scholarly journals THE INFLUENCE OF FERTILIZATION AND RAINFALL ON WHEAT PRODUCTION

1970 ◽  
Vol 62 ◽  
Author(s):  
C. Naidin
Keyword(s):  
Plant Development ◽  
Vegetation Period ◽  
N Fertilization ◽  
Wheat Crop ◽  
Growth Stages ◽  
P Fertilization ◽  
Wheat Production ◽  
Previous Crop ◽  
Rainfall Deficit ◽  
Higher Doses

In this paper, we analyze the influence of N and P fertilization on wheat yields, taking into account the previous crop and the level of rainfall accumulated until the end of the growth stages in plant development. In the wheat crops developed on the reddish-brown low luvi soil found at ARDS Simnic, the N fertilization has favorable effects in moderate doses (60 - 100 kg N/ha after corn and 60 - 90 kg N/ha after peas), while in higher doses (120 - 160 kg N/ha) fertilization determines a fall in production, both in the case of rainfall deficit and excess. The P has favorable effects on wheat crops, especially when the previous crop is peas. The rainfall quantity, as well as its repartition along the vegetation period, influences the wheat crop, causing great variations from year to year. The obtained data shows that rainfall deficit as well as rainfall excess determines a drop in wheat production; relatively high and stable average productions can be obtained in the case of rainfall levels close to the multi annual averages in different plant development stages.

scholarly journals Wheat Yield Estimation with NDVI Values Using a Proximal Sensing Tool

2020 ◽  
Vol 12 (17) ◽  
pp. 2749
Author(s):  
Marta Aranguren ◽  
Ander Castellón ◽  
Ana Aizpurua
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Yields ◽  
Wheat Yield ◽  
Growing Season ◽  
N Fertilization ◽  
Wheat Crop ◽  
Growth Stages ◽  
Climate Conditions ◽  
N Status

Nitrogen (N) splitting is critical to achieving high crop yields without having negative effects on the environment. Monitoring crop N status throughout the wheat growing season is key to finding the balance between crop N requirements and fertilizer needs. Three soft winter wheat fertilization trials under rainfed conditions in Mediterranean climate conditions were monitored with a RapidScan CS-45 (Holland Scientific, Lincoln, NE, USA) instrument to determine the normalized difference vegetation index (NDVI) values at the GS30, GS32, GS37, and GS65 growth stages. The threshold NDVI values in the Cezanne variety were 0.7–0.75 at the GS32, GS37, and GS65 growing stages. However, for the GS30 growing stage, a threshold value could not be established precisely. At this stage, N deficiency may not affect wheat yield, as long as the N status increases at GS32 stage and it is maintained thereafter. Following the NDVI dynamic throughout the growing season could help to predict the yields at harvest time. Therefore, the ΣNDVI from GS30 to GS65 explains about 80% of wheat yield variability. Therefore, a given yield could be achieved with different dynamics in wheat NDVI values throughout the growing cycle. The determined ranges of the NDVI values might be used for developing new fertilization strategies that are able to adjust N fertilization to wheat crop needs.

scholarly journals Predict Growth Stages of Wheat Crop using Digital Image Processing

2020 ◽  
Vol 8 (5) ◽  
pp. 3026-3035
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing Technique ◽  
Mean Value ◽  
Image Processing Technique ◽  
Wheat Crop ◽  
Growth Stages ◽  
Increase Productivity ◽  
Crop Growth Stages

Manual examination is not as accurate to examine crop growing stages because of the possibility of the human mistake and errors. While machine examination or automatic examination can easily examine crop growing stages and increase productivity because it provides fast and accurate examine result. This study provide a solution to finding the wheat crop growth stages, Once the growing stages are established, farmers can take suitable and measured steps to improve the production of wheat or other agricultural crops. For finding the growth stages of wheat digital image processing technique is used. RGB model, HSI model, mean value of green colour, hue and saturation images use for examining wheat crop.

scholarly journals Trends and Decomposition of Wheat Production in Western Maharashtra

2020 ◽  
pp. 69-78
Author(s):  
P. P. Baviskar ◽  
U. T. Dangore ◽  
A. D. Dhunde ◽  
U. P. Gaware ◽  
A. G. Kadu
Keyword(s):  
Decomposition Analysis ◽  
Parametric Models ◽  
Production Performance ◽  
Wheat Crop ◽  
Secondary Source ◽  
Research Activity ◽  
Wheat Production ◽  
Area Effect ◽  
Relative Contribution ◽  
Almost All

The study was aimed to investigate the production performance of wheat in western Maharashtra. The data of 20 years regarding area, production and productivity of wheat was made available through the secondary source for all the districts of the western Maharashtra region. The study period of 1996-97 to 2015-16 was split into two sub periods i.e. period-I (1996-97 to 2005-2006), period-II (2006-07 to 2015-16) and overall period. The growth rates were calculated using the exponential function. The instability in area, production, and productivity was measured with a coefficient of variation (CV) and Cuddy Della Valle’s Instability index. The relative contribution of area and yield to change in output was estimated by Minhas decomposition model. The district-wise analysis was carried out which resulted that, during the period-I and period-II, almost all districts in the western Maharashtra region registered negative growth including the region as a whole. The area and productivity showed stability in wheat crop in almost all the districts of western Maharashtra region. In the western Maharashtra region, among all the parametric models fitted to the area, production and productivity of wheat crop, the maximum R2 was observed in the case of cubic model in all the districts of Western Maharashtra region with the region as a whole. The region as a whole recorded 59 per cent which marked as the highest R2 in productivity as compared to area and production. The decomposition analysis for western Maharashtra region depicted the largest area effect on wheat production. It was also observed that for both periods the area effect was more pronounced than the yield effect and interaction effect. Hence, there is need for policy maker to formulate development-oriented policies and the researchers to design an investigative research activity for promoting a sustainable wheat production system in the region for expansion of area under wheat cultivation.

scholarly journals Nitrogen Availability and Use Efficiency in Wheat Crop as Influenced by the Organic-Input Quality Under Major Integrated Nutrient Management Systems

2021 ◽  
Vol 12 ◽  
Author(s):  
Ajay K. Bhardwaj ◽  
Deepika Rajwar ◽  
Rajender K. Yadav ◽  
Suresh K. Chaudhari ◽  
Dinesh K. Sharma
Keyword(s):  
Nutrient Management ◽  
Farmyard Manure ◽  
Wheat Crop ◽  
Growth Stages ◽  
N Availability ◽  
Integrated Nutrient Management ◽  
Inorganic Fertilizers ◽  
Available N ◽  
Stubble Retention ◽  
Use Efficiency

PurposeOne of the serious constraints for the integration of organics in soil fertility plans is the release and availability of nitrogen (N) to match the critical growth stages of a crop. The interplay between organic amendment characteristics and soil moisture conditions can significantly affect the nutrient release and availability, especially for dryland crops like wheat. In this study, the effects of integrated nutrient management strategies using diverse qualities of organic amendments on daily N mineralization and its availability to plants during the full growing season of the wheat crop were analyzed in a 10-year experiment.MethodsThe management included (1) F, inorganic fertilizers at 100% rate, compared to a reduced rate of inorganic fertilizers (55% N) supplemented with organic inputs via (2) GM, green manuring, (3) LE, legume cropping and its biomass recycling, (4) WS, wheat stubble retention, (5) RS, rice stubble retention, and (6) FYM, farmyard manure application, during the preceding rice season. Ion exchange resin (IER) membrane strips were used as plant root simulators to determine daily NH4+-N and NO3–-N availability in soil solution during the full wheat growing period.ResultsTotal available N for the full season was in the following order: GM (962 μg cm–2) > F (878 μg cm–2) > LE (872 μg cm–2) > FYM (865 μg cm–2) > RS (687 μg cm–2) > WS (649 μg cm–2). No significant differences were observed in NH4+-N availability throughout the cropping period as compared to NO3–-N which showed significant differences among management at critical crop growth stages.ConclusionLegume biomass incorporation (GM, LE) and farmyard manure (FYM) based management provided the most consistent supply equivalent to or even exceeding 100% inorganic fertilizers at several critical stages of growth, especially at tillering and stem elongation. Integration of organics in management increased nitrogen use efficiency 1.3–2.0 times, with cereal crop residue-based management having the highest efficiency followed by legume biomass incorporation.

scholarly journals Winter wheat crop water consumption and its effect on yields in southern Romania, in the very dry 2019-2020 agricultural year

2021 ◽  
Vol 49 (2) ◽  
pp. 12309
Author(s):  
Mihai BERCA ◽  
Valentina-Ofelia ROBESCU ◽  
Roxana HOROIAS
Keyword(s):  
Winter Wheat ◽  
Water Consumption ◽  
Soil Depth ◽  
Vegetation Period ◽  
Early Summer ◽  
Wheat Crop ◽  
Soil Drought ◽  
Crop Water ◽  
Winter Wheat Crop ◽  
Crop Water Consumption

Researches on winter wheat in the south part of Romanian Plain during the dry years 2019 and 2020 have been focused on the crop water consumption issue in excessive conditions of air and soil drought. The wheat crop water consumption in the research sites (Calarasi and Teleorman counties), for the entire vegetation period, autumn – spring – summer, is between 1000 and 1050 m3 of water for each ton of wheat produced. Only in the spring-summer period, the wheat extracts a quantity of about 5960 m3 ha-1, i.e. 851 m3 t-1. The useful water reserve is normally located at about 1500 m3/ha-1, at a soil depth of 0-150 cm. In the spring of 2020, it has been below 400 m3 ha-1, so that at the beginning of May the soil moisture had almost reached the wilting coefficient (WC). Wheat plants have been able to survive the thermal and water shock of late spring - early summer, due to enhanced thermal alternation between air and soil. For a period of about 34 days, this alternation brought the plants 1-1.5 mm water, i.e. approximately 442 m3 ha-1, which allowed the prolongation of the plant’s agony until the rains of the second half of May. Yields have been, depending on the variety, between 1500 and 3000 kg ha-1, in average, covering only 60% of the crop costs. Other measures to save water in the soil have also been proposed in the paper.

scholarly journals Influence of legume crops on content of organic carbon in sandy soil

2015 ◽  
Vol 66 (2) ◽  
pp. 52-56 ◽  
Author(s):  
Edmund Hajduk ◽  
Stanisław Właśniewski ◽  
Ewa Szpunar-Krok
Keyword(s):  
Organic Carbon ◽  
Carbon Content ◽  
Sandy Soil ◽  
Pure Culture ◽  
N Fertilization ◽  
Organic Carbon Content ◽  
Mineral N ◽  
Legume Crop ◽  
Naked Oats ◽  
Higher Doses

AbstractThe paper presents the results of a 3-year field experiment designed to evaluate the content of organic carbon in brown soil (Haplic Cambisol Dystric) developed from a light loamy sand under legumes cultivation. Experimental factors were: species of legume crop (colorful-blooming pea(Pisum sativum), chickling vetch(Lathyrus sativus), narrow-leafed lupin(Lupinus angustifolius), methods of legumes tillage (legumes in pure culture and in mixture with naked oats) and mineral N fertilization (0, 30, 60, 90 kg N·ha−1). Cultivation of legumes on sandy soil did not result in an increase of organic carbon content in the soil after harvest as compared to the initial situation, i.e. 7.39 vs. 7.76 g·kg−1dry matter (DM), on average, respectively. However, there was the beneficial effect of this group of plants on soil abundance in organic matter, the manifestation of which was higher content of organic carbon in soils after legume harvest as compared to soils with oats grown (7.21 g·kg−1DM, on average). Among experimental crops, cultivation of pea exerted the most positive action to organic carbon content (7.58 g·kg−1, after harvest, on average), whereas narrow-leaved lupin had the least effect on organic carbon content (7.23 g·kg−1, on average). Pure culture and greater intensity of legume cultivation associated with the use of higher doses of mineral nitrogen caused less reduction in organic carbon content in soils after harvest.

scholarly journals Effect of Tillage, Previous Crop, and N Fertilization on Agronomic and Economic Performances of Durum Wheat (Triticum durum Desf.) under Rainfed Semi-Arid Environment

Agronomy ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 1161
Author(s):  
Amir Souissi ◽  
Haithem Bahri ◽  
Hatem Cheikh M’hamed ◽  
Mohamed Chakroun ◽  
Salah Benyoussef ◽  
...  
Keyword(s):  
Durum Wheat ◽  
Arid Environment ◽  
N Fertilization ◽  
No Tillage ◽  
Tillage Practices ◽  
Previous Crop ◽  
Use Efficiency ◽  
Gross Margins ◽  
N Use ◽  
Semi Arid

The implementation of conservation agriculture (CA) remains crucial for facing interannual variability in climatic conditions that impact durum wheat production and food security. The current work was conducted to assess the effects of the tillage practice, previous crop, and nitrogen (N) fertilization rate on the agronomic and economic performances of rainfed durum wheat in a semi-arid environment in Tunisia. Tillage practices included no-tillage (NT) and conventional tillage (CT). Preceding crops were either a common vetch or a bread wheat. The N rates applied were: 0, 75, 100, 120, and 140 kg N ha−1. Our results show that, based on a 2-year experiment, tillage practices are not affecting grain yield, grain N, and gross margins. However, the N-use efficiency of durum wheat was significantly higher when wheat was grown using NT. Grain yield and N content in grain were 340 kg ha−1 and 0.34%; much higher after vetch than after bread wheat. For both tillage practices, the merit of 75 kg N ha−1 is paramount to maximize yield through a more efficient use of available N. Our results highlight the importance of no-tillage-based CA combined with rotation, including vetch, on enhanced yields, N-use efficiency, and gross margins. These findings provide the evidence of the positive impact of CA for rainfed durum wheat under semi-arid Mediterranean conditions.

scholarly journals Comparison of Spectral Reflectance-Based Smart Farming Tools and a Conventional Approach to Determine Herbage Mass and Grass Quality on Farm

2020 ◽  
Vol 12 (19) ◽  
pp. 3256
Author(s):  
Leonie Hart ◽  
Olivier Huguenin-Elie ◽  
Roy Latsch ◽  
Michael Simmler ◽  
Sébastien Dubois ◽  
...  
Keyword(s):  
Crude Protein ◽  
Near Infrared ◽  
Vegetation Period ◽  
Conventional Approach ◽  
Spatiotemporal Variability ◽  
Growth Stages ◽  
Harvest Scheduling ◽  
Analysis Model ◽  
Near Infrared Reflectance ◽  
Smart Farming

The analysis of multispectral imagery (MSI) acquired by unmanned aerial vehicles (UAVs) and mobile near-infrared reflectance spectroscopy (NIRS) used on-site has become increasingly promising for timely assessments of grassland to support farm management. However, a major challenge of these methods is their calibration, given the large spatiotemporal variability of grassland. This study evaluated the performance of two smart farming tools in determining fresh herbage mass and grass quality (dry matter, crude protein, and structural carbohydrates): an analysis model for MSI (GrassQ) and a portable on-site NIRS (HarvestLabTM 3000). We compared them to conventional look-up tables used by farmers. Surveys were undertaken on 18 multi-species grasslands located on six farms in Switzerland throughout the vegetation period in 2018. The sampled plots represented two phenological growth stages, corresponding to an age of two weeks and four to six weeks, respectively. We found that neither the performance of the smart farming tools nor the performance of the conventional approach were satisfactory for use on multi-species grasslands. The MSI-model performed poorly, with relative errors of 99.7% and 33.2% of the laboratory analyses for herbage mass and crude protein, respectively. The errors of the MSI-model were indicated to be mainly caused by grassland and environmental characteristics that differ from the relatively narrow Irish calibration dataset. The On-site NIRS showed comparable performance to the conventional Look-up Tables in determining crude protein and structural carbohydrates (error ≤ 22.2%). However, we identified that the On-site NIRS determined undried herbage quality with a systematic and correctable error. After corrections, its performance was better than the conventional approach, indicating a great potential of the On-site NIRS for decision support on grazing and harvest scheduling.

scholarly journals Can temperatures from an online weather forecast service be suitable for modelling growth stages using a CERES-Wheat type phenology model?

2017 ◽  
Vol 8 (2) ◽  
pp. 684-688
Author(s):  
M. Launspach ◽  
J.A. Taylor ◽  
J. Wilson
Keyword(s):  
Air Temperature ◽  
Plant Development ◽  
Weather Forecast ◽  
Growth Stages ◽  
North East ◽  
Temperature Forecast ◽  
Temperature And Precipitation ◽  
Station Data ◽  
Forecast Data ◽  
Phenology Model

Weather and climate have a fundamental impact on plant development. Monitoring key observables, e.g. temperature and precipitation, is paramount for the interpretation of agricultural experiments and simulation of plant development. Whereas the presence of appropriate sensors in a research environment can be expected, the situation can be different in commercial agricultural settings. Local air temperature from online weather forecasts is investigated as a substitute for local weather station data. Hourly air temperature forecast and station data for several locations in Scotland and North East England are aggregated into daily air temperature values spanning a period of several months. Dates for key growth stages using temperatures from weather stations and weather forecast data are compared. For the examples discussed here the date differences in modelled key growth stages did not exceed 3 days indicating that temperature forecast data is suitable for farm-specific applications.

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