The impact of topography on soil properties and yield and the effects of weather conditions

Kumhálová, Jitka; Kumhála, F.; Kroulík, M.; Matějková, Štěpánka

doi:10.1007/s11119-011-9221-x

Cited by 71 publications

(41 citation statements)

References 25 publications

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“…This crop response was probably caused by low precipitation during the growth stages BBCH 30-59. Again, low precipitation can cause a significant displacement of relatively higher yield to water-accumulating depressions (Kumhálová et al, 2011;. The summary statistics presented in Table 4 confirmed again the yield inequality.…”

Section: Resultssupporting

confidence: 61%

“…The movement of higher yield to concave areas in 2007 was also validated by summary statistics presented in Table 4, whereby both standard deviation and min-max range were higher than in 2015. In our previous articles (Kumhálová et al, 2011;, the influence of topography on yield in drier years was also found. Table 5 and Fig.…”

Section: Resultsmentioning

confidence: 81%

“…Yield was measured by a combine harvester equipped with the yield monitor LH 500 (LH Agro, Denmark). Detailed description of the yield measuring device can be found in Kumhálová et al (2011). Experimental variograms of yield were computed by common procedures using an exponential model.…”

Section: Methodsmentioning

confidence: 99%

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Yield variability prediction by remote sensing sensors with different spatial resolution

Kumhálová¹,

Matějková²

2017

International Agrophysics

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Currently, remote sensing sensors are very popular for crop monitoring and yield prediction. This paper describes how satellite images with moderate (Landsat satellite data) and very high (QuickBird and WorldView-2 satellite data) spatial resolution, together with GreenSeeker hand held crop sensor, can be used to estimate yield and crop growth variability. Winter barley (2007 and 2015) and winter wheat (2009 and 2011) were chosen because of cloud-free data availability in the same time period for experimental field from Landsat satellite images and QuickBird or WorldView-2 images. Very high spatial resolution images were resampled to worse spatial resolution. Normalised difference vegetation index was derived from each satellite image data sets and it was also measured with GreenSeeker handheld crop sensor for the year 2015 only. Results showed that each satellite image data set can be used for yield and plant variability estimation. Nevertheless, better results, in comparison with crop yield, were obtained for images acquired in later phenological phases, e.g. in 2007 - BBCH 59 - average correlation coefficient 0.856, and in 2011 - BBCH 59-0.784. GreenSeeker handheld crop sensor was not suitable for yield estimation due to different measuring method.

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Section: Resultssupporting

confidence: 61%

Section: Resultsmentioning

confidence: 81%

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Yield variability prediction by remote sensing sensors with different spatial resolution

Kumhálová¹,

Matějková²

2017

International Agrophysics

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“…Yield was measured by a combine harvester equipped with the yield monitor LH 500 (LH Agro, Aabybro, Denmark). Detailed description of the yield measuring device can be found in Kumhálová et al (2011). Experimental variograms of yield were computed by common procedures using an exponential model.…”

Section: Methodsmentioning

confidence: 99%

Use of Landsat images for yield evaluation within a small plot

et al. 2014

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Many factors can influence crop yield. One of the most important factors is topography, which can play a crucial role especially in dry years. Plant variability can be monitored by many methods. This paper evaluates the suitability of vegetation indices derived from satellite Landsat 5 TM data in comparison with yield, curvature and topography wetness index over a relatively small field (11.5 ha). Imageries were chosen from the years 2006 and 2010, when oat was grown and from 2005 and 2011, when winter wheat was grown. These images were taken in June in the same growth stage for every crop. It was confirmed that derived indices from Landsat images can be used for comparison with yield and selected topographic attributes and it can explain yield variability, which can be influenced by water distribution during growth stages. Correlation coefficient between moisture stress index and winter wheat yield was -0.816 in the image acquisition date of 4. 6. 2011.

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“…A su vez, se observó que los datos de ambos métodos tuvieron altos coeficientes de variación, T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 lo cual estaría asociado a la alta variabilidad espacial de los contenidos de P de los suelos (Kumhálová et al, 2011) Respuesta a la fertilización fosforada…”

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Corn and Soybean Response to Granulated and Liquid Phosphorus Fertilizer in Soils of the Semiarid Pampas

Bernal¹,

Santiago²,

Noellemeyer³

2018

Semiárida

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RESUMENEl objetivo del estudio fue evaluar la eficacia de dos métodos para la determinación del P disponible y la respuesta de maíz y soja a la fertilización fosforada con fuentes sólida y líquida de P en un suelo Hapludol típico con presencia de carbonatos de calcio. El ensayo se realizó en las cercanías de General Pico (La Pampa) y durante dos campañas se fertilizó maíces de siembra temprana y tardía y en una campaña dos cultivares de soja, con superfosfato triple (SPT) y polifosfato de amonio (APP) con una dosis de 40 kg.ha -1 de P 2 O 5 . Se determinó el P disponible en los primeros 20 cm por los métodos Bray-Kurtz 1 y Olsen, y el rendimiento para todos los tratamientos. Los resultados mostraron que no hubo respuesta al agregado de P con excepción de dos tratamientos fertilizados con SPT (maíz tardío y soja grupo 4 corto). Los valores de P extractable a la cosecha fueron mayores para los tratamientos fertilizados con APP. Esto se observó sobre todo para el P Olsen, mientras que para Bray-Kurtz 1 las diferencias fueron menores. Estos resultados indicarían que los suelos solubilizarían suficientes cantidades de P para satisfacer los requerimientos de maíces y soja. La diferencia entre las dos fuentes de P no se manifestó. PALABRAS CLAVE:Bray-Kurtz 1, P Olsen, polifosfato de amonio, superfosfato triple ABStRACtThe aim of the survey was to evaluate the efficiency of two methods for the determination of available P and the effect of fertilization with solid and liquid P sources on corn and soybean crops in soils with calcium carbonates. A field experiment near general Pico (La Pampa) was used where during two crop seasons early and late planted corn and in one season two maturity groups of soybean were fertilized with triple superphosphate (TSP) and ammonium polyphosphate (APP) at 40 kg.ha -1 of P 2 O 5 . The available P was determined with Bray-Kurtz 1 and Olsen extractions, and the yield was recorded in all treatments. The results showed no response to P addition except in two fertilized treatments with TSP (one late corn crop and in the group 4 soybean cultivar). This was particularly true for POlsen, while for Bray-Kurtz 1 the differences were less. These results indicated that the soils solubilized sufficient quantities of P to satisfy the requirements of corn and soybean and no differences between the two sources was found.

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The impact of topography on soil properties and yield and the effects of weather conditions

Cited by 71 publications

References 25 publications

Yield variability prediction by remote sensing sensors with different spatial resolution

Yield variability prediction by remote sensing sensors with different spatial resolution

Use of Landsat images for yield evaluation within a small plot

Corn and Soybean Response to Granulated and Liquid Phosphorus Fertilizer in Soils of the Semiarid Pampas

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