Use of remote sensing for linkage mapping and genomic prediction for common rust resistance in maize.

Breeding for disease resistance is a central component of strategies implemented to mitigate biotic stress impacts on crop yield. Conventionally, genotypes of a plant population are evaluated through a labor-intensive process of assigning visual scores (VS) of susceptibility (or resistance) by specifically trained staff, which limits manageable volumes and repeatability of evaluation trials. Remote sensing (RS) tools have the potential to streamline phenotyping processes and to deliver more standardized results at higher through-put. Here, we use a two-year evaluation trial of three newly developed biparental populations of maize doubled haploid lines (DH) to compare the results of genomic analyses of resistance to common rust (CR) when phenotyping is either based on conventional VS or on RS-derived (vegetation) indices. As a general observation, for each population × year combination, the broad sense heritability of VS was greater than or very close to the maximum heritability across all RS indices. Moreover, results of linkage mapping as well as of genomic prediction (GP), suggest that VS data was of a higher quality, indicated by higher -logp values in the linkage studies and higher predictive abilities for genomic prediction. Nevertheless, despite the qualitative differences between the phenotyping methods, each successfully identified the same genomic region on chromosome 10 as being associated with disease resistance. This region is likely related to the known CR resistance locus Rp1. Our results indicate that RS technology can be used to streamline genetic evaluation processes for foliar disease resistance in maize. In particular, RS can potentially reduce costs of phenotypic evaluations and increase trialing capacities.

Optical parametric generation in OP-GaAs waveguides pumped by a femtosecond fluoride fiber laser.

We report on mid-infrared optical parametric generation in the 4-5 µm and 9-12 µm bands by pumping custom-designed orientation-patterned gallium arsenide (OP-GaAs) rib waveguides with an ultrafast femtosecond fiber laser system. This pump source is seeded by a mode-locked fluoride fiber laser with 59 MHz repetition rate and can be tuned between 2.8 and 3.2 µm using a soliton self-frequency shifting stage. The single TE and TM modes OP-GaAs crystals feature quasi-phase-matched grating periods of 85 and 90 µm and different transverse sizes thus allowing a wide spectral tunability.

Radiative transfer model inversion using high-resolution hyperspectral airborne imagery - Retrieving maize LAI to access biomass and grain yield.

Mapping crop within-field yield variability provide an essential piece of information for precision agriculture applications. Leaf Area Index (LAI) is an important parameter that describes maize growth, vegetation structure, light absorption and subsequently maize biomass and grain yield (GY). The main goal for this study was to estimate maize biomass and GY through LAI retrieved from hyperspectral aerial images using a PROSAIL model inversion and compare its performance with biomass and GY estimations through simple vegetation index approaches. This study was conducted in two separate maize fields of 12 and 20 ha located in north-west Mexico. Both fields were cultivated with the same hybrid. One field was irrigated by a linear pivot and the other by a furrow irrigation system. Ground LAI data were collected at different crop growth stages followed by maize biomass and GY at the harvesting time. Through a weekly/biweekly airborne flight campaign, a total of 19 mosaics were acquired between both fields with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400 to 850 nanometres (nm) at different crop growth stages. The PROSAIL model was calibrated and validated for retrieving maize LAI by simulating maize canopy spectral reflectance based on crop-specific parameters. The model was used to retrieve LAI from both fields and to subsequently estimate maize biomass and GY. Additionally, different vegetation indices were calculated from the aerial images to also estimate maize yield and compare the indices with PROSAIL based estimations. The PROSAIL validation to retrieve LAI from hyperspectral imagery showed a R2 value of 0.5 against ground LAI with RMSE of 0.8 m2/m2. Maize biomass and GY estimation based on NDRE showed the highest accuracies, followed by retrieved LAI, GNDVI and NDVI with R2 value of 0.81, 0.73, 0.73 and 0.65 for biomass, and 0.83, 0.69, 0.73 and 0.62 for GY estimation, respectively. Furthermore, the late vegetative growth stage at V16 was found to be the best stage for maize yield prediction for all studied indices.

DOES SIZE MATTER? A CRITICAL REVIEW OF META-ANALYSIS IN AGRONOMY.

Intended to test broad hypotheses and arrive at unifying conclusions, meta-analysis is the process of extracting, assembling, and analyzing large quantities of data from multiple publications to increase statistical power and uncover explanatory patterns. This paper describes the ways in which meta-analysis has been applied to support claims and counter-claims regarding two topics widely debated in agricultural research, namely organic agriculture (OA) and conservation agriculture (CA). We describe the origins of debate for each topic and assess prominent meta-analyses considering data-selection criteria, research question framing, and the interpretation and extrapolation of meta-analytical results. Meta-analyses of OA and CA are also examined in the context of the political economy of development-oriented agricultural research. Does size matter? We suggest that it does, although somewhat ironically. While meta-analysis aims to pool all relevant studies and generate comprehensive databases from which broad insights can be drawn, our case studies suggest that the organization of many meta-analyses may affect the generalizability and usefulness of research results. The politicized nature of debates over OA and CA also appear to affect the divergent ways in which meta-analytical results may be interpreted and extrapolated in struggles over the legitimacy of both practices. Rather than resolving scientific contestation, these factors appear to contribute to the ongoing debate. Meta-analysis is nonetheless becoming increasingly popular with agricultural researchers attracted by the power for the statistical inference offered by large datasets. This paper consequently offers three suggestions for how scientists and readers of scientific literature can more carefully evaluate meta-analyses. First, the ways in which papers and data are collected should be critically assessed. Second, the justification of research questions, framing of farming systems, and the scales at which research results are extrapolated and discussed should be carefully evaluated. Third, when applied to strongly politicized topics situated in an arena of scientific debate, as is the case with OA and CA, more conservative interpretations of meta-analytical results that recognize the socially and politically embedded nature of agricultural research is are needed.

140 W peak power laser system tunable in the LWIR.

We present a high peak power rapidly tunable laser system in the long-wave infrared comprising an external-cavity quantum cascade laser (EC-QCL) broadly tunable from 8 to 10 µm and an optical parametric amplifier (OPA) based on quasi phase-matching in orientation-patterned gallium arsenide (OP-GaAs) of fixed grating period. The nonlinear crystal is pumped by a pulsed fiber laser system to achieve efficient amplification in the OPA. Quasi phase-matching remains satisfied when the EC-QCL wavelength is swept from 8 to 10 µm with a crystal of fixed grating period through tuning the pump laser source around 2 µm. The OPA demonstrates parametric amplification from 8 µm to 10 µm and achieves output peak powers up to 140 W with spectral linewidths below 3.5 cm-1. The beam profile quality (M2) remains below 3.4 in both horizontal and vertical directions. Compared to the EC-QCL, the linewidth broadening is attributed to a coupling with the OPA.

Enhancing Smallholder Access to Agricultural Machinery Services: Lessons from Bangladesh.

Resource poor smallholders in developing countries often lack access to capital goods such as farm machinery. Enabling adequate access through machinery services can thereby significantly contribute to food security and farm incomes. At the core of the service provision model is the lead farmer, who makes the initial investment in agricultural machinery, and provides services to others on a fee-for-service basis. Profiling the lead farmers can thereby provide important lessons and scaling implications. The present paper provides a case study of Bangladesh, using primary data to characterise the lead farmers. General education, credit availability and risk taking attitude play significant roles in whether or not a farm household will be a lead farmer in Bangladesh.

Optical parametric generation by a simultaneously Q-switched mode-locked single-oscillator thulium-doped fiber laser in orientation-patterned gallium arsenide.

Optical parametric generation is demonstrated in orientation-patterned gallium arsenide, pumped by a novel single-oscillator simultaneously Q-switched and mode-locked thulium-doped fiber laser, downconverting the pump radiation into the mid-infrared wavelength regime. The maximum output energy reached is greater than 2.0 µJ per pump pulse.

Multispectral and thermal infrared data, visual scores for severity of common rust symptoms, and genotypic single nucleotide polymorphism data of three F2-derived biparental doubled-haploid maize populations.

Three F2-derived biparental doubled haploid (DH) maize populations were generated for genetic mapping of resistance to common rust. Each of the three populations has the same susceptible parent, but a different resistance donor parent. Population 1 and 3 consist of 320 lines each, population 2 consists of 260 lines. The DH lines were evaluated for their susceptibility to common rust in two years and with two replications in each year. For phenotyping, a visual score (VS) for susceptibility was assigned. Additionally, unmanned aerial vehicle (UAV) derived multispectral and thermal infrared data was recorded and combined in different vegetation indices ("remote sensing", RS). The DH lines were genotyped with the DarTseq method, to obtain data on single nucleotide polymorphisms (SNPs). After quality control, 9051 markers remained. Missing values were "imputed" by the empirical mean of the marker scores of the respective locus. We used the data for comparison of genome-wide association studies and genomic prediction when based on different phenotyping methods, that is either VS or RS data. The data may be interesting for reuse for instance for benchmarking genomic prediction models, for phytopathological studies addressing common rust, or for specifications of vegetation indices.

Robust, frequency-stable and accurate mid-IR laser spectrometer based on frequency comb metrology of quantum cascade lasers up-converted in orientation-patterned GaAs.

We demonstrate a robust and simple method for measurement, stabilization and tuning of the frequency of cw mid-infrared (MIR) lasers, in particular of quantum cascade lasers. The proof of principle is performed with a quantum cascade laser at 5.4 µm, which is upconverted to 1.2 µm by sum-frequency generation in orientation-patterned GaAs with the output of a standard high-power cw 1.5 µm fiber laser. Both the 1.2 µm and the 1.5 µm waves are measured by a standard Er:fiber frequency comb. Frequency measurement at the 100 kHz-level, stabilization to sub-10 kHz level, controlled frequency tuning and long-term stability are demonstrated.

GaAs-chip-based mid-infrared supercontinuum generation.

The mid-infrared spectral region opens up new possibilities for applications such as molecular spectroscopy with high spatial and frequency resolution. For example, the mid-infrared light provided by synchrotron sources has helped for early diagnosis of several pathologies. However, alternative light sources at the table-top scale would enable better access to these state-of-the-art characterizations, eventually speeding up research in biology and medicine. Mid-infrared supercontinuum generation in highly nonlinear waveguides pumped by compact fiber lasers represents an appealing alternative to synchrotrons. Here, we introduce orientation-patterned gallium arsenide waveguides as a new versatile platform for mid-infrared supercontinuum generation. Waveguides and fiber-based pump lasers are optimized in tandem to allow for the group velocities of the signal and the idler waves to match near the degeneracy point. This configuration exacerbates supercontinuum generation from 4 to 9 µm when waveguides are pumped at 2750 nm with few-nanojoule energy pulses. The brightness of the novel mid-infrared source exceeds that of the third-generation synchrotron source by a factor of 20. We also show that the nonlinear dynamics is strongly influenced by the choice of waveguide and laser parameters, thus offering an additional degree of freedom in tailoring the spectral profile of the generated light. Such an approach then opens new paths for high-brightness mid-infrared laser sources development for high-resolution spectroscopy and imaging. Furthermore, thanks to the excellent mechanical and thermal properties of the waveguide material, further power scaling seems feasible, allowing for the generation of watt-level ultra-broad frequency combs in the mid-infrared.

Integrating crops and livestock in subtropical agricultural systems.

As the demand for livestock products increases, and is expected to continue to increase over the next few decades, especially in developing countries, smallholder mixed systems are becoming more intensive. However, with limited land and water resources and concern about the environmental impact of agricultural practices and climate change, the challenge is to find ways of increasing productivity that do not compromise household food security, but rather increase incomes equitably and sustain or enhance the natural resource base. In developed countries there has been increased specialisation of crop and livestock production. In contrast, the majority of livestock in developing countries is kept in mixed crop/livestock systems. Crops (cereal grains and pulses) and crop residues provide the basis of the diet for animals, e.g. cereal straw fed to dairy cattle or sweet potato vines fed to pigs. Animal manure can provide significant nutrient inputs to crops. Water productivity is higher in mixed crop/livestock systems compared with growing crops alone. Mixed systems allow for a more flexible and profitable use of family labour where employment opportunities are limited. They also spread risks across several enterprises, a consideration in smallholder systems that may become even more important under certain climate change scenarios. Integrated crop/livestock systems can play a significant role in improving global food security but will require appropriate technological developments, institutional arrangements and supportive policy environments if they are to fulfil that potential in the coming decades.

High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation.

Plant height (PH) is an essential trait in the screening of most crops. While in crops such as wheat, medium stature helps reduce lodging, tall plants are preferred to increase total above-ground biomass. PH is an easy trait to measure manually, although it can be labor-intense depending on the number of plots. There is an increasing demand for alternative approaches to estimate PH in a higher throughput mode. Crop surface models (CSMs) derived from dense point clouds generated via aerial imagery could be used to estimate PH. This study evaluates PH estimation at different phenological stages using plot-level information from aerial imaging-derived 3D CSM in wheat inbred lines during two consecutive years. Multi-temporal and high spatial resolution images were collected by fixed-wing (P l a t F W ) and multi-rotor (P l a t M R ) unmanned aerial vehicle (UAV) platforms over two wheat populations (50 and 150 lines). The PH was measured and compared at four growth stages (GS) using ground-truth measurements (PHground) and UAV-based estimates (PHaerial). The CSMs generated from the aerial imagery were validated using ground control points (GCPs) as fixed reference targets at different heights. The results show that PH estimations using P l a t F W were consistent with those obtained from P l a t M R , showing some slight differences due to image processing settings. The GCPs heights derived from CSM showed a high correlation and low error compared to their actual heights (R 2 ≥ 0.90, RMSE ≤ 4 cm). The coefficient of determination (R 2) between PHground and PHaerial at different GS ranged from 0.35 to 0.88, and the root mean square error (RMSE) from 0.39 to 4.02 cm for both platforms. In general, similar and higher heritability was obtained using PHaerial across different GS and years and ranged according to the variability, and environmental error of the PHground observed (0.06-0.97). Finally, we also observed high Spearman rank correlations (0.47-0.91) and R 2 (0.63-0.95) of PHaerial adjusted and predicted values against PHground values. This study provides an example of the use of UAV-based high-resolution RGB imagery to obtain time-series estimates of PH, scalable to tens-of-thousands of plots, and thus suitable to be applied in plant wheat breeding trials.

Maize intercropping in the milpa system. Diversity, extent and importance for nutritional security in the Western Highlands of Guatemala.

We present an assessment of the extent, diversity, and nutritional contribution of the milpa through a quantitative analysis of data from a survey conducted in 989 small scale farm households in the Western Highlands of Guatemala (WHG). The milpa is a traditional agricultural system in which maize is intercropped with other species, such as common beans, faba beans, squashes or potatoes. Our study shows that more than two-thirds of the 1,205 plots recorded were under the milpa system, with a great diversity of crop combinations. As shown with the 357 plots for which specific yields were available, milpa systems present higher total productivity than monocropped maize, expressed as total energy yield of the harvested crops in the respective system, and were also better at providing the recommended daily allowances of fourteen essential nutrients, based on a Potential Nutrient Adequacy (PNA) indicator. Maize-bean-potato, maize-potato, and maize-bean-faba intercrops had the highest PNAs, and monocropped maize, the lowest. These results support the implementation of milpa systems tailored to different agro-ecologies in order to improve nutrition in the WHG and a variety of similar regions.

Increased ranking change in wheat breeding under climate change.

The International Maize and Wheat Improvement Center develops and annually distributes elite wheat lines to public and private breeders worldwide. Trials have been created in multiple sites over many years to assess the lines' performance for use in breeding and release as varieties, and to provide iterative feedback on refining breeding strategies1. The collaborator test sites are experiencing climate change, with new implications for how wheat genotypes are bred and selected2. Using a standard quantitative genetic model to analyse four International Maize and Wheat Improvement Center global spring wheat trial datasets, we examine how genotype-environment interactions have changed over recent decades. Notably, crossover interactions-a critical indicator of changes in the ranking of cultivar performance in different environments-have increased over time. Climatic factors explained over 70% of the year-to-year variability in crossover interactions for yield. Yield responses of all lines in trial environments from 1980 to 2018 revealed that climate change has increased the ranking change in breeding targeted to favourable environments by ~15%, while it has maintained or reduced the ranking change in breeding targeted to heat and drought stress by up to 13%. Genetic improvement has generally increased crossover interactions, particularly for wheat targeted to high-yielding environments. However, the latest wheat germplasm developed under heat stress was better adapted and more stable, partly offsetting the increase in ranking changes under the warmer climate.

Application of Remote Sensing for Phenotyping Tar Spot Complex Resistance in Maize.

Tar spot complex (TSC), caused by at least two fungal pathogens, Phyllachora maydis and Monographella maydis, is one of the major foliar diseases of maize in Central and South America. P. maydis was also detected in the United States of America in 2015 and since then the pathogen has spread in the maize growing regions of the country. Although remote sensing (RS) techniques are increasingly being used for plant phenotyping, they have not been applied to phenotyping TSC resistance in maize. In this study, several multispectral vegetation indices (VIs) and thermal imaging of maize plots under disease pressure and disease-free conditions were tested using an unmanned aerial vehicle (UAV) over two crop seasons. A strong relationship between grain yield, a vegetative index (MCARI2), and canopy temperature was observed under disease pressure. A strong relationship was also observed between the area under the disease progress curve of TSC and three vegetative indices (RDVI, MCARI1, and MCARI2). In addition, we demonstrated that TSC could cause up to 58% yield loss in the most susceptible maize hybrids. Our results suggest that the RS techniques tested in this study could be used for high throughput phenotyping of TSC resistance and potentially for other foliar diseases of maize. This may help reduce the cost and time required for the development of improved maize germplasm. Challenges and opportunities in the use of RS technologies for disease resistance phenotyping are discussed.

Multi-Temporal and Spectral Analysis of High-Resolution Hyperspectral Airborne Imagery for Precision Agriculture: Assessment of Wheat Grain Yield and Grain Protein Content.

This study evaluates the potential of high resolution hyperspectral airborne imagery to capture within-field variability of durum wheat grain yield (GY) and grain protein content (GPC) in two commercial fields in the Yaqui Valley (northwestern Mexico). Through a weekly/biweekly airborne flight campaign, we acquired 10 mosaics with a micro-hyperspectral Vis-NIR imaging sensor ranging from 400-850 nanometres (nm). Just before harvest, 114 georeferenced grain samples were obtained manually. Using spectral exploratory analysis, we calculated narrow-band physiological spectral indices-normalized difference spectral index (NDSI) and ratio spectral index (RSI)-from every single hyperspectral mosaic using complete two by two combinations of wavelengths. We applied two methods for the multi-temporal hyperspectral exploratory analysis: (a) Temporal Principal Component Analysis (tPCA) on wavelengths across all images and (b) the integration of vegetation indices over time based on area under the curve (AUC) calculations. For GY, the best R2 (0.32) were found using both the spectral (NDSI-Ri, 750 to 840 nm and Rj, ±720-736 nm) and the multi-temporal AUC exploratory analysis (EVI and OSAVI through AUC) methods. For GPC, all exploratory analysis methods tested revealed (a) a low to very low coefficient of determination (R2 ≤ 0.21), (b) a relatively low overall prediction error (RMSE: 0.45-0.49%), compared to results from other literature studies, and (c) that the spectral exploratory analysis approach is slightly better than the multi-temporal approaches, with early season NDSI of 700 with 574 nm and late season NDSI of 707 with 523 nm as the best indicators. Using residual maps from the regression analyses of NDSIs and GPC, we visualized GPC within-field variability and showed that up to 75% of the field area could be mapped with relatively good predictability (residual class: -0.25 to 0.25%), therefore showing the potential of remote sensing imagery to capture the within-field variation of GPC under conventional agricultural practices.

Selection for earlier flowering crop associated with climatic variations in the Sahel.

Climate changes will have an impact on food production and will require costly adaptive responses. Adapting to a changing environment will be particularly challenging in sub-Saharan Africa where climate change is expected to have a major impact. However, one important phenomenon that is often overlooked and is poorly documented is the ability of agro-systems to rapidly adapt to environmental variations. Such an adaptation could proceed by the adoption of new varieties or by the adaptation of varieties to a changing environment. In this study, we analyzed these two processes in one of the driest agro-ecosystems in Africa, the Sahel. We performed a detailed study in Niger where pearl millet is the main crop and covers 65% of the cultivated area. To assess how the agro-system is responding to recent recurrent drought, we analyzed samples of pearl millet landraces collected in the same villages in 1976 and 2003 throughout the entire cultivated area of Niger. We studied phenological and morphological differences in the 1976 and 2003 collections by comparing them over three cropping seasons in a common garden experiment. We found no major changes in the main cultivated varieties or in their genetic diversity. However, we observed a significant shift in adaptive traits. Compared to the 1976 samples, samples collected in 2003 displayed a shorter lifecycle, and a reduction in plant and spike size. We also found that an early flowering allele at the PHYC locus increased in frequency between 1976 and 2003. The increase exceeded the effect of drift and sampling, suggesting a direct effect of selection for earliness on this gene. We conclude that recurrent drought can lead to selection for earlier flowering in a major Sahelian crop. Surprisingly, these results suggest that diffusion of crop varieties is not the main driver of short term adaptation to climatic variation.

Diversity of wild and cultivated pearl millet accessions (Pennisetum glaucum [L.] R. Br.) in Niger assessed by microsatellite markers.

Genetic diversity of crop species in sub-Sahelian Africa is still poorly documented. Among such crops, pearl millet is one of the most important staple species. In Niger, pearl millet covers more than 65% of the total cultivated area. Analyzing pearl millet genetic diversity, its origin and its dynamics is important for in situ and ex situ germplasm conservation and to increase knowledge useful for breeding programs. We developed new genetic markers and a high-throughput technique for the genetic analysis of pearl millet. Using 25 microsatellite markers, we analyzed genetic diversity in 46 wild and 421 cultivated accessions of pearl millet in Niger. We showed a significantly lower number of alleles and lower gene diversity in cultivated pearl millet accessions than in wild accessions. This result contrasts with a previous study using iso-enzyme markers showing similar genetic diversity between cultivated and wild pearl millet populations. We found a strong differentiation between the cultivated and wild groups in Niger. Analyses of introgressions between cultivated and wild accessions showed modest but statistically supported evidence of introgressions. Wild accessions in the central region of Niger showed introgressions of cultivated alleles. Accessions of cultivated pearl millet showed introgressions of wild alleles in the western, central, and eastern parts of Niger.