Influence of Weed Competition on Potato Growth, Production and Radiation Use Efficiency

Mondani, Farzad; Golzardi, Farid; Ahmadvand, G; Ghorbani, Reza; Moradi, Rooholla

doi:10.15835/nsb336125

Cited by 19 publications

(14 citation statements)

References 32 publications

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“…This phenomenon emerges from sampling continuously increasing or decreasing GP samples, which are more frequent when the sample is close to patterns in data where the response to fertilizer is flat. A zero-fertilizer recommendation could be interpreted as a soil sufficiently fertile to supply the crop, or a soil poorly responsive due to other constraints [122] such as pests and diseases [20, 123] or weed damage [124]. Nevertheless, we covered a wide range of factors that may impact potato crop growth and yield without falling into mechanistic modeling.…”

Section: Discussionmentioning

confidence: 99%

Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada

Coulibali

Cambouris

Parent

2020

Preprint

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17Statistical modeling is commonly used to relate the performance of potato 18 (Solanum tuberosum L.) to fertilizer requirements. Prescribing optimal nutrient doses is 19 challenging because of the involvement of many variables including weather, soils, land 20 management, genotypes, and severity of pests and diseases. Where sufficient data are 21 available, machine learning algorithms can be used to predict crop performance. The 22 objective of this study was to predict tuber yield and quality (size and specific gravity) as 23 impacted by nitrogen, phosphorus and potassium fertilization as well as weather, soils 24 and land management variables. We exploited a data set of 273 field experiments 25 conducted from 1979 to 2017 in Quebec (Canada). We developed, evaluated and 26 compared predictions from a hierarchical Mitscherlich model, k-nearest neighbors, 27 random forest, neuronal networks and Gaussian processes. Machine learning models 28 returned R 2 values of 0.49-0.59 for tuber marketable yield prediction, which were higher 29 than the Mitscherlich model R 2 (0.37). The models were more likely to predict medium-30 size tubers (R 2 = 0.60-0.69) and tuber specific gravity (R 2 = 0.58-0.67 ) than large-size 31 tubers (R 2 = 0.55-0.64) and marketable yield. Response surfaces from the Mitscherlich 32 model, neural networks and Gaussian processes returned smooth responses that agreed 33 more with actual evidence than discontinuous curves derived from k-nearest neighbors 34 and random forest models. When marginalized to obtain optimal dosages from dose-35 response surfaces given constant weather, soil and land management conditions, some 36 disagreements occurred between models. Due to their built-in ability to develop 37 recommendations within a probabilistic risk-assessment framework, Gaussian processes 38 stood out as the most promising algorithm to support decisions that minimize economic 39 or agronomic risks. 3 40 Keywords: 41 Precision fertilization, Mitscherlich model, k-nearest neighbors, random forest, neuronal 42 networks, Gaussian process, economic optimal dose, agronomic optimal dose, Solanum 43 tuberosum L. 4 44 2 Introduction 45 Modeling provides a quantitative understanding of how crop systems operate 46 [1]. Site-specific simulations of fertilizer requirements to obtain high local potato yield 47 and quality rely on models' ability to detect subtle variations in factors affecting plant 48 growth and environment and to learn from the past to make predictions [2]. Several crop 49 models have been developed with different degrees of sophistication, scale, and 50 representativeness [2]. Mechanistic models have been published for potato cropping 51 systems [3, 4]. Semi-mechanistic growth models could be used to downscale tuber yield 52 assessment from regional to field levels [5, 6]. Multilevel modeling can assist in 53 selecting a set of relevant parameters that impact tuber yield and fertilizer requirements, 54 but can hardly predict site-specific nutrient requirements [7]. 55 Several variables can im...

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Section: Discussionmentioning

confidence: 99%

Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada

Coulibali

Cambouris

Parent

2020

Preprint

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“…Velvetleaf reduced maize cumulative absorbed light, so cumulative absorbed light by maize dropped to below 50 % (Figure 3h). Mondani et al (2011) stated that weed competition decreased light interception of potato due to reduction in LAI and lodging of potato canopy. Bean and soybean decreased the cumulative absorbed light by velvetleaf, therefore light interception by maize increased compared to pure stand of maize under weedy conditions (Figures 3a, 3b, 3d, 3e, 3h).…”

Section: Cumulative Absorbed Lightmentioning

confidence: 99%

“…This yield reduction is probably result of clover's weak suppression effect on velvetleaf performance, in fact, clover presence also decreased the leaf area index of maize (Table 1) and increased the leaf area density of velvetleaf compared to bean and soybean (Figures 1c, 1f). One of the most important aims of crop management, especially in the presence of weeds, is the better capture of solar radiation by crops (Mondani et al, 2011). In general, soybean improved the yield of maize through increasing in the light interception by maize and reduction of light interception by velvetleaf compared to maize sole cropping under weed infestation (Figure 2).…”

Section: Yield Of Maizementioning

confidence: 99%

Effect of cover crops on maize-velvet leaf competition: leaf area density and light interception

Zaefarian

shakibafar

Rezvani

et al. 2016

AAS

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Cover crops influence on canopy structure and light interception of maize (Zea mays L.) and velvetleaf (Abutilon theophrasti Medik), was studied in a field experiment. Treatments included planting of bean (Phaseolus vulgaris L.), soybean (Glycine max (L.) Merr.) and berseem clover (Trifolium alexandrium L.) as cover crops at the same date and 21 days after maize. Sole cropping of maize under weed-free and weedy conditions were also included in this experiment. All tested cover crops significantly reduced leaf area density and height of velvetleaf up to 50 %, while maize leaf area density increased in the presence of cover crops. Among cover crops, bean and soybean were the most effective in reducing velvetleaf leaf area density and height. Bean and soybean also strongly reduced absorbed light by velvetleaf by up to 80 % compared to clover. Maize grain yields were significantly influenced by cover crops planting in the inter row space. Compared to weeds free plots, only treatment with soybean as a cover crop resulted in similar maize grain yields, while maize intercropping with bean and clover significantly reduced maize yields. Delayed planting of cover crops, 21 day after maize, increased maize grain yield compared to cover crops and maize planting at the same time. Obravnavanja so obsegala setev nizkega fižola (Phaseolus vulgaris L.), soje (Glycine max (L.) Merr.) in aleksandrijske detelje (Trifolium alexandrium L.) kot podsevkov, posejane istega dne kot koruza ali 21 dni po njeni setvi. V poskus sta bili vključeni tudi 2 kontroli (čista setev koruze z zatiranjem in brez zatiranja plevelov). Vsi preizkušeni podsevki so značilno zmanjšali gostoto listne površine in višino bržunastega osleza do 50 %, medtem ko se je gostota listne površine koruze v prisotnosti vseh treh podsevkov povečala. Med podsevki sta bila fižol in soja najbolj učinkovita v zmanjševanju gostote listne površine in višine bržunastega osleza. Fižol in soja sta v primerjavi z deteljo najmočneje zmanjšala absorbirano svetlobo bržunastega osleza, do 80 %. Podsevki v vrste med koruzo so značilno vplivali na pridelek njenega zrnja. V primerjavi s kontrolo brez plevela je samo obravnavanje s sojo kot podsevkom dalo podobne pridelke, podsevka fižola in detelje sta značilno zmanjšali pridelek zrnja koruze. Odložena setev podsevkov, 21 dni po setvi koruze, je povečala pridelek zrnja koruze v primerjavi z obravnavanji, ko so bili koruza in podsevki posejani istočasno.Ključne besede: zgradba sestoja, gostota listne površine, prestrezanje svetlobe, indeks listne površine, koruza, bržunasti oslez, tekmovalnost plevelov Zahra SHAKIBAFAR et al.

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“…A randomized complete block design was used with four replications in 2001-2002and 3 replications in 2010-2011-2011 each plot was composed of three inter-furrows of 1.5 m and 4.2 m width (1.5 meter between furrows). The plot's area was 18.9 m 2 (4.5 m x 4.2 m).…”

Section: Experimental Design and Proceduresmentioning

confidence: 99%

Determination of critical period for weed control in intensive and non-intensive sugarcane (<i>Saccharum officinarum</i> L., Poaceae) production systems in center Côte d’Ivoire

Kouamé¹,

Orega²,

Toure³

et al. 2015

Int. J. Bio. Chem. Sci

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Field experiments were conducted in Zuenoula and Yamoussoukro for determining Critical Period for Weed Control (CPWC) in sugarcane. The treatments consisted in two sets of weed interference. In the first set, the crop was kept weed-free until 31, 61, 92, 123 days after planting (DAP) in Zuenoula and until 32, 69, 98 and 162 DAP in Yamoussoukro. In the second set, weeds were permitted to grow within the crop until the above-mentioned DAP. The CPWC was determined for 5, 10, 15 and 20% acceptable yield loss levels by fitting Logistic and Gompertz nonlinear equations to relative yield data. In both locations, increasing the duration of weed interference decreased sugarcane yield significantly. In Zuenoula, the CPWC was from 28 to 117, 30 to 93, 32 to 75 and 34 to 59 DAP to prevent yield losses of 5, 10, 15 and 20%, respectively. In Yamoussoukro, the CPWC ranged from 38 to 163, 39 to 112, 40 to 99 and from 40 to 91 DAP to prevent yield losses of 5, 10, 15 and 20%, respectively. Results suggest weed control between 28 and 117 DAP in Zuenoula and between 38 and 163 DAP in Yamoussoukro to provide maximum yield.

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Influence of Weed Competition on Potato Growth, Production and Radiation Use Efficiency

Cited by 19 publications

References 32 publications

Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada

Site-specific machine learning predictive fertilization models for potato crops in Eastern Canada

Effect of cover crops on maize-velvet leaf competition: leaf area density and light interception

Determination of critical period for weed control in intensive and non-intensive sugarcane (<i>Saccharum officinarum</i> L., Poaceae) production systems in center Côte d’Ivoire

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