Biological control and growth promotion in Solanum spp.

Potato (Solanum tuberosum) is Sweden’s most treated crop in terms of chemical disease control, primarily to prevent potato late blight caused by Phytophthora infestans. Another important disease is potato early blight caused by Alternaria solani. In this thesis biological disease control was explored as a supplement or an alternative to chemical disease control. The idea was to isolate, identify and apply bacteria on potato plants and examine their effects on growth and disease control. Two bacterial strains were isolated from the potato relative bittersweet nightshade(Solanum dulcamare), assuming that these bacteria would also colonize potato roots. Screening for suitable bacteria used tissue prints and biofilm forming capability. Identification of the bacterial species was based on partial gene sequencing of the 16S rRNA gene and several housekeeping genes as well as biotests. Initial gene sequencing results did not completely match any bacteria in the NCBI database, but indicated relatedness to the genus Stenotrophomonas. Since certain Stenotrophomonas strains are opportunistic human pathogens, time was re-allocated to further investigate the identity before further applied work with the bacteria. Many Stenotrophomonas strains are plant-associated and candidates for growth promotion and biological control according to literature. The growth promoting effect of the two bacteria was weak but partly statistically significant in greenhouse tests. The in vitro experiments with disease control of P. infestans and A. solani were difficult to evaluate. Pathogen inoculation of potato leaves previously treated with the bacterial strains, indicated a control effect in certain bacteria/pathogen combinations. In tubers however, pathogen inoculation resulted in no disease symptoms neither in the control group nor in the group pre-treated with the bacterial strains. These experiments should be regarded as pilot studies and given published studies, additional experiments should be conducted with our isolates. Growth promotion and biological control based on beneficial plant-microbe interaction will play an important role in future crop production and pest management, either as a complement or as a substitute to chemicals. Challenges are to increase efficacy of these treatments in complex biological environments and assure successful transfer of in vitro effects to field conditions

Using Sentinel-2 Satellite Images to Estimate Traits of Forage Grasslands

In this project, regression models based on data from field measurements and spectral information extracted from satellite imagery were used to estimate traits of forage grasslands; dry matter yield, canopy average height and total leaf chlorophyll. Four fields at SLUs Röbäcksdalen field station were sampled on 22 occasions and a total of 198 samples, including measurement of the highest plant, canopy height, leaf chlorophyll content, canopy spectral reflectance and biomass were collected. Two regression methods, partial least squares (PLS) and support vector machines (SVM), were used to build regression models using different subsets of the available spectral information. Model calibration was performed with 2/3 of the dataset and model validation was performed with the remaining 1/3 of the dataset. It was shown that the models built with SVM outperformed the models built with PLS, during both calibration and validation as well as for all different traits and subsets of spectral information. Field measurement and regression model results were discussed and limitations, their significance and possible improvements were considered. It was concluded that using spectral information from satellite images is a promising approach for estimation of traits in the field and could be used to build tools as a tool to support farmers’ decision making.Vall är en viktig del av svenskt lantbruk och produceras på ungefär 45% av den tillgängliga åkermarken. Vall används främst för produktion av djurfoder men även som en del av växtföljder för att minska trycket från skadegörare eller ogräs och för att främja den biologiska mångfalden. På grund av förhållandevis låga vinstmarginaler vid försäljning av djurfoder investerar lantbruksföretag inte mycket i optimering av vallproduktion. En mindre kostsam investering kan vara precisionsodling, som är ett sätt att optimera växtodling genom att övervaka jordbruksgrödor automatiskt och genomföra insatser såsom applicering av gödsel och växtskyddsmedel eller själva skörden vid rätt tidpunkt och på rätt plats. Inom lantbruk tillämpas övervakningen, också kallad fjärranalys, ofta med sensorer som mäter hur synligt och osynligt ljus av olika våglängder reflekteras av en yta. I det här fallet har denna information använts i form av satellitbilder från Sentinel-2 satelliterna som är tillgängliga varannan dag. Satellitbilderna kan sedan användas för att bygga modeller genom att koppla ihop resultat av mätningar i fält med informationen från satellitbilden. Syftet med detta projekt var att utföra mätningar i fält och bygga modeller baserat på dessa mätningar samt informationen från satellitbilderna. Hypotesen var att bilderna från Sentinel-2 satelliterna gör det möjligt att med hjälp av modellerna uppskatta mätvärden i fält. Inom projektet genomfördes mätningar av biomassa, planthöjd, klorofyllinnehåll och reflektans på 4 olika fält med vall vid SLUs Röbäcksdalen forskningsstation i Umeå. Två regressionsmetoder, partial least squares (PLS) och support vector machines (SVM), användes för att bygga modellerna. Kalibrering och anpassning av modellerna utfördes med 2/3 av satellitbilderna och fältmätningarna, kontroll och validering av modellerna utfördes med resterande 1/3 av satellitbilderna och fältmätningarna. Resultaten visade att SVM modellerna fungerade bättre än PLS modellerna vid uppskattning av mätvärden i fält. En utvärdering av modellerna på andra fält på annan ort har inte genomförts och det är okänt hur modellerna fungerar under olika förhållanden. En intressant aspekt som upptäcktes var att modeller byggda med satellitbilderna som motsvarar synligt ljus gav förhållandevis bra resultat. Det innebär att kamerabilder, till exempel från en drönare, skulle kunna användas för att bygga modeller vilket öppnar för mätningar vid behov. Metoden är lovande och skulle som ett verktyg kunna användas av lantbrukare för övervakning av jordbruksgrödor och hjälpa dem att fatta beslut kring insatser

Forage biomass estimation using sentinel-2 imagery at high latitudes

Forages are the most important kind of crops at high latitudes and are the main feeding source for ruminant-based dairy industries. Maximizing the economic and ecological performances of farms and, to some extent, of the meat and dairy sectors require adequate and timely supportive field-specific information such as available biomass. Sentinel-2 satellites provide open access imagery that can monitor vegetation frequently. These spectral data were used to estimate the dry matter yield (DMY) of harvested forage fields in northern Sweden. Field measurements were conducted over two years at four sites with contrasting soil and climate conditions. Univariate regression and multivariate regression, including partial least square, support vector machine and random forest, were tested for their capability to accurately and robustly estimate in-season DMY using reflectance values and vegetation indices obtained from Sentinel-2 spectral bands. Models were built using an iterative (300 times) calibration and validation approach (75% and 25% for calibration and validation, respectively), and their performances were formally evaluated using an independent dataset. Among these algorithms, random forest regression (RFR) produced the most stable and robust results, with Nash–Sutcliffe model efficiency (NSE) values (average ± standard deviation) for the calibration, validation and evaluation of 0.92 ± 0.01, 0.55 ± 0.22 and 0.86 ± 0.04, respectively. Although relatively promising, these results call for larger and more comprehensive datasets as performances vary largely between calibration, validation and evaluation datasets. Moreover, RFR, as any machine learning algorithm regression, requires a very large dataset to become stable in terms of performance

Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes

Forages are the most important kind of crops at high latitudes and are the main feeding source for ruminant-based dairy industries. Maximizing the economic and ecological performances of farms and, to some extent, of the meat and dairy sectors require adequate and timely supportive field-specific information such as available biomass. Sentinel-2 satellites provide open access imagery that can monitor vegetation frequently. These spectral data were used to estimate the dry matter yield (DMY) of harvested forage fields in northern Sweden. Field measurements were conducted over two years at four sites with contrasting soil and climate conditions. Univariate regression and multivariate regression, including partial least square, support vector machine and random forest, were tested for their capability to accurately and robustly estimate in-season DMY using reflectance values and vegetation indices obtained from Sentinel-2 spectral bands. Models were built using an iterative (300 times) calibration and validation approach (75% and 25% for calibration and validation, respectively), and their performances were formally evaluated using an independent dataset. Among these algorithms, random forest regression (RFR) produced the most stable and robust results, with Nash–Sutcliffe model efficiency (NSE) values (average ± standard deviation) for the calibration, validation and evaluation of 0.92 ± 0.01, 0.55 ± 0.22 and 0.86 ± 0.04, respectively. Although relatively promising, these results call for larger and more comprehensive datasets as performances vary largely between calibration, validation and evaluation datasets. Moreover, RFR, as any machine learning algorithm regression, requires a very large dataset to become stable in terms of performance

Undetected pseudoprogressions in the CeTeG/NOA-09 trial: hints from postprogression survival and MRI analyses

Purpose: In the randomized CeTeG/NOA-09 trial, lomustine/temozolomide (CCNU/TMZ) was superior to TMZ therapy regarding overall survival (OS) in MGMT promotor-methylated glioblastoma. Progression-free survival (PFS) and pseudoprogression rates (about 10%) were similar in both arms. Further evaluating this discrepancy, we analyzed patterns of postprogression survival (PPS) and MRI features at first progression according to modified RANO criteria (mRANO).Methods: We classified the patients of the CeTeG/NOA-09 trial according to long vs. short PPS employing a cut-off of 18 months and compared baseline characteristics and survival times. In patients with available MRIs and confirmed progression, the increase in T1-enhancing, FLAIR hyperintense lesion volume and the change in ADC mean value of contrast-enhancing tumor upon progression were determined.Results: Patients with long PPS in the CCNU/TMZ arm had a particularly short PFS (5.6 months). PFS in this subgroup was shorter than in the long PPS subgroup of the TMZ arm (11.1 months, p = 0.01). At mRANO-defined progression, patients of the CCNU/TMZ long PPS subgroup had a significantly higher increase of mean ADC values (p = 0.015) and a tendency to a stronger volumetric increase in T1-enhancement (p = 0.22) as compared to long PPS patients of the TMZ arm.Conclusion: The combination of survival and MRI analyses identified a subgroup of CCNU/TMZ-treated patients with features that sets them apart from other patients in the trial: short first PFS despite long PPS and significant increase in mean ADC values upon mRANO-defined progression. The observed pattern is compatible with the features commonly observed in pseudoprogression suggesting mRANO-undetected pseudoprogressions in the CCNU/TMZ arm of CeTeG/NOA-09.Keywords: Glioblastoma; MGMT promotor methylation; MRI; Progression; Pseudoprogression