scholarly journals Development of the methodology of estimating of agricultural crop yield potential with consideration of climate and agrophytotechnology impact

2017 ◽  
pp. 52-60
Author(s):  
V. P. Dmytrenko ◽  
L. P. Odnolyetok ◽  
О. О. Kryvoshein ◽  
A. V. Krukivska
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Crop Yields ◽  
Wheat Yield ◽  
Yield Potential ◽  
Agricultural Crop ◽  
Yield Level ◽  
Winter Wheat Yield ◽  
The Mathematical Model

In the paper it is outlined the main methodological positions and the results of the approbation of new approaches to the integrated assessment of the potential of crop yields. There are considered the theoretical foundations of a joint assessment of the biological, ecological and anthropogenic components of the yield potential of agricultural crops which are based on the ecosystem concept and the mathematical model "Weather-Crop Yield" developed by V. P. Dmytrenko. In the considered approaches the peculiarities of the influence of various environmental factors on the formation of crop yields are determined by indicators of various potential yields -  general, climatic and trend (agrotechnological). Each type of yield potential can be used for evaluation of the effectiveness of the conditions of field crop growing for each factor taken into account, as well as the optimality criterion in the agrometeorological adaptation strategies and also as a criterion for the degree of sensitivity of the yield level to the conditions of crops cultivating. The developed approaches are tested on the example of estimation of long-term dynamics of winter wheat yield potential in Ukraine. According to the results of the evaluation of different factors of the potential of the productivity of winter wheat for the periods 1961-1990 and 1991-2010 the dominant importance of organizational and technological processes in comparison with the contribution of changes of agroclimatic conditions has been determined in both periods.

scholarly journals Crop Yield and Soil Fertility Status of Long-Term Rice-Rice-Wheat Cropping Systems

2017 ◽  
Vol 5 (1) ◽  
pp. 42-50
Author(s):  
Nabin Rawal ◽  
Rajan Ghimire ◽  
Devraj Chalise
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Management Practices ◽  
Crop Yields ◽  
Oryza Sativa L ◽  
Farmyard Manure ◽  
Wheat Yield ◽  
Nutrient Supply ◽  
Available Phosphorus

Balanced nutrient supply is important for the sustainable crop production. We evaluated the effects of nutrient management practices on soil properties and crop yields in rice (Oryza sativa L.) - rice - wheat (Triticum aestivum L.) system in a long-term experiment established at National Wheat Research Program (NWRP), Bhairahawa, Nepal. The experiment was designed as a randomized complete block experiment with nine treatments and three replications. Treatments were applied as: T1- no nutrients added, T2- N added; T3- N and P added; T4- N and K added; T5- NPK added at recommended rate for all crops. Similarly, T6- only N added in rice and NPK in wheat at recommended rate; T7- half N; T8- half NP of recommended rate for both crops; and T9- farmyard manure (FYM) @10 Mg ha-1 for all crops in rotation. Results of the study revealed that rice and wheat yields were significantly greater under FYM than all other treatments. Treatments that did not receive P (T2, T3, T7, T8) and K (T2, T4) had considerably low wheat yield than treatments that received NPK (T5) and FYM (T9). The FYM lowered soil pH and improved soil organic matter (SOM), total nitrogen (TN), available phosphorus (P), and exchangeable potassium (K) contents than other treatments. Management practices that ensure nutrient supply can increase crop yield and improve soil fertility status.Int. J. Appl. Sci. Biotechnol. Vol 5(1): 42-50

scholarly journals An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images

2019 ◽  
Vol 11 (9) ◽  
pp. 1088 ◽  
Author(s):  
Yulong Wang ◽  
Xingang Xu ◽  
Linsheng Huang ◽  
Guijun Yang ◽  
Lingling Fan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Winter Wheat ◽  
Crop Yield ◽  
Satellite Remote Sensing ◽  
Wheat Yield ◽  
Growing Season ◽  
Remote Sensing Images ◽  
Casa Model ◽  
Winter Wheat Yield

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

ECONOMIC AND PHYSICAL MODELING OF LAND USE IN GCAM 3.0 AND AN APPLICATION TO AGRICULTURAL PRODUCTIVITY, LAND, AND TERRESTRIAL CARBON

2014 ◽  
Vol 05 (02) ◽  
pp. 1450003 ◽  
Author(s):  
MARSHALL WISE ◽  
KATE CALVIN ◽  
PAGE KYLE ◽  
PATRICK LUCKOW ◽  
JAE EDMONDS
Keyword(s):  
Land Use ◽  
Crop Yield ◽  
Carbon Emissions ◽  
Crop Yields ◽  
Assessment Model ◽  
Agricultural Crop ◽  
Terrestrial Carbon ◽  
Global Change Assessment Model ◽  
The Impact

The release of the Global Change Assessment Model (GCAM) version 3.0 represents a major revision in the treatment of agriculture and land-use activities in a model of long-term, global human and physical Earth systems. GCAM 3.0 incorporates greater spatial and temporal resolution compared to GCAM 2.0. In this paper, we document the methods embodied in the new release, describe the motivation for the changes, compare GCAM 3.0 methods to those of other long-term, global agriculture-economy models and apply GCAM 3.0 to explore the impact of changes in agricultural crop yields on global land use and terrestrial carbon. In the absence of continued crop yield improvements throughout the century, not only are cumulative carbon emissions a major source of CO 2 emissions to the atmosphere, but bioenergy production remains trivial — land is needed for food. In contrast, the high crop yield improvement scenario cuts terrestrial carbon emissions dramatically and facilitates both food and energy production.

scholarly journals Winter Wheat Yield Prediction Using Convolutional Neural Networks from Environmental and Phenological Data

2021 ◽  
Author(s):  
Amit Kumar Srivast ◽  
Nima Safaei ◽  
Saeed Khaki ◽  
Gina Lopez ◽  
Wenzhi Zeng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Learning ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Yield Prediction ◽  
Learning Models ◽  
Winter Wheat Yield ◽  
Machine Learning Models

Abstract Crop yield forecasting depends on many interactive factors including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using extensive datasets of weather, soil, and crop phenology. We propose a convolutional neural network (CNN) which uses the 1-dimentional convolution operation to capture the time dependencies of environmental variables. The proposed CNN, evaluated along with other machine learning models for winter wheat yield prediction in Germany, outperformed all other models tested. To address the seasonality, weekly features were used that explicitly take soil moisture and meteorological events into account. Our results indicated that nonlinear models such as deep learning models and XGboost are more effective in finding the functional relationship between the crop yield and input data compared to linear models and deep neural networks had a higher prediction accuracy than XGboost. One of the main limitations of machine learning models is their black box property. Therefore, we moved beyond prediction and performed feature selection, as it provides key results towards explaining yield prediction (variable importance by time). As such, our study indicates which variables have the most significant effect on winter wheat yield.

scholarly journals Operating at the extreme: estimating the upper yield boundary of winter wheat production in commercial practice

2020 ◽  
Vol 7 (4) ◽  
pp. 191919
Author(s):  
Emily G. Mitchell ◽  
Neil M. J. Crout ◽  
Paul Wilson ◽  
Andrew T. A. Wood ◽  
Gilles Stupfler
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Agronomic Traits ◽  
Maximum Yield ◽  
Crop Protection ◽  
Wheat Yield ◽  
Statistical Evidence ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Winter Wheat Yield

Wheat farming provides 28.5% of global cereal production. After steady growth in average crop yield from 1950 to 1990, wheat yields have generally stagnated, which prompts the question of whether further improvements are possible. Statistical studies of agronomic parameters such as crop yield have so far exclusively focused on estimating parameters describing the whole of the data, rather than the highest yields specifically. These indicators include the mean or median yield of a crop, or finding the combinations of agronomic traits that are correlated with increasing average yields. In this paper, we take an alternative approach and consider high yields only. We carry out an extreme value analysis of winter wheat yield data collected in England and Wales between 2006 and 2015. This analysis suggests that, under current climate and growing conditions, there is indeed a finite upper bound for winter wheat yield, whose value we estimate to be 17.60 tonnes per hectare. We then refine the analysis for strata defined by either location or level of use of agricultural inputs. We find that there is no statistical evidence for variation of maximal yield depending on location, and neither is there statistical evidence that maximum yield levels are improved by high levels of crop protection and fertilizer use.

Increasing Hard Winter Wheat Yield Potential via Synthetic Wheat: I. Path-Coefficient Analysis of Yield and Its Components

Crop Science ◽  
2012 ◽  
Vol 52 (5) ◽  
pp. 2014-2022 ◽  
Author(s):  
Jessica K. Cooper ◽  
A.M.H. Ibrahim ◽  
J. Rudd ◽  
Subas Malla ◽  
Dirk B. Hays ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
Yield Potential ◽  
Path Coefficient ◽  
Path Coefficient Analysis ◽  
Synthetic Wheat ◽  
Winter Wheat Yield

scholarly journals Estimating Farm Wheat Yields from NDVI and Meteorological Data

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 946
Author(s):  
Astrid Vannoppen ◽  
Anne Gobin
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Meteorological Data ◽  
Wheat Yield ◽  
Model Performance ◽  
Weather Conditions ◽  
Current Data ◽  
Monthly Precipitation ◽  
Empirical Modelling ◽  
Winter Wheat Yield

Information on crop yield at scales ranging from the field to the global level is imperative for farmers and decision makers. The current data sources to monitor crop yield, such as regional agriculture statistics, are often lacking in spatial and temporal resolution. Remotely sensed vegetation indices (VIs) such as NDVI are able to assess crop yield using empirical modelling strategies. Empirical NDVI-based crop yield models were evaluated by comparing the model performance with similar models used in different regions. The integral NDVI and the peak NDVI were weak predictors of winter wheat yield in northern Belgium. Winter wheat (Triticum aestivum) yield variability was better predicted by monthly precipitation during tillering and anthesis than by NDVI-derived yield proxies in the period from 2016 to 2018 (R2 = 0.66). The NDVI series were not sensitive enough to yield affecting weather conditions during important phenological stages such as tillering and anthesis and were weak predictors in empirical crop yield models. In conclusion, winter wheat yield modelling using NDVI-derived yield proxies as predictor variables is dependent on the environment.

Effects of long-term fertilization on the yield of winter wheat and N forms on Luvic Phaeosem soil determined in 0.01 M CaCl 2

2010 ◽  
Vol 59 (1) ◽  
pp. 135-144 ◽  
Author(s):  
E. Bertáné Szabó ◽  
J. Loch ◽  
Gy. Zsigrai ◽  
L. Blaskó
Keyword(s):  
Winter Wheat ◽  
Wheat Yield ◽  
N Fertilization ◽  
N Balance ◽  
P Fertilization ◽  
Yield Data ◽  
Yield Increase ◽  
Winter Wheat Yield ◽  
Fertilization Practice

The effects of regular NPK fertilization on the amounts of winter wheat yield and the amounts and proportion of different N forms (NO 3 -N, NH 4 -N, N org , N total ) of a Luvic Phaeosem soil determined in 0.01 M CaCl 2 were studied in the B1740 variant of the National Long-Term Fertilization Experiment at Karcag. According to the yield data, N and P fertilization increased winter wheat yield significantly. When applying the 200 kg N·ha -1 dose, P fertilization resulted in a more than 2 t·ha -1 yield increase, as compared to the treatments without P fertilization. K fertilization had no effect on the yield, similarly to preceding years. These findings may be adapted to fields of the Middle-Tisza Region with similar conditions to the trial site. The N forms of the soil determined in CaCl 2 reflected fertilization well. All of the fractions, but especially NO 3 -N and N total , increased significantly in response to N fertilization. Close relationships (r = 0.87–0.88) were found among the NO 3 -N and N total fractions and the N balance, which means that the amounts of NO 3 -N and N total are suitable for assessing both the N deficit and the N surplus. The strength of the correlation between the NH 4 -N content and N balance was moderate (r = 0.65). The N org fraction increased significantly as a function of N and P fertilization. These results can be explained with the yield increase. A significant correlation (r = 0.55) was found between the N org fraction and yield amounts. It can be established that organic residuals remaining on the site resulted in a significant increase in the N org content of soils. The gained results confirm that the N org fraction is suitable for the characterization of the readily mobilizable N reserves previously ignored in fertilization practice. On the basis of the presented results the CaCl 2 method is recommended for the precise estimation of nutrient requirements.

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