Map-based investigation of soil physical conditions and crop yield using diverse sensor techniques

Sun, Yifei; Druecker, Harm; Hartung, Eberhard; Hueging, H.; Cheng, Qiang; Qing-jun, Zeng; Sheng, Weifan; Lin, Jenshan; Roller, Olaf; Paetzold, S.; Lammers, Peter Schulze

doi:10.1016/j.still.2010.12.002

Cited by 23 publications

(14 citation statements)

References 27 publications

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“…1a, b). On the same field, Timmermann et al (2003), Mertens et al (2008) and Sun et al (2011) consistently found similar patterns between soil properties and maize growth and yield, respectively. Hence, the soil water supply directly affected interaction and competitiveness of weeds and crops.…”

Section: Variability Of Weed Distribution During the Long-term Surveymentioning

confidence: 74%

Linking weed patterns with soil properties: a long-term case study

et al. 2019

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The spatial distribution and density of different weed species were monitored during a long-term survey over a period of 9 years on a 5.8 ha arable field and related to soil properties. Weed seedlings were determined every year in spring on a regular grid with 429 observation points (15 × 7.5 m; net study area = 4 ha). Dominant weed species were Chenopodium album, Polygonum aviculare, Viola arvensis and different grass weeds, clearly dominated by Alopecurus myosuroides. A non-invasive electromagnetic induction survey was conducted to evaluate available water capacity directly in the field at high spatial resolution. Further soil properties were evaluated following the minimum-invasive approach with soil sampling and subsequent mid-infrared spectroscopy. Plant available nutrients were analysed with conventional lab methods. Redundancy analysis served to describe the effect of soil properties, different years and field crops on weed species variability. Seven soil properties together explained 30.7% of the spatial weed species variability, whereas 28.2% was explained by soil texture, available water capacity and soil organic carbon. Maps for site-specific weed management were created based on soil maps. These maps permit several benefits for precision crop protection, such as a better understanding of soilweed interrelations , improved sampling strategies and reduction in herbicide use.

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Section: Variability Of Weed Distribution During the Long-term Surveymentioning

confidence: 74%

Linking weed patterns with soil properties: a long-term case study

et al. 2019

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“…The basic climatical growth suitability thresholds are presented in Table 3. After the identification of suitable crops, the conceptualization of MALLIS for MAEZ was done on a general level (regional scale), since detailed best practice recommendations for the optimized management of agricultural practices very much depend on local conditions (field-to-farm scale) [56][57][58][59][60]. Therefore, the MALLIS for the new field trials to be conducted in the MAGIC project (field-to-farm scale) were developed considering three main MAEZ criteria:…”

Section: Definition and Methodology Of Marginal Land Low-input Systemmentioning

confidence: 99%

Marginal Agricultural Land Low-Input Systems for Biomass Production

et al. 2019

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This study deals with approaches for a social-ecological friendly European bioeconomy based on biomass from industrial crops cultivated on marginal agricultural land. The selected crops to be investigated are: Biomass sorghum, camelina, cardoon, castor, crambe, Ethiopian mustard, giant reed, hemp, lupin, miscanthus, pennycress, poplar, reed canary grass, safflower, Siberian elm, switchgrass, tall wheatgrass, wild sugarcane, and willow. The research question focused on the overall crop growth suitability under low-input management. The study assessed: (i) How the growth suitability of industrial crops can be defined under the given natural constraints of European marginal agricultural lands; and (ii) which agricultural practices are required for marginal agricultural land low-input systems (MALLIS). For the growth-suitability analysis, available thresholds and growth requirements of the selected industrial crops were defined. The marginal agricultural land was categorized according to the agro-ecological zone (AEZ) concept in combination with the marginality constraints, so-called ‘marginal agro-ecological zones’ (M-AEZ). It was found that both large marginal agricultural areas and numerous agricultural practices are available for industrial crop cultivation on European marginal agricultural lands. These results help to further describe the suitability of industrial crops for the development of social-ecologically friendly MALLIS in Europe.

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“…Zones with varying soil properties can be distinguished when conducting a soil survey with diverse sensing techniques. Sensor maps often unravel soil heterogeneity and explain variation in, e.g., grain yield or total biomass (Mertens et al 2008;Sun et al 2011). Such information is commercially provided by consultants and helps to define management or soil sampling zones.…”

Section: Introductionmentioning

confidence: 99%

Predicting plant available phosphorus using infrared spectroscopy with consideration for future mobile sensing applications in precision farming

et al. 2019

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Phosphorus (P) fertilisation recommendations rely primarily on soil content of plant available P (P avl) that vary spatially within farm fields. Spatially optimized P fertilisation for precision farming requires reliable, rapid and non-invasive P avl determination. This laboratory study aimed to test and to compare visible-near infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy for P avl prediction with emphasis on future application in precision agriculture. After calibration with the conventional calcium acetate lactate (CAL) extraction method, limitations of Vis-NIRS and MIRS to predict P avl were evaluated in loess topsoil samples from different fields at six localities. Overall calibration with 477 (Vis-NIRS) and 586 (MIRS) samples yielded satisfactory model performance (R 2 0.70 and 0.72; RPD 1.8 and 1.9, respectively). Local Vis-NIRS models yielded better results with R 2 up to 0.93 and RPD up to 3.8. For MIRS, results were comparable. However, an overall model to predict P avl on independent test data partly failed. Sampling date, pre-crop harvest residues and fertilising regime affected model transferability. Varying transferability could partly be explained after deriving the cellulose absorption index from the Vis-NIR spectra. In 62 (Vis-NIRS) and 67% (MIRS) of all samples, prediction matched the correct P avl content class. Rapid discrimination between high, optimal and low P classes could be carried out on many samples from single fields thus marking an improvement over the common practice. However, P avl determination by means of IR spectroscopy is not yet satisfactory for determination of precision fertilizer dosage. For introduction into agricultural practice, a standardized sampling protocol is recommended to help achieve reliable spectroscopic P avl prediction.

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Map-based investigation of soil physical conditions and crop yield using diverse sensor techniques

Cited by 23 publications

References 27 publications

Linking weed patterns with soil properties: a long-term case study

Linking weed patterns with soil properties: a long-term case study

Marginal Agricultural Land Low-Input Systems for Biomass Production

Predicting plant available phosphorus using infrared spectroscopy with consideration for future mobile sensing applications in precision farming

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