scholarly journals Using UAV Collected RGB and Multispectral Images to Evaluate Winter Wheat Performance across a Site Characterized by Century-Old Biochar Patches in Belgium

2020 ◽  
Vol 12 (15) ◽  
pp. 2504 ◽  
Author(s):  
Ramin Heidarian Dehkordi ◽  
Victor Burgeon ◽  
Julien Fouche ◽  
Edmundo Placencia Gomez ◽  
Jean-Thomas Cornelis ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Positive Impact ◽  
Crop Growth ◽  
Wheat Crop ◽  
P Value ◽  
Growth Stages ◽  
Crop Performance ◽  
Crop Health

Remote sensing data play a crucial role in monitoring crop dynamics in the context of precision agriculture by characterizing the spatial and temporal variability of crop traits. At present there is special interest in assessing the long-term impacts of biochar in agro-ecosystems. Despite the growing body of literature on monitoring the potential biochar effects on harvested crop yield and aboveground productivity, studies focusing on the detailed crop performance as a consequence of long-term biochar enrichment are still lacking. The primary objective of this research was to evaluate crop performance based on high-resolution unmanned aerial vehicle (UAV) imagery considering both crop growth and health through RGB and multispectral analysis, respectively. More specifically, this approach allowed monitoring of century-old biochar impacts on winter wheat crop performance. Seven Red-Green-Blue (RGB) and six multispectral flights were executed over 11 century-old biochar patches of a cultivated field. UAV-based RGB imagery exhibited a significant positive impact of century-old biochar on the evolution of winter wheat canopy cover (p-value = 0.00007). Multispectral optimized soil adjusted vegetation index indicated a better crop development over the century-old biochar plots at the beginning of the season (p-values < 0.01), while there was no impact towards the end of the season. Plant height, derived from the RGB imagery, was slightly higher for century-old biochar plots. Crop health maps were computed based on principal component analysis and k-means clustering. To our knowledge, this is the first attempt to quantify century-old biochar effects on crop performance during the entire growing period using remotely sensed data. Ground-based measurements illustrated a significant positive impact of century-old biochar on crop growth stages (p-value of 0.01265), whereas the harvested crop yield was not affected. Multispectral simplified canopy chlorophyll content index and normalized difference red edge index were found to be good linear estimators of harvested crop yield (p-value(Kendall) of 0.001 and 0.0008, respectively). The present research highlights that other factors (e.g., inherent pedological variations) are of higher importance than the presence of century-old biochar in determining crop health and yield variability.

scholarly journals Effects of disk tillage on soil condition, crop yield and weed infestation

2011 ◽  
Vol 48 (No. 1) ◽  
pp. 20-26
Author(s):  
M. Birkás ◽  
T. Szalai ◽  
C. Gyuricza ◽  
M. Gecse ◽  
K. Bordás
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Field Experiments ◽  
Soil Layer ◽  
Soil Condition ◽  
Perennial Weeds ◽  
Weed Infestation ◽  
Set Up ◽  
Different Levels

This research was instigated by the fact that during the last decade annually repeated shallow disk tillage on the same field became frequent practice in Hungary. In order to study the changes of soil condition associated with disk tillage and to assess it is consequences, long-term tillage field experiments with different levels of nutrients were set up in 1991 (A) and in 1994 (B) on Chromic Luvisol at G&ouml;d&ouml;ll&ouml;. The effects of disk tillage (D) and disk tillage combined with loosening (LD) on soil condition, on yield of maize and winter wheat, and on weed infestation were examined. The evaluation of soil condition measured by cone index and bulk density indicated that use of disking annually resulted in a dense soil layer below the disking depth (diskpan-compaction). It was found, that soil condition deteriorated by diskpan-compaction decreased the yield of maize significantly by 20 and 42% (w/w), and that of wheat by 13 and 15% (w/w) when compared to soils with no diskpan-compaction. Averaged over seven years, and three fertilizer levels, the cover % of the total, grass and perennial weeds on loosened soils were 73, 69 and 65% of soils contained diskpan-compaction.

scholarly journals Phosphorus and Zinc Fertilization Influence Crop Growth Rates and Total Biomass of Coarse vs. Fine Types Rice Cultivars

Agronomy ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 1356
Author(s):  
Amanullah ◽  
Inamullah ◽  
Jawaher Alkahtani ◽  
Mohamed Soliman Elshikh ◽  
Mona S. Alwahibi ◽  
...  
Keyword(s):  
Growth Rates ◽  
Positive Impact ◽  
Oryza Sativa L ◽  
Crop Growth ◽  
Growth Stages ◽  
Total Biomass ◽  
Continuous Cropping ◽  
Rice Cultivars ◽  
Rice Genotypes ◽  
The Impact

Under the rice–wheat cropping system (RWS), the continuous cropping of rice (Oryza sativa L.) and wheat (Triticum aestivum L.) deplete soil fertility, and reduce crop growth and total rice biomass. In RWS, both phosphorus (P) and zinc (Zn) deficiencies are considered important nutritional constraints for reducing rice crop growth rates (CGR) and total biomass/biological yield (BY). The objective of this experiment was to investigate the impact of phosphorus (0, 40, 80, 120 kg P ha−1) and zinc rates (0, 5, 10, 15 kg Zn ha−1) on CGR and BY of three rice genotypes [fine (Bamati-385) versus coarse (Fakhre-e-Malakand and Pukhraj)] in Northwestern Pakistan during summer 2011 (Y1) and 2012 (Y2). The results revealed that higher CGR at various growth stages and total BY was obtained with the integrated use of higher phosphorus (80 and 120 kg P ha−1) and zinc rates (10 and 15 kg Zn ha−1). The lower CGR and BY were recorded when P and Zn were not applied (control) or when P and Zn were applied alone. In the case of rice genotypes, the highest CGR and BY were recorded for the hybrid rice (Pukhraj) than the other two genotypes. The CGR was increased to the highest level at the heading stage as compared to tillering and physiological maturity. The increase in CGR had a positive impact on the total BY of rice cultivars. The increase in BY had a positive relationship with grain yield and grower’s income. It was concluded from the study that the combined application of higher P and Zn rates to the coarse rice genotypes (Fakhre-e-Malakand and Pukhraj) could increase CGR, total BY, crop productivity and profitability.

Irrigation of field peas (Pisum sativum): response to timing at different crop growth stages

1999 ◽  
Vol 132 (4) ◽  
pp. 417-424 ◽  
Author(s):  
C. M. KNOTT
Keyword(s):  
Pisum Sativum ◽  
Vegetative Growth ◽  
Growth Stage ◽  
Crop Growth ◽  
Yield Response ◽  
Growth Stages ◽  
Field Peas ◽  
Crop Growth Stages ◽  
Different Growth Stages

The response of two cultivars of dry harvest field peas (Pisum sativum), Solara and Bohatyr, to irrigation at different growth stages was studied on light soils overlying sand in Nottinghamshire, England in 1990, when the spring was particularly dry, in 1991 which had a dry spring and summer and in contrast, 1992, when rainfall was greater compared with the long-term (40 year) mean.Solara, short haulmed and semi-leafless was more sensitive to drought than the tall conventional-leaved cultivar Bohatyr and gave a greater yield response to irrigation, particularly at the vegetative growth stage in the first two dry years 1990 and 1991, of 108% and 55% respectively, compared with unirrigated plots. Bohatyr was less sensitive to the timing of single applications.In all years, peas irrigated throughout on several occasions produced the highest yields, but this was the least efficient use of water.

Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data

2017 ◽  
Vol 33 (9) ◽  
pp. 942-956 ◽  
Author(s):  
P. Kumar ◽  
R. Prasad ◽  
D. K. Gupta ◽  
V. N. Mishra ◽  
A. K. Vishwakarma ◽  
...  
Keyword(s):  
Time Series ◽  
Winter Wheat ◽  
Growth Parameters ◽  
Crop Growth ◽  
Wheat Crop ◽  
Winter Wheat Crop ◽  
Sar Data

scholarly journals Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3161 ◽  
Author(s):  
Haizhu Pan ◽  
Zhongxin Chen ◽  
Allard de Wit ◽  
Jianqiang Ren
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Crop Growth ◽  
Open Loop ◽  
Yield Estimation ◽  
Spatiotemporal Resolution ◽  
Sentinel 2

It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.

Crop yield and N2O emission affected by long-term organic manure substitution fertilizer under winter wheat-summer maize cropping system

2020 ◽  
Vol 732 ◽  
pp. 139321
Author(s):  
Fenglian Lv ◽  
Jiashan Song ◽  
Donna Giltrap ◽  
Yongtao Feng ◽  
Xueyun Yang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Crop Yield ◽  
Cropping System ◽  
N2o Emission ◽  
Organic Manure ◽  
Summer Maize

WINTER WHEAT CROP FORMATION, DEPENDING ON THE FERTILIZER SYSTEM WHILE MINIMIZING BASIC TILLAGE

10.12737/3823 ◽  
2014 ◽  
Vol 9 (1) ◽  
pp. 117-121
Author(s):  
Шаронова ◽  
Natalya Sharonova ◽  
Яппаров ◽  
Akhtam Yapparov ◽  
Ильясов ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Positive Impact ◽  
Wheat Yield ◽  
Field Research ◽  
Mineral Fertilizer ◽  
Wheat Crop ◽  
Positive Effects ◽  
Leached Chernozem ◽  
Fertilizer System

The article presents data of field research of fertilizers systems and tillage on heavy leached chernozem at planting winter wheat in the Republic of Tatarstan. The paper shows the positive effects of organomineral fertilizer system on crop growth and quality of winter wheat, compared with mineral fertilizer system. The improvement of water and soil nutrient status was revealed. The layered and chisel tillage systems were the most effective methods. The study showed, that the use of organomineral fertilizer system had a stronger positive impact on the yield and quality of winter wheat, compared with mineral fertilizer system. The most winter wheat yield was obtained by applying the organomineral fertilizer system at layered plowing - 4.49 tons per hectare (the increase relative to the control is 0.64 tons per hectare). The best indicators of water and food regime of leached chernozem also marked at using organomineral fertilizer system, especially in layered tillage .

scholarly journals Trends of weather-related impacts on Austrian crop production under changing climate

2021 ◽  
Author(s):  
Sabina Thaler ◽  
Josef Eitzinger ◽  
Gerhard Kubu
Keyword(s):  
Climate Change ◽  
Winter Wheat ◽  
Crop Production ◽  
Crop Yields ◽  
Weather Conditions ◽  
Crop Growth ◽  
Risk Indicators ◽  
Climate Scenarios ◽  
Crop Water

&lt;p&gt;Weather-related risks can affect crop growth and yield potentials directly (e.g. heat, frost, drought) and indirectly (e.g. through biotic factors such as pests). Due to climate change, severe shifts of cropping risks may occur, where farmers need to adapt effectively and in time to increase the resilience of existing cropping systems. For example, since the early 21st century, Europe has experienced a series of exceptionally dry and warmer than usual weather conditions (2003, 2012, 2013, 2015, 2018) which led to severe droughts with devastating impacts in agriculture on crop yields and pasture productivity.&lt;/p&gt;&lt;p&gt;Austria has experienced above-average warming in the period since 1880. While the global average surface temperature has increased by almost 1&amp;#176;C, the warming in Austria during this period was nearly 2&amp;#176;C. Higher temperatures, changing precipitation patterns and more severe and frequent extreme weather events will significantly affect weather-sensitive sectors, especially agriculture. Therefore, the development of sound adaptation and mitigation strategies towards a &quot;climate-intelligent agriculture&quot; is crucial to improve the resilience of agricultural systems to climate change and increased climate variability. Within the project AGROFORECAST a set of weather-related risk indicators and tailored recommendations for optimizing crop management options are developed and tested for various forecast or prediction lead times (short term management: 10 days - 6 months; long term strategic planning: climate scenarios) to better inform farmers of upcoming weather and climate challenges.&lt;/p&gt;&lt;p&gt;Here we present trends of various types of long-term weather-related impacts on Austrian crop production under past (1980-2020) and future periods (2035-2065). For that purpose, agro-climatic risk indicators and crop production indicators are determined in selected case study regions with the help of models. We use for the past period Austrian gridded weather data set (INCA) as well as different regionalized climate scenarios of the Austrian Climate Change Projections &amp;#214;KS15. The calculation of the agro-climatic indicators is carried out by the existing AGRICLIM model and the GIS-based ARIS software, which was developed for estimating the impact of adverse weather conditions on crops. The crop growth model AQUACROP is used for analysing soil-crop water balance parameters, crop yields and future crop water demand.&lt;/p&gt;&lt;p&gt;Depending on the climatic region, a more or less clear shift in the various agro-climatic indices can be expected towards 2050, e.g. the number of &quot;heat-stress-days&quot; for winter wheat increases significantly in eastern Austria. Furthermore, a decreasing trend in maize yield is simulated, whereas a mean increase in yield of spring barley and winter wheat can be expected under selected scenarios. Other agro-climatic risk indicators analysed include pest algorithms, risks from frost occurrence, overwintering conditions, climatic crop growing conditions, field workability and others, which can add additional impacts on crop yield variability, not considered by crop models.&lt;/p&gt;

scholarly journals VARIABILITY OF RICE YIELD WITH RESPECT TO CROP HEALTH

2016 ◽  
Vol 78 (1-2) ◽  
Author(s):  
Renny Eka Putri ◽  
Azmi Yahya ◽  
Nor Maria Adam ◽  
Samsuzana Abd Aziz
Keyword(s):  
Chlorophyll Content ◽  
Crop Yield ◽  
Rice Yield ◽  
Economic Status ◽  
Grid Point ◽  
Growth Stages ◽  
Spad Value ◽  
Cutting Test ◽  
Available Technology ◽  
Crop Health

Chlorophyll content of leaf can be used as an indicator of the crop health. The SPAD chlorophyll meter has been acceptably used for rapid analysis of chlorophyll content and nitrogen status of crops while it has not been established how strongly the SPAD values are correlated with rice yield within a plot. This study was to explore the relationship between rice yields and the leaf SPAD value of the associated rice plots. Twenty sampling points of rice leaves plant were taken at three difference growing stages based on grid point sampling of 30m x 18m for two crop seasons. Two methods, namely instantaneous yield from on-board yield monitoring system mounted on a combine harvester and estimated crop yield from cutting test (CCT) yield were used to measure the variability of harvested rice yield within the rice plot. The SPAD values were found positively correlated with grain yield at different growth stages.  The highest significant correlation was at crop age 70 days after planting with Pearson’s correlations (r) ranging 0.7280 to 0.8336 (P<0.001). Consequently, information with regards to SPAD value variability could triggers farmers in taking immediate in situ action for improving the crop yield while information with regards to crop yield variability could assist farmers in planning the proper farming practice for the subsequent cropping seasons. Generally, this available technology would assist farmers in improving their crop yield and their economic status.

scholarly journals Determination of Wheat Growth, Crop Coefficient (KC) and Water Stress Coefficient (Ks) under Different Salinity

2016 ◽  
Author(s):  
Meysam Abedinpour
Keyword(s):  
Water Stress ◽  
Crop Coefficient ◽  
Crop Growth ◽  
Growth Patterns ◽  
Wheat Crop ◽  
Growth Stages ◽  
Stress Coefficient ◽  
Salinity Levels ◽  
Water Stress Coefficient

A field experiment was conducted for determination of crop coefficient (KC) and water stress coefficient (Ks) for wheat crop under different salinity levels, during 2015-16. Complete randomized block design of five treatments were considered, i.e., 0.51 dS/m (fresh water) as a control treatment and other four saline water treatments (4, 6, 8 and 10 dS/m), for S1, S2, S3 and S4 with three replications. The results revealed that the water consumed by plants during the different crop growth stages follows the order of FW&gt;S1&gt;S2&gt;S3&gt;S4 salinity levels. According to the obtained results, the calculated values of crop coefficients significantly differed from those suggested by FAO No.56 for the crops. The Ks values clearly differ from one stage to another because the salt stress causes both osmotic stress, due to a decrease in the soil water potential, and ionic stress which the average values of water stress coefficient (Ks) follows this order; FW(1.0)=S1(1.0)&gt;S2(1.0)&gt;S3(0.93)&gt;S4(0.82). Overall, it was found the differences are attributed primarily to specific cultivar, the changes in local climatic conditions and seasonal differences in crop growth patterns. Thus, further studies are essential to determine the crop coefficient values under different variables, to make the best management practice (BMP) in agriculture.

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