3), Si(3) and Si(6) statistics, which were located under the concept of dynamic or agronomic stability because they are associated with yield. The other group (G2) was composed of the NPI(1), Si(1), Si(2), DE and RS statistics, which fell within the static or biological stability concept.
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Sylvester-Bradley, R., D. R. Kindred, B. Marchant, S. Rudolph, S. Roques, A. Calatayud, S. Clarke, and V. Gillingham. "Agronōmics: transforming crop science through digital technologies." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 728–33. http://dx.doi.org/10.1017/s2040470017001029.
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Good progress in crop husbandry and science requires that impacts of field-scale interventions can be measured, analysed and interpreted easily and with confidence. The term ‘agronōmics’ describes the arena for research created by field-scale digital technologies where these technologies can enable effective commercially relevant experimentation. Ongoing trials with ‘precision-farm research networks’, along with new statistical methods (and associated software), show that robust conclusions can be drawn from digital field-scale comparisons, but they also show significant scope for improvement in the validity, accuracy and precision of digital measurements, especially those determining crop yields.
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Thai, Thi Huyen, Richard Ansong Omari, Dietmar Barkusky, and Sonoko Dorothea Bellingrath-Kimura. "Statistical Analysis versus the M5P Machine Learning Algorithm to Analyze the Yield of Winter Wheat in a Long-Term Fertilizer Experiment." Agronomy 10, no. 11 (November 13, 2020): 1779. http://dx.doi.org/10.3390/agronomy10111779.
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To compare how different analytical methods explain crop yields from a long-term field experiment (LTFE), we analyzed the grain yield of winter wheat (WW) under different fertilizer applications in Müncheberg, Germany. An analysis of variance (ANOVA), linear mixed-effects model (LMM), and MP5 regression tree model were used to evaluate the grain yield response. All the methods identified fertilizer application and environmental factors as the main variables that explained 80% of the variance in grain yields. Mineral nitrogen fertilizer (NF) application was the major factor that influenced the grain yield in all methods. Farmyard manure slightly influenced the grain yield with no NF application in the ANOVA and M5P regression tree. While sources of environmental factors were unmeasured in the ANOVA test, they were quantified in detail in the LMM and M5P model. The LMM and M5P model identified the cumulative number of freezing days in December as the main climate-based determinant of the grain yield variation. Additionally, the temperature in October, the cumulative number of freezing days in February, the yield of the preceding crop, and the total nitrogen in the soil were determinants of the grain yield in both models. Apart from the common determinants that appeared in both models, the LMM additionally showed precipitation in June and the cumulative number of days in July with temperatures above 30 °C, while the M5P model showed soil organic carbon as an influencing factor of the grain yield. The ANOVA results provide only the main factors affecting the WW yield. The LMM had a better predictive performance compared to the M5P, with smaller root mean square and mean absolute errors. However, they were richer regressors than the ANOVA. The M5P model presented an intuitive visualization of important variables and their critical thresholds, and revealed other variables that were not captured by the LMM model. Hence, the use of different methods can strengthen the statement of the analysis, and thus, the co-use of the LMM and M5P model should be considered, especially in large databases involving multiple variables.
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Houšť, Martin, Blanka Procházková, and Pavel Hledík. "Effect of different tillage intensity on yields and yield-forming factors in winter wheat." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 60, no. 5 (2012): 89–96. http://dx.doi.org/10.11118/actaun201260050089.
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The paper presents results of a study on application of minimum tillage technologies when growing winter wheat. Experiments were performed in the sugar-beet-growing region with loamy chernozem within the period of 2005–2009. Aanalysed and evaluated were effects of different methods of soil processing on yield-forming factors in stands of winter wheat grown after three different preceding crops (i.e. alfalfa, maize for silage and pea). Evaluated were the following four variants of tillage: (1) conventional ploughing to the depth of 0.22 m (Variant 1); (2) ploughing to the depth of 0.15 m (Variant 2); (3) direct sowing into the untilled soil (Variant 3), and (4) shallow tillage to the depth of 0.10 m (Variant 4).The effect of different tillage intensity on winter wheat yields was statistically non-significant after all forecrops. After alfalfa, the highest and the lowest average yields were recorded in Variant 2 (i.e. with ploughing to the depth of 0.15 m) and Variant 3 (direct sowing into the untilled soil), respectively. After maize grown for silage, higher yields were obtained in Variant 2 and Variant 1 (conventional ploughing) while in Variants 4 and 3 the obtained yields were lower. When growing winter wheat after pea as a preceding crop, the highest and the lowest average yields were recorded after direct sowing (Variant 3) and in Variant 1 (i.e. ploughing to the depth of 0.22 m), respectively. Results of studies on effect of different tillage technologies on yields of winter wheat crops indicate that under the given pedological and climatic conditions it is possible to apply methods of reduced tillage intensity. However, the choice of the corresponding technology must be performed with regard to the type of preceding crop.
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Kim, Nari, Kyung-Ja Ha, No-Wook Park, Jaeil Cho, Sungwook Hong, and Yang-Won Lee. "A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015." ISPRS International Journal of Geo-Information 8, no. 5 (May 21, 2019): 240. http://dx.doi.org/10.3390/ijgi8050240.
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This paper compares different artificial intelligence (AI) models in order to develop the best crop yield prediction model for the Midwestern United States (US). Through experiments to examine the effects of phenology using three different periods, we selected the July–August (JA) database as the best months to predict corn and soybean yields. Six different AI models for crop yield prediction are tested in this research. Then, a comprehensive and objective comparison is conducted between the AI models. Particularly for the deep neural network (DNN) model, we performed an optimization process to ensure the best configurations for the layer structure, cost function, optimizer, activation function, and drop-out ratio. In terms of mean absolute error (MAE), our DNN model with the JA database was approximately 21–33% and 17–22% more accurate for corn and soybean yields, respectively, than the other five AI models. This indicates that corn and soybean yields for a given year can be forecasted in advance, at the beginning of September, approximately a month or more ahead of harvesting time. A combination of the optimized DNN model and spatial statistical methods should be investigated in future work, to mitigate partly clustered errors in some regions.
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Kovalev, I. V., D. I. Kovalev, Z. E. Shaporova, A. A. Voroshilova, and D. V. Borovinskii. "Statistical analysis of agro-climatic factors of crop failure of agricultural plots." IOP Conference Series: Earth and Environmental Science 1112, no. 1 (December 1, 2022): 012093. http://dx.doi.org/10.1088/1755-1315/1112/1/012093.
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Abstract The article presents an example of a statistical analysis of agro-climatic factors of crop failure in agricultural plots for the cultivation of rice. It is shown that crop failure is mainly due to a variety of random, independent agro-climatic factors: the sum of active temperatures, the sum of precipitation, soil fertility, etc. Agricultural technology of crop cultivation also has a significant impact on crop failure, which is characterized by the following features: the predecessor, the amount of fertilizer, weeding, the number of days from the bay to the discharge of water, the number of days from mowing to threshing. As methods of statistical data processing, the authors use complex data processing by traditional methods, including comparison of mean values of features, principal component analysis, multiple regression analysis, and discriminant analysis. It is noted that the results of complex processing of the agrotechnical characteristics of plots with different rice yields using the apparatus of one-dimensional and multivariate statistical analysis provide a basis only for identifying general trends, since the agrotechnical system has a high level of complexity. For a deeper and more complete penetration into its essence, an apparatus should be used that can identify and take into account the structural heterogeneity of data about this system.
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Hubenko, L. V., and O. Y. Lyubchich. "The impact of fertilizers on the productivity of white mustard." Scientific Journal Grain Crops 4, no. 2 (December 11, 2020): 289–95. http://dx.doi.org/10.31867/2523-4544/0137.
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Recently, scientists and producers have been paying increasing attention to niche crops that are able to significantly diversify the monocultural oil field of dominance in the crop rotation of sunflower, soybean and rapeseed. In today's climate, one of these crops is mustard, which at the same time, with the ability to form stable yields of seeds and raw materials of good quality, is distinguished by its relative unpretentiousness to external factors. Purpose. Improvement of elements of technology of cultivation and determination of their influence on productivity of mustard white. Methods. The studies involved the use of standardized methods: field – to determine the yield, biometric records and measurements, laboratory – to determine the agrophysical properties of the soil, the content of the basic nutrients in it, to determine the structure of the crop; calculated – evaluation of the economic efficiency of the elements of white mustard growing technology studied; statistical – analysis of variance. The article presents the results of studies to study the effect of different doses of fertilizers, micro fertilizers on seed yields and oil the content in white mustard seeds. Optimal parameters of elements of technology of cultivation of mustard white, which provide maximum yield in the conditions of the northern forest-steppe of Ukraine, are established. As a result of the research, it was found that the highest seed yield of white mustard seed (2,58 t/ha) with oil content (43,29 %) was provided by the application of fertilizer with fertilizer at a dose of N45P60K90 and foliar feeding with Tropicel. The significance of the influence of the investigated factors on the crop yield is estimated. It was established that in 2016–2018. factors in terms of the degree of influence on the yield of the white mustard variety Belaya Princess in terms of importance can be arranged as follows: mineral fertilizers – 14.3 %, treatment of crops with micronutrient fertilizer Tropikel – 52.2 %. Cost-effec-tiveness analysis showed that profitability (225 %) and profit (UAH 32158) reached the highest values by growing white mustard using technology that involves the introduction of N45P60K90 and foliar fertilization of Tropikel microfertilizers. Keywords: mustard white, mineral fertilizers, micro fertilizers, yield, seed quality, cost-effectiveness, Tropicel.
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Melikhova, Elena. "Regularities of beet root crop yield formation based on retrospective data." E3S Web of Conferences 217 (2020): 10007. http://dx.doi.org/10.1051/e3sconf/202021710007.
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The article presents a statistical model of the impact of agromeliorative factors, including methods and modes of irrigation on the productivity of beet root crops in the combination of drip irrigation and fine sprinkling (MAV). The experiments were carried out according to a three – factor scheme providing for the regulation of the phytoclimate (factor A): A1 - drip irrigation; A2-drip irrigation together with the management of the phytoclimate by MAV. Hydrothermal regulation of the phytoclimate was carried out using additional equipment with an interval of 1 hour during the entire vegetation period, provided that the air temperature was higher than the biologically optimal 26°C. the parameters of controlling the lowest humidity of HB (factor B) were taken: B1 – 70 %; B2 – 80 %. On the basis of the dispersion statistical analysis of the results of field studies, the following statistically significant shares of their participation in the formation of the crop were established: factor A – 23%, factor B – 29%, factor C – 44%. The revealed joint influence of factors A and C on the variability of the crop of root crops, the share of which was two percent, exceeds the value of the influence of other pair interactions.
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ÖFVERSTEN, J., L. JAUHIAINEN, H. NIKANDER, and Y. SALO. "Assessing and predicting the local performance of spring wheat varieties." Journal of Agricultural Science 139, no. 4 (December 2002): 397–404. http://dx.doi.org/10.1017/s0021859602002642.
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Each crop variety has a genotype-specific ability to maintain performance over a wide range of environmental conditions. This ability is usually referred to as the sensitivity or adaptability of a variety. Such an ability is an important property, because farmers naturally want to use varieties which perform well in their own fields. Assessing sensitivity has, however, proved difficult, because of problems involved in defining and measuring the wide diversity of natural environments. These problems often lead to split statistical analyses of trial data or statistical models including explanatory variables with no biological interpretation. That causes ambiguity in statistical inference and prediction. The present study shows how the latest advances in statistical research can be applied to overcome some of these difficulties. A key point is to use the conditional expectation of the yield given the environment as a latent explanatory variable. In this way the predicted yields of different varieties can be estimated at any expected environmental yield level. Discussion is restricted to yield data but similar methods can be applied to other performance characters. The Finnish statutory variety trial data are used to illustrate the methods and the results.
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Kapusta, Franciszek. "Rynek warzyw w Polsce i jego powiązania międzynarodowe." Zeszyty Naukowe SGGW w Warszawie - Problemy Rolnictwa Światowego 17(32), no. 2 (June 30, 2017): 93–105. http://dx.doi.org/10.22630/prs.2017.17.2.29.
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The aim of the study was to show: the place and role of vegetable crops in agriculture, changes in their production (area of crops, yields, crops), directions of vegetable development, production of vegetable products, trade with foreign countries and their products. The paper uses such sources of information as: research literature, market analysis of the Institute of Agricultural Economics and Food Economy, publications of the Central Statistical Office – yearbooks. The collected information has been developed and interpreted using a set of methods, including statistical and comparative in vertical form. The assessment of self-sufficiency was done by technical and economic indicators. There was a decrease in the area of vegetable cultivation, the increase in yield and large fluctuations in the size of the crop. The trade balance of fresh and processed vegetables is generally positive. In years 2010 and 2011 it was negative. There is a steady positive balance of trade with the EU-12 and CIS countries, although exports from these countries have fallen since 2013 - especially Russia. On the other hand, the import of vegetables increases. The permanently negative balance is with the EU-15 and other countries.
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Zhildikbaeva, A., A. Zhyrgalova, and V. Nilipovsky. "Effect of heavy metals on soil fertility and crop yields." Problems of AgriMarket, no. 4 (December 15, 2022): 148–55. http://dx.doi.org/10.46666/2022-4.2708-9991.16.
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One of the important tasks of modern agroecology is the study of the patterns of circulation in the biosphere of chemical elements that are regulators of biological processes. The goal – is to determine the quantitative and qualitative effect of heavy metals in soil on crop yields and beneficial properties of products obtained. At the same time, it was found that soil contaminated with heavy metals not only worsens the quality of products and food, but also reduces cadastral value of land. Methods – economic and statistical in analysis and assessment of the current state, abstract and logical, used to identify industry and regional characteristics. Results – urgent problem of degradation of agricultural lands, their desertification in the Republic of Kazakhstan is considered. The conducted research shows that the content of lead and arsenic corresponds to the norm, and cadmium and mercury exceed the normative indicators and do not meet food safety requirements. Conclusions – heavy metals lead, cadmium, mercury, arsenic are toxic even in very low concentrations. Heavy metals enter agricultural lands from mineral fertilizers and plant protection products. It is generally accepted that their effect is negative if the yield is significantly reduced by 10% or more. It is necessary to carry out a detailed survey of the sphere of agricultural production on contaminated soils. It is practically impossible to reduce total concentration of heavy metals in unproductive arable land, but it is possible to significantly reduce their mobility and make them less accessible to plants, reduce accumulation of toxic substances in their biomass, improve the quality of land plots and, accordingly, their cadastral price.
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Mashchenko, Yu V., I. M. Semeniaka, M. I. Cheriachukin, and O. M. Hryhoreva. "Effectiveness of short-term crop rotations under different fertilization systems in the insufficient moisture zone of the Right-Bank Steppe of Ukraine." Scientific Journal Grain Crops 6, no. 1 (August 15, 2022): 169–76. http://dx.doi.org/10.31867/2523-4544/0220.
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Topicality. In the condition of insufficient moisture in the Right Bank Steppe of Ukraine, agriculture is associated with weather risks, non-compliance with the appropriate structure of sown areas and fertilization systems, which makes it difficult to obtain high and stable yields of agricultural crops. The development of agricultural systems is the basis for increasing both yield levels and the competitiveness of the agricultural industry as a whole. Aim. To study the influence of fertilization systems on the fertility of ordinary chernozems and crop productivity in biological short-term crop rotations. Methods. Field trial, laboratory, statistical methods. Results. It was found that the field crops of both grain-fallow-row and grain-row crop rotations were formed the highest productivity under high level of fertilization. Under different fertilization systems, it was noted that the productivity of grain-fallow-row crop rotation is higher than grain-row crop rotation by 8.3 t/ha or by 5.6 %. The productivity of both crop rotations was increased by an average of 6.4–7.8 % due to the application of microbial preparations against the background of mineral and organomineral fertilization systems. The content of mobile phosphorus and exchangeable potassium in all variants of both crop rotations increased due to studied fertilizer rates, but these rates were insufficient to maintain the content of nitrogen and humus at the initial level. In both crop rotations, it was noted that the lowest degree of "burning" humus was on the background of the organomineral fertilizers. When organomineral fertilizers were applied in the grain-fallow-row crop rotation, this indicator was 0.50 % that was 0.06 % less compared to variant with the mineral fertilization and control, and in grain-row crop rotation, this indicator was 0.46 % that was less by 0.11 and 0.06 %, respectively. Conclusions. Profit at the level (on average) of UAH 9114.4/ha was obtained in grain-fallow-row crop rotation, it is more by UAH 1039/ha, or 11.4 % compared to grain-row crop rotation. The advantage was that the predecessor residues in the organomineral fertilizer system was used as organic fertilizer, which have a positive effect on the synthesis of organic matter in the soil, productivity and economic efficiency. Keywords: crop rotations, fertilizers, yield, productivity, soil fertility, economic efficiency.
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Rosenstock, Todd, and Patrick Brown. "Integration of Precision Agriculture and Systems Modeling in Pistachio." HortScience 40, no. 4 (July 2005): 1141E—1142. http://dx.doi.org/10.21273/hortsci.40.4.1141e.
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Alternate bearing exerts economic and environmental consequences through unfulfilled yield potential and fertilizer runoff, respectively. We will discuss a systematic biological–statistical modeling management integration approach to address the concert of mechanisms catalyzing alternate bearing. New engineering technologies (precision harvesting, spatially variable fertigation, and mathematical crop modeling) are enabling optimization of alternate bearing systems. Four years of harvest data have been collected, documenting yield per tree of an 80-acre orchard. These results have shown variability within orchard to range from 20–180 lbs per tree per year. Results indicate irregular patterns not directly correlated to previous yield, soil, or tissue nutrient levels, or pollen abundance. Nor does significant autocorrelation of high or low yields occur throughout the orchard, suggesting that genetically dissimilar rootstocks may have significant impact. The general division of high- and low-yielding halves of the orchard may infer a biotic incongruency in microclimates. This orchard does not display a traditional 1 year-on, 1 year-off cyclic pattern. Delineation of causal mechanisms and the ability to manage effectively for current demands will empower growers to evaluate their fertilization, irrigation, male: female ratio, site selection, and economic planning. In comparison to annual crops, the application of precision agriculture to tree crops is more complex and profitable. When applied in conjunction, the aforementioned methods will have the ability to forecast yields, isolate mechanisms of alternate bearing, selectively manage resources, locate superior individuals, and establish new paradigms for experimental designs in perennial tree crops.
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Chen, Shuo, Weihang Liu, Puyu Feng, Tao Ye, Yuchi Ma, and Zhou Zhang. "Improving Spatial Disaggregation of Crop Yield by Incorporating Machine Learning with Multisource Data: A Case Study of Chinese Maize Yield." Remote Sensing 14, no. 10 (May 12, 2022): 2340. http://dx.doi.org/10.3390/rs14102340.
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Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R2) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.
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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.
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Wahab, Ibrahim, Magnus Jirström, and Ola Hall. "An Integrated Approach to Unravelling Smallholder Yield Levels: The Case of Small Family Farms, Eastern Region, Ghana." Agriculture 10, no. 6 (June 6, 2020): 206. http://dx.doi.org/10.3390/agriculture10060206.
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Yield levels and the factors determining crop yields is an important strand of research on rainfed family farms. This is particularly true for Sub-Saharan Africa (SSA), which reports some of the lowest crop yields. This also holds for Ghana, where actual yields of maize, the most important staple crop, are currently about only a third of achievable yields. Developing a comprehensive understanding of the factors underpinning these yield levels is key to improving them. Previous research endeavours on this frontier have been incumbered by the mono-disciplinary focus and/or limitations relating to spatial scales, which do not allow the actual interactions at the farm level to be explored. Using the sustainable livelihoods framework and, to a lesser extent, the induced innovation theory as inspiring theoretical frames, the present study employs an integrated approach of multiple data sources and methods to unravel the sources of current maize yield levels on smallholder farms in two farming villages in the Eastern region of Ghana. The study relies on farm and household survey data, remotely-sensed aerial photographs of maize fields and photo-elicitation interviews (PEIs) with farmers. These data cover the 2016 major farming season that spanned the period March–August. We found that the factors that contributed to current yield levels are not consistent across yield measures and farming villages. From principal component analysis (PCA) and multiple linear regression (MLR), the timing of maize planting is the most important determinant of yield levels, explaining 25% of the variance in crop cut yields in Akatawia, and together with household income level, explaining 32% of the variance. Other statistically significant yield determinants include level of inorganic fertiliser applied, soil penetrability and phosphorus content, weed control and labour availability. However, this model only explains a third of the yields, which implies that two-thirds are explained by other factors. Our integrated approach was crucial in further shedding light on the sources of the poor yields currently achieved. The aerial photographs enabled us to demonstrate the dominance of poor crop patches on the edges and borders of maize fields, while the PEIs further improved our understanding of not just the causes of these poor patches but also the factors underpinning delayed planting despite farmers’ awareness of the ideal planting window. The present study shows that socioeconomic factors that are often not considered in crop yield analyses—land tenure and labour availability—often underpin poor crop yields in such smallholder rainfed family farms. Labour limitations, which show up strongly in both in the MLR and qualitative data analyses, for example, induces certain labour-saving technologies such as multiple uses of herbicides. Excessive herbicide use has been shown to have negative effects on maize yields.
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Messina, Carlos D., Fred van Eeuwijk, Tom Tang, Sandra K. Truong, Ryan F. McCormick, Frank Technow, Owen Powell, et al. "Crop Improvement for Circular Bioeconomy Systems." Journal of the ASABE 65, no. 3 (2022): 491–504. http://dx.doi.org/10.13031/ja.14912.
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HighlightsWe describe and demonstrate a multidimensional framework to integrate environmental and genomic predictors to enable crop improvement for a circular bioeconomy.A model training procedure based on multiple phenotypes is shown to improve predictive skill.The decision set comprised of model outputs can inform selection for both productivity and circularity metrics.Abstract. Contemporary agricultural systems are poised to transition from linear to circular, adopting concepts of recycling, repurposing, and regeneration. This transition will require changing crop improvement objectives to consider the entire system, and thus provide solutions to improve complex systems for higher productivity, resource use efficiency, and environmental quality. The methods and approaches that underpinned the doubling of yields during the last century may no longer be fully adequate to target crop improvement for circular agricultural systems. Here we propose a multidimensional framework for prediction with outcomes useful to assess both crop performance traits and environmental sustainability of the designed agricultural systems. The study focuses on maize harvestable grain yield and total carbon production, water use, and use efficiency for yield and carbon. The framework builds on the crop growth model whole genome prediction system, which is enabled by advanced phenomics and the integration of symbolic and sub-symbolic artificial intelligence. We demonstrate the approach and prediction accuracy advantages over a standard statistical genomic prediction approach used to breed maize hybrids for yield, flowering time, and kernel set using a dataset comprised of 7004 hybrids, 103 breeding populations, and 62 environments resulting from six years of experimentation in maize drought breeding in the U.S. We propose this framework to motivate a dialogue for how to enable circularity in agriculture through prediction-based systems design. Keywords: Circular bioeconomy, Circular economy, Crop improvement, Crop models, Drought, Gene editing, Genomic prediction, Maize, Plant breeding.
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Messina, Carlos D., Fred van Eeuwijk, Tom Tang, Sandra K. Truong, Ryan F. McCormick, Frank Technow, Owen Powell, et al. "Crop Improvement for Circular Bioeconomy Systems." Journal of the ASABE 65, no. 3 (2022): 491–504. http://dx.doi.org/10.13031/ja.14912.
Full textAbstract:
HighlightsWe describe and demonstrate a multidimensional framework to integrate environmental and genomic predictors to enable crop improvement for a circular bioeconomy.A model training procedure based on multiple phenotypes is shown to improve predictive skill.The decision set comprised of model outputs can inform selection for both productivity and circularity metrics.Abstract. Contemporary agricultural systems are poised to transition from linear to circular, adopting concepts of recycling, repurposing, and regeneration. This transition will require changing crop improvement objectives to consider the entire system, and thus provide solutions to improve complex systems for higher productivity, resource use efficiency, and environmental quality. The methods and approaches that underpinned the doubling of yields during the last century may no longer be fully adequate to target crop improvement for circular agricultural systems. Here we propose a multidimensional framework for prediction with outcomes useful to assess both crop performance traits and environmental sustainability of the designed agricultural systems. The study focuses on maize harvestable grain yield and total carbon production, water use, and use efficiency for yield and carbon. The framework builds on the crop growth model whole genome prediction system, which is enabled by advanced phenomics and the integration of symbolic and sub-symbolic artificial intelligence. We demonstrate the approach and prediction accuracy advantages over a standard statistical genomic prediction approach used to breed maize hybrids for yield, flowering time, and kernel set using a dataset comprised of 7004 hybrids, 103 breeding populations, and 62 environments resulting from six years of experimentation in maize drought breeding in the U.S. We propose this framework to motivate a dialogue for how to enable circularity in agriculture through prediction-based systems design. Keywords: Circular bioeconomy, Circular economy, Crop improvement, Crop models, Drought, Gene editing, Genomic prediction, Maize, Plant breeding.
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Pejić, Borivoj, Ksenija Mačkić, Ivana Bajić, Vladimir Sikora, Dejan Simić, Milena Jančić-Tovjanin, and Boško Gajić. "Calculation of maize evapotranspiration using evaporation and reference evapotranspiration methods." Zemljiste i biljka 69, no. 2 (2020): 15–25. http://dx.doi.org/10.5937/zembilj2002015p.
Full textAbstract:
Analysis of irrigation scheduling of maize was done by using evaporation from the free water surface (Eo) and correction coefficients (k) and reference evapotranspiration (ETo) and crop coefficients (kc). The field experiment was carried out in 2018 on the Experimental field of the Institute of field and vegetable crops in the Department of alternative crops in Bački Petrovac. Maize hybrid NS 6030 was used for the trials. The plants were irrigated by a drip system with a lateral in each row with drippers spaced every 0.33 m. The drippers had an average flow rate of 2.0 l h-1 under the pressure of 70 kPa. The differences in yield of maize in the irrigation conditions were not statistically significant compared to the variant without irrigation because the year was favorable for maize production. As well there was no difference among variants used for the calculation of maize evapotranspiration. Maize evapotranspiration in the growing season (ETm) were 502 mm and 429 mm by using ETo and kc and Eo and k. Monthly values of ETm during the growing season were consistent regardless of the calculation methods, except in July. Values of ETm in July of 151 mm and 107 mm calculated by using ETo and kc and Eo and k methods as well the daily values which are correlated with the monthly have to be checked in irrigation scheduling of maize in the following investigation period. If statistical significance in maize yield between different methods of calculation is determined, the procedure with a higher yield has to be accepted in the calculation of ETm in the climatic conditions of the Vojvodina region. Otherwise, if the differences in maize yield are not statistical significance a method of calculation by using Eo and k will be recommended, because the value of the lower daily water used on maize evapotranspiration may be considered more realistic.
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Hamed, Raed, Anne F. Van Loon, Jeroen Aerts, and Dim Coumou. "Impacts of compound hot–dry extremes on US soybean yields." Earth System Dynamics 12, no. 4 (November 30, 2021): 1371–91. http://dx.doi.org/10.5194/esd-12-1371-2021.
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Abstract. The US agriculture system supplies more than one-third of globally traded soybean, and with 90 % of US soybean produced under rainfed agriculture, soybean trade is particularly sensitive to weather and climate variability. Average growing season climate conditions can explain about one-third of US soybean yield variability. Additionally, crops can be sensitive to specific short-term weather extremes, occurring in isolation or compounding at key moments throughout crop development. Here, we identify the dominant within-season climate drivers that can explain soybean yield variability in the US, and we explore the synergistic effects between drivers that can lead to severe impacts. The study combines weather data from reanalysis and satellite-informed root zone soil moisture fields with subnational crop yields using statistical methods that account for interaction effects. On average, our models can explain about two-thirds of the year-to-year yield variability (70 % for all years and 60 % for out-of-sample predictions). The largest negative influence on soybean yields is driven by high temperature and low soil moisture during the summer crop reproductive period. Moreover, due to synergistic effects, heat is considerably more damaging to soybean crops during dry conditions and is less problematic during wet conditions. Compounding and interacting hot and dry (hot–dry) summer conditions (defined by the 95th and 5th percentiles of temperature and soil moisture respectively) reduce yields by 2 standard deviations. This sensitivity is 4 and 3 times larger than the sensitivity to hot or dry conditions alone respectively. Other relevant drivers of negative yield responses are lower temperatures early and late in the season, excessive precipitation in the early season, and dry conditions in the late season. We note that the sensitivity to the identified drivers varies across the spatial domain. Higher latitudes, and thus colder regions, are positively affected by high temperatures during the summer period. On the other hand, warmer southeastern regions are positively affected by low temperatures during the late season. Historic trends in identified drivers indicate that US soybean production has generally benefited from recent shifts in weather except for increasing rainfall in the early season. Overall, warming conditions have reduced the risk of frost in the early and late seasons and have potentially allowed for earlier sowing dates. More importantly, summers have been getting cooler and wetter over the eastern US. Nevertheless, despite these positive changes, we show that the frequency of compound hot–dry summer events has remained unchanged over the 1946–2016 period. In the longer term, climate models project substantially warmer summers for the continental US, although uncertainty remains as to whether this will be accompanied by drier conditions. This highlights a critical element to explore in future studies focused on US agricultural production risk under climate change.
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Likhatsevich, A. P. "Mathematical model of agricultural crop yield." Proceedings of the National Academy of Sciences of Belarus. Agrarian Series 59, no. 3 (August 5, 2021): 304–18. http://dx.doi.org/10.29235/1817-7204-2021-59-3-304-318.
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Theoretical basis for presenting research results in agricultural science is mathematical statistics and probability theory using empirical forms of generalization of experimental data. To improve the methods of planning field experiment and processing its data using digital technologies, we proposed to use mathematical modeling based on physical principle of balance of cause-and-effect interactions in a closed physical system as a priority option. When analyzing impact of environmental factors on crop yields, the initial provisions, the mathematical modeling of the crop yield is based, on are not associated with characteristics of crops and natural conditions, therefore, the model options are universal in application and are valid for any agricultural crop, regardless of the region of cultivation. To ensure statistically correct digital information, based on the established forms of mathematical model, the field experiment layout aimed at establishing the dependence of the crop yield on yield-forming factors should include at least 4 options for nutritional levels (NPK) with a research duration of at least 4 years. To check the accuracy of the developed crop yield model, the data of independent field experiments of Professor N.N. Semenenko with barley and winter triticale has been used. It has been determined that, in Belarus, yield-forming factors, as a result of their impact on the grain yield, are arranged in the following decreasing sequence: total dose of applied NPK º the amount of precipitation during the active phases of growing season → air temperature for the same period. Calculations have shown that decrease in the number of yield-forming factors taken into account in the mathematical model from three (food, moisture and heat) to two (food and moisture) reduces the accuracy of calculating the grain crop yield insignificantly.
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Bornhofen, Elesandro, Giovani Benin, Lindolfo Storck, Leomar Guilherme Woyann, Thiago Duarte, Matheus Giovane Stoco, and Sergio Volmir Marchioro. "Statistical methods to study adaptability and stability of wheat genotypes." Bragantia 76, no. 1 (March 2017): 1–10. http://dx.doi.org/10.1590/1678-4499.557.
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ABSTRACT The sensitivity of wheat crop to environmental variations frequently results in significant genotype (G) x environment (E) interaction (GEI). We compared statistical methods to analyze adaptability and stability of wheat genotypes in value for cultivation and use (VCU) trials. We used yield performance data of 22 wheat genotypes evaluated in three locations (Guarapuava, Cascavel, and Abelardo Luz) in 2012 and 2013. Each trial consisted of a complete randomized block design with three replications. The GEI was evaluated using methodologies based on mixed models, analysis of variance, linear regression, multivariate, and nonparametric analysis. The Spearman’s rank correlation coefficient was used to verify similarities in the genotype selection process by different methodologies. The Annicchiarico, Lin and Binns modified methodologies, as well as the Harmonic Mean of the Genetic Values (HMGV) allowed to identify simultaneously highly stable and productive genotypes. The grain yield is not associated with Wricke, Eberhart and Russell stability parameters, scores of the first principal component of the AMMI1 method, and GGE biplot stability, indicating that stable genotypes are not always more productive. The data analyzed in this study showed that the AMMI1 and GGE biplot methods are equivalent to rank genotypes for stability and adaptability.
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Jury, Mark R. "Ethiopian Highlands Crop-Climate Prediction: 1979–2009." Journal of Applied Meteorology and Climatology 52, no. 5 (May 2013): 1116–26. http://dx.doi.org/10.1175/jamc-d-12-0139.1.
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AbstractThis study compares different methods of predicting crop-related climate in the Ethiopian highlands for the period 1979–2009. A target index (ETH4) is developed as an average of four variables in the June–September season—rainfall, rainfall minus evaporation, estimated latent heat flux, and vegetation, following correlation with crop yields at Melkassa, Ethiopia (8.4°N, 39.3°E, 1550 m elevation). Predictors are drawn from gridded near-global fields of surface temperature, surface air pressure, and 200-hPa zonal wind in the preceding December–March season. Prediction algorithms are formulated by stepwise multivariate regression. The first set of predictors derive from objective principal component (PC) time scores with tropical loading patterns, and the second set is based on key areas determined from correlation with the target index. The second PC of upper zonal wind reveals a tropical–subtropical dipole that is correlated with ETH4 at two-season lead time (correlation coefficient r = −0.53). Point-to-field regression maps show high-latitude signals in surface temperature (positive in North America and negative in Eurasia) and air pressure (negative in the North Pacific Ocean and positive in the South Pacific). Upper zonal winds are most strongly related with ETH4 over the tropical Pacific and Africa at two-season lead time. The multivariate algorithm that is based on PC predictors has an adjusted r2 fit of 0.23, and the algorithm using key-area predictors achieves r2 = 0.37. In comparison, numerical model forecasts reach r2 = 0.33 for ECMWF simulations but are low for other models. The statistical results are specific to the ETH4 index, which is a climate proxy for crop yields in the Ethiopian highlands.
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Zawieja, Bogna, Ewa Bakinowska, and Andrzej Bichoński. "Evaluation of spring barley breeding lines in a two-year multi-location experiment using some statistical methods." Biometrical Letters 53, no. 2 (December 1, 2016): 149–62. http://dx.doi.org/10.1515/bile-2016-0011.
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AbstractIn breeding experiments conducted prior to tests connected with the registration of new breeding lines of crops, pre-preliminary and preliminary trials are carried out. In this study a comparison was made among some models of analysis of variance, in relation to the selection of new breeding lines of spring barley (Hordeum vulgare L.). The aim is to determine whether the choice of model of analysis of variance may influence the choice of tested breeding lines. The trait considered was the yield in two years of trials. A more comprehensive analysis of variance model was found to be superior. It was also found that the results of analyses performed using average measurements for lines significantly differ from those obtained on the basis of all measurements. It was concluded that the type of ANOVA model used may have an impact on inferences about breeding lines. Moreover, a lack of stability in the yields of tested lines was revealed, implying the necessity of several years of trials.
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Stettmer, Matthias, Martin Mittermayer, Franz-Xaver Maidl, Jürgen Schwarzensteiner, Kurt-Jürgen Hülsbergen, and Heinz Bernhardt. "Three Methods of Site-Specific Yield Mapping as a Data Source for the Delineation of Management Zones in Winter Wheat." Agriculture 12, no. 8 (July 29, 2022): 1128. http://dx.doi.org/10.3390/agriculture12081128.
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In this study, three digital, site-specific, yield-mapping methods for winter wheat were examined, and their precision was evaluated. The crop yields of heterogeneous fields at three locations were determined on a site-specific basis using a yield-recording system composed of a combine harvester and algorithms based on reflection measurements made via satellites, as well as a tractor-mounted sensor. As a reference, the yield was determined with a plot harvester (ground truth data). The precision of the three methods was evaluated via statistical indicators (mean, median, minimum, maximum, and standard deviation) and correlation analyses between the yield of the ground truth data and the respective method. The results show a yield variation of 4.5–10.9 t ha−1 in the trial fields. The yield of the plot harvester was strongly correlated with the yield estimate from the sensor data (R2 = 0.71–0.75), it was moderately correlated with the yield estimate from the satellite data (R2 = 0.53–0.68), and it ranged from strongly to weakly correlated with the yield map of the combine harvester (R2 = 0.30–0.72). The absolute yield can be estimated using sensor data. Slight deviations (<10%) in the absolute yield are observed with the combine harvester, and there are clear deviations (±48%) when using the satellite data. The study shows differences in the precision and accuracy of the investigated methods. Further research and optimization are urgently needed to determine the exactness of the individual methods.
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Kussaiynov, T. A., and B. O. Assilov. "Impact of technological innovations on labor productivity in crop production." Problems of AgriMarket, no. 3 (September 15, 2022): 82–89. http://dx.doi.org/10.46666/2022-3.2708-9991.09.
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An important element in managing the economic results of economic entities is the search for reserves to increase labor productivity. The goal is to improve the methodology for calculating the impact of adaptive farming technologies on intensification of labor activity in grain production. Methods – statistical: data on technical solutions, machine systems, types of fertilizers, hydrothermal conditions, yields and areas of wheat crops, the number of workers employed in grain subcomplex-steppe zone of Northern Kazakhstan for 1961-2020. To assess the impact of specific components on the level of working productivity, the methods of regression and index analysis were used. The impact of production factors, including technological innovations, on the change in the criterion of beneficial effect in agricultural sector is quite accurately calculated by the proposed methods and procedures. Results – it has been determined that adaptive opportunities for obtaining agricultural products have a positive effect on increasing profitability and profi tability in agroindustrial complex. The expansion of the range of indicators and the tasks set makes it possible to make calculations with even greater reliability and accuracy. However, it should be borne in mind that the system of statistical accounting in countries with transit economies is at the stage of improvement. Conclusions – since the sixties, labor productivity in grain industry has undergone significant changes. Its growth rates differed in different time periods. Until the end of the last century, the indicator changed relatively slowly. A jump-like growth of more than two times was observed in the 2000s, due to a number of reasons, primarily the use of resource-saving mechanisms adapted to real external conditions and high-performance machines.
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Vieira, Sidney Rosa, and Sonia Carmela Falci Dechen. "Spatial variability studies in São Paulo, Brazil along the last twenty five years." Bragantia 69, suppl (2010): 53–66. http://dx.doi.org/10.1590/s0006-87052010000500007.
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Soil properties vary in space due to many causes. For this reason it is wise to know the magnitude and behaviour of the variability for adequate data analysis and decision making. Our work on spatial variability of soil properties in São Paulo, Brazil began in 1982 with a very simple soil sampling in a small field. Much progress has been made since then on sampling designs, field equipment and methods, and mostly on computation equipment and softwares. This paper reports the results corresponding to some aspects of this progress, as far as the field, analysis and computation work are concerned. The objective of this study was to illustrate the use of geostatistics in data analysis for three sampling conditions on long term no-tillage system. The analysis is done on a wide range of field scales, variables, sampling schemes as well as repeating sampling scheme for the same variable in different years. Semivariograms are compared for the same variables in different scales and sampling dates and depths as to provide a guide for sampling spacing and number of samples. Normalized crop yield parameters for many years are used in the discussion of time variability and on the use of yield maps to locate management zones. The time of the year in which measurements of soil physical properties are made affected the results both in terms of descriptive statistical and spatial dependence parameters. Crop yields changed (soybean decrease and maize increase) with time of no-tillage but the real cause was not identified. The length of time with no-tillage affected the range of dependence for the main crops (increased for soybean, maize and oats) and therefore increased the size of the homogeneous management zones. The evolution of the sampling grid from 20 m with 63 sampling points to 10 m with 302 sampling points allowed for a much better knowledge of the spatial variability of crop yields but it had the reverse effect on the spatial variability of soil physical properties.
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Christias, Panagiotis, Ioannis N. Daliakopoulos, Thrassyvoulos Manios, and Mariana Mocanu. "Comparison of Three Computational Approaches for Tree Crop Irrigation Decision Support." Mathematics 8, no. 5 (May 3, 2020): 717. http://dx.doi.org/10.3390/math8050717.
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This paper explores methodologies for developing intelligent automated decision systems for complex processes that contain uncertainties, thus requiring computational intelligence. Irrigation decision support systems (IDSS) promise to increase water efficiency while sustaining crop yields. Here, we explored methodologies for developing intelligent IDSS that exploit statistical, measured, and simulated data. A simple and a fuzzy multicriteria approach as well as a Decision Tree based system were analyzed. The methodologies were applied in a sample of olive tree farms of Heraklion in the island of Crete, Greece, where water resources are scarce and crop management is generally empirical. The objective is to support decision for optimal financial profit through high yield while conserving water resources through optimal irrigation schemes under various (or uncertain) intrinsic and extrinsic conditions. Crop irrigation requirements are modelled using the FAO-56 equation. The results demonstrate that the decision support based on probabilistic and fuzzy approaches point to strategies with low amounts and careful distributed water irrigation strategies. The decision tree shows that decision can be optimized by examining coexisting factors. We conclude that irrigation-based decisions can be highly assisted by methods such as decision trees given the right choice of attributes while keeping focus on the financial balance between cost and revenue.
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Meng, Linghua, Huanjun Liu, Susan L. Ustin, and Xinle Zhang. "Predicting Maize Yield at the Plot Scale of Different Fertilizer Systems by Multi-Source Data and Machine Learning Methods." Remote Sensing 13, no. 18 (September 19, 2021): 3760. http://dx.doi.org/10.3390/rs13183760.
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Timely and reliable maize yield prediction is essential for the agricultural supply chain and food security. Previous studies using either climate or satellite data or both to build empirical or statistical models have prevailed for decades. However, to what extent climate and satellite data can improve yield prediction is still unknown. In addition, fertilizer information may also improve crop yield prediction, especially in regions with different fertilizer systems, such as cover crop, mineral fertilizer, or compost. Machine learning (ML) has been widely and successfully applied in crop yield prediction. Here, we attempted to predict maize yield from 1994 to 2007 at the plot scale by integrating multi-source data, including monthly climate data, satellite data (i.e., vegetation indices (VIs)), fertilizer data, and soil data to explore the accuracy of different inputs to yield prediction. The results show that incorporating all of the datasets using random forests (RF) and AB (adaptive boosting) can achieve better performances in yield prediction (R2: 0.85~0.98). In addition, the combination of VIs, climate data, and soil data (VCS) can predict maize yield more effectively than other combinations (e.g., combinations of all data and combinations of VIs and soil data). Furthermore, we also found that including different fertilizer systems had different prediction accuracies. This paper aggregates data from multiple sources and distinguishes the effects of different fertilization scenarios on crop yield predictions. In addition, the effects of different data on crop yield were analyzed in this study. Our study provides a paradigm that can be used to improve yield predictions for other crops and is an important effort that combines multi-source remotely sensed and environmental data for maize yield prediction at the plot scale and develops timely and robust methods for maize yield prediction grown under different fertilizing systems.