Climate and disease: tackling coffee brown-eye spot with advanced forecasting models

BACKGROUND

Climate influences the interaction between pathogens and their hosts significantly. This is particularly evident in the coffee industry, where fungal diseases like Cercospora coffeicola, causing brown-eye spot, can reduce yields drastically. This study focuses on forecasting coffee brown-eye spot using various models that incorporate agrometeorological data, allowing for predictions at least 1 week prior to the occurrence of disease. Data were gathered from eight locations across São Paulo and Minas Gerais, encompassing the South and Cerrado regions of Minas Gerais state. In the initial phase, various machine learning (ML) models and topologies were calibrated to forecast brown-eye spot, identifying one with potential for advanced decision-making. The top-performing models were then employed in the next stage to forecast and spatially project the severity of brown-eye spot across 2681 key Brazilian coffee-producing municipalities. Meteorological data were sourced from NASA's Prediction of Worldwide Energy Resources platform, and the Penman-Monteith method was used to estimate reference evapotranspiration, leading to a Thornthwaite and Mather water-balance calculation. Six ML models - K-nearest neighbors (KNN), artificial neural network multilayer perceptron (MLP), support vector machine (SVM), random forests (RF), extreme gradient boosting (XGBoost), and gradient boosting regression (GradBOOSTING) - were employed, considering disease latency to time define input variables.

RESULTS

These models utilized climatic elements such as average air temperature, relative humidity, leaf wetness duration, rainfall, evapotranspiration, water deficit, and surplus. The XGBoost model proved most effective in high-yielding conditions, demonstrating high precision and accuracy. Conversely, the SVM model excelled in low-yielding scenarios. The incidence of brown-eye spot varied noticeably between high- and low-yield conditions, with significant regional differences observed. The accuracy of predicting brown-eye spot severity in coffee plantations depended on the biennial production cycle. High-yielding trees showed superior results with the XGBoost model (R2  = 0.77, root mean squared error, RMSE = 10.53), whereas the SVM model performed better under low-yielding conditions (precision 0.76, RMSE = 12.82).

CONCLUSION

The study's application of agrometeorological variables and ML models successfully predicted the incidence of brown-eye spot in coffee plantations with a 7 day lead time, illustrating that they were valuable tools for managing this significant agricultural challenge. © 2024 Society of Chemical Industry.

Upland rice varietal mixtures in Madagascar: evaluating the effects of varietal interaction on crop performance

Introduction

Rice plays a critical role in human livelihoods and food security. However, its cultivation requires inputs that are not accessible to all farming communities and can have negative effects on ecosystems. simultaneously, ecological research demonstrates that biodiversity management within fields contributes to ecosystem functioning.

Methods

This study aims to evaluate the mixture effect of four functionally distinct rice varieties in terms of characteristics and agronomic performance and their spatial arrangement on the upland rice performance in the highlands of Madagascar. The study was conducted during the 2021-2022 rainfall season at two close sites in Madagascar. Both site differ from each other's in soil properties and soil fertility management. The experimental design at each site included three modalities: i) plot composition, i.e., pure stand or binary mixture; ii) the balance between the varieties within a mixture; iii) and for the balanced mixture (50% of each variety), the spatial arrangement, i.e., row or checkerboard patterns. Data were collected on yields (grain and biomass), and resistance to Striga asiatica infestation, Pyricularia oryzea and bacterial leaf blight (BLB) caused by Xanthomonas oryzae-pv from each plot.

Results and discussion

Varietal mixtures produced significantly higher grain and biomass yields, and significantly lower incidence of Pyricularia oryzea compared to pure stands. No significant differences were observed for BLB and striga infestation. These effects were influenced by site fertility, the less fertilized site showed stronger mixture effects with greater gains in grain yield (60%) and biomass yield (42%). The most unbalanced repartition (75% and 25% of each variety) showed the greatest mixture effect for grain yield at both sites, with a strong impact of the varietal identity within the plot. The mixture was most effective when EARLY_MUTANT_IAC_165 constituted 75% of the density associated with other varieties at 25% density. The assessment of the net effect ratio of disease, an index evaluating the mixture effect in disease reduction, indicated improved disease resistance in mixtures, regardless of site conditions. Our study in limited environments suggests that varietal mixtures can enhance rice productivity, especially in low-input situations. Further research is needed to understand the ecological mechanisms behind the positive mixture effect.

Influence of organic plant breeding on the rhizosphere microbiome of common bean (Phaseolus vulgaris L.)

Introduction

We now recognize that plant genotype affects the assembly of its microbiome, which in turn, affects essential plant functions. The production system for crop plants also influences the microbiome composition, and as a result, we would expect to find differences between conventional and organic production systems. Plant genotypes selected in an organic regime may host different microbiome assemblages than those selected in conventional environments. We aimed to address these questions using recombinant inbred populations of snap bean that differed in breeding history.

Methods

Rhizosphere microbiomes of conventional and organic common beans (Phaseolus vulgaris L.) were characterized within a long-term organic research site. The fungal and bacterial communities were distinguished using pooled replications of 16S and ITS amplicon sequences, which originated from rhizosphere samples collected between flowering and pod set.

Results

Bacterial communities significantly varied between organic and conventional breeding histories, while fungal communities varied between breeding histories and parentage. Within the organically-bred populations, a higher abundance of a plant-growth-promoting bacteria, Arthrobacter pokkalii, was identified. Conventionally-bred beans hosted a higher abundance of nitrogen-fixing bacteria that normally do not form functional nodules with common beans. Fungal communities in the organically derived beans included more arbuscular mycorrhizae, as well as several plant pathogens.

Discussion

The results confirm that the breeding environment of crops can significantly alter the microbiome community composition of progeny. Characterizing changes in microbiome communities and the plant genes instrumental to these changes will provide essential information about how future breeding efforts may pursue microbiome manipulation.

Leguminous Seedborne Pathogens: Seed Health and Sustainable Crop Management

Pulses have gained popularity over the past few decades due to their use as a source of protein in food and their favorable impact on soil fertility. Despite being essential to modern agriculture, these species face a number of challenges, such as agronomic crop management and threats from plant seed pathogens. This review's goal is to gather information on the distribution, symptomatology, biology, and host range of seedborne pathogens. Important diagnostic techniques are also discussed as a part of a successful process of seed health certification. Additionally, strategies for sustainable control are provided. Altogether, the data collected are suggested as basic criteria to set up a conscious laboratory approach.

"How sweet are your strawberries?": Predicting sugariness using non-destructive and affordable hardware

Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to obtain quality estimations in a consistent and non-destructive manner. The majority of research on fruit quality measurements analyzed fruits in the lab with uniform data collection. However, it is laborious and expensive to scale up to the level of the whole yield. The "harvest-first, analysis-second" method also comes too late to decide to adjust harvesting schedules. In this research, we validated our hypothesis of using in-field data acquirable via commodity hardware to obtain acceptable accuracies. The primary instance that the research concerns is the sugariness of strawberries, described by the juice's total soluble solid (TSS) content (unit: °Brix or Brix). We benchmarked the accuracy of strawberry Brix prediction using convolutional neural networks (CNN), variational autoencoders (VAE), principal component analysis (PCA), kernelized ridge regression (KRR), support vector regression (SVR), and multilayer perceptron (MLP), based on fusions of image data, environmental records, and plant load information, etc. Our results suggest that: (i) models trained by environment and plant load data can perform reliable prediction of aggregated Brix values, with the lowest RMSE at 0.59; (ii) using image data can further supplement the Brix predictions of individual fruits from (i), from 1.27 to as low up to 1.10, but they by themselves are not sufficiently reliable.

Predicting common bean (Phaseolus vulgaris) productivity according to Rhizoctonia root and stem rot and weed development at field plot scale

INTRODUCTION

A two-year research trial was conducted to evaluate progression of common bean (Phaseolus vulgaris) growth, Rhizoctonia root rot and weed development in association with productivity when treated with different cultivars, planting dates and weed control methods in north-western part of Iran.

METHODS

To determine the best descriptors, six standard curves were examined to model development of bean dry matter, Rhizoctonia root rot incidence, and weed density during two growing seasons across 256 field plots. Exponential and linear-by-linear models were fitted to bean-disease-weed progression data, and then model parameters representing over-season progress curve elements were used in multivariate regression analyses to estimate bean production.

RESULTS AND DISCUSSION

Furthermore, using herbicides (Imazethapyr and Trifluralin) restricted weed density by 28% in early (mid-spring) and 42% in late (late spring to early summer) plantings. Late plantings of two bean cultivars decreased disease progress up to 36% for herbicide use, hand-weeding and control. Although bean dry matter, pod and seed production for herbicide use and hand-weeding treatments were 6-17% greater than control, late planting improved productivity in control by 10-24%. Findings suggested that late planting of bean improved efficiency of herbicides to control weeds. Late planting also restricted Rhizoctonia root rot progress and thus, improved bean yield. There were significant correlations between bean-disease-weed development descriptors. According to principal component analysis, bean-disease-productivity-weed variables accounted for 80% of total data variance. Such information extends our understanding of bean-disease-weed progress in interaction with planting date to develop more effective and sustainable integrated Rhizoctonia management programs.

The effects of ants on pest control: a meta-analysis

Environmental impacts of conventional agriculture have generated interest in sustainable agriculture. Biological pest control is a fundamental tool, and ants are key players providing ecological services, as well as some disservices. We have used a meta-analytical approach to investigate the contribution of ants to biological control, considering their effects on pest and natural enemy abundance, plant damage and crop yield. We also evaluated whether the effects of ants are modulated by traits of ants, pests and other natural enemies, as well as by field size, crop system and experiment duration. Overall (considering all meta-analyses), from 52 studies on 17 different crops, we found that ants decrease the abundance of non-honeydew-producing pests, decrease plant damage and increase crop yield (services). In addition, ants decrease the abundance of natural enemies, mainly the generalist ones, and increase honeydew-producing pest abundance (disservices). We show that the pest control and plant protection provided by ants are boosted in shaded crops compared to monocultures. Furthermore, ants increase crop yield in shaded crops, and this effect increases with time. Finally, we bring new insights such as the importance of shaded crops to ant services, providing a good tool for farmers and stakeholders considering sustainable farming practices.

Capability of the TrueColor Sensor Array for Determining the Nitrogen Supply in Winter Barley (Hordeum vulgare L.)

In agriculture, efforts are being made to reduce pesticides and fertilizers because of the possible negative environmental impacts, high costs, political requirements, and declining social acceptance. With precision farming, significant savings can be achieved by the site-specific application of fertilizers. In contrast to currently available single sensors and camera-based systems, arrays or line sensors provide a suitable spatial resolution without requiring complex signal processing and promise significant potential regarding price and precision. Such systems comprise a cost-effective and compact unit that can be extended to any working width by cascading into arrays. In this study, experiments were performed to evaluate the applicability of a TrueColor sensor array in monitoring the nitrogen supply of winter barley during its growth. This sensor is based on recording the reflectance values in various channels of the CIELab color space: luminosity, green-red, and blue-yellow. The unique selling point of this sensor is the detection of luminosity because only the CIELab color space provides this opportunity. Strong correlations were found between the different reflection channels and the nitrogen level (R² = 0.959), plant coverage (R² = 0.907), and fresh mass yield (R² = 0.866). The fast signal processing allows this sensor to meet stringent demands for the operating speed, spatial resolution, and price structure.

Amino Acids in Rice Grains and Their Regulation by Polyamines and Phytohormones

Rice is one of the most important food crops in the world, and amino acids in rice grains are major nutrition sources for the people in countries where rice is the staple food. Phytohormones and plant growth regulators play vital roles in regulating the biosynthesis of amino acids in plants. This paper reviewed the content and compositions of amino acids and their distribution in different parts of ripe rice grains, and the biosynthesis and metabolism of amino acids and their regulation by polyamines (PAs) and phytohormones in filling grains, with a focus on the roles of higher PAs (spermidine and spermine), ethylene, and brassinosteroids (BRs) in this regulation. Recent studies have shown that higher PAs and BRs (24-epibrassinolide and 28-homobrassinolide) play positive roles in mediating the biosynthesis of amino acids in rice grains, mainly by enhancing the activities of the enzymes involved in amino acid biosynthesis and sucrose-to-starch conversion and maintaining redox homeostasis. In contrast, ethylene may impede amino acid biosynthesis by inhibiting the activities of the enzymes involved in amino acid biosynthesis and elevating reactive oxygen species. Further research is needed to unravel the temporal and spatial distribution characteristics of the content and compositions of amino acids in the filling grain and their relationship with the content and compositions of amino acids in different parts of a ripe grain, to elucidate the cross-talk between or among phytohormones in mediating the anabolism of amino acids, and to establish the regulation techniques for promoting the biosynthesis of amino acids in rice grains.

Carbon allocation strategies and water uptake in young grafted and own-rooted hazelnut (Corylus avellana L.) cultivars

In this study, grafted and own-rooted young hazelnut plants of three high-quality cultivars were cultivated in Central Italy to investigate possible differences in growth, fruit and flower production, and physiological processes encompassing water uptake, photosynthetic variables and non-structural carbohydrate allocation. Stable isotopes and photosynthetic measurements were used to study carbon and water fluxes in plants. For the first time, an ecophysiological study was carried out to understand the seasonal growth dynamics of grafted plants in comparison with own-rooted plants. The own-rooted hazelnuts showed rapid above-ground development with large canopy volume, high amount of sprouts and earlier yield. The grafted plants showed greater below-ground development with lower canopy volumes and lower yield. However, later, the higher growth rates of the canopy led these plants to achieve the same size as that of the own-rooted hazelnuts and to enter the fruit production phase. Different seasonal behaviour in root water uptake and leaf photosynthesis-related variables was detected between the two types of plants. The grafted plants showed root development that allowed deeper water uptake than that of the own-rooted hazelnuts. Moreover, the grafted plants were characterized by a higher accumulation of carbohydrate reserves in their root tissues and by higher stomatal reactivity, determining significant plasticity in response to seasonal thermal variations.

Effects of climate change and crop management on changes in rice phenology in China from 1981 to 2010

BACKGROUND

Crop phenology change is co-determined by climate change and adaptation strategies, such as crop management, but their combined and isolated impacts on rice phenology are still unclear. Quantifying the impacts and identifying the main contributors are critical to food security under climate change. Thus we distinguished and quantified the relative contribution of climate change and crop management to rice (Oryza sativa L.) phenological changes in China from 1981 to 2010, using a first-difference multivariate regression method.

RESULTS

Rice phenology has changed over the past 30 years in China. The mean length of the phenological stage from emergence to transplanting was shortened, whereas the mean length of the stage from transplanting to heading, from heading to maturity, was prolonged. The relative contribution of crop management was greater than that of climate change for single and late rice, which took up over 90% of the total change in certain phenology stages. Among the climatic factors, temperature was the dominant contributor, which accounted for more than 50% of the change in rice phenology. The stage from transplanting to heading of early rice and late rice had strongly negative sensitivities to increasing temperature.

CONCLUSIONS

Crop management has offset the adverse effects of climate change on single and early rice phenology in China over the past 30 years to some extent, while further adaptation measures such as adjusting sowing date, shifting rice varieties, applying nitrogen fertilizer and irrigation should be applied to late rice in southern China, especially in a warmer future. © 2021 Society of Chemical Industry.

Growth Rate, Dry Matter Accumulation, and Partitioning in Soybean (Glycine max L.) in Response to Defoliation under High-Rainfall Conditions

The frequency of heavy rains is increasing with climate change in regions that already have high annual rainfall (i.e., Sichuan, China). Crop response under such high-rainfall conditions is to increase dry matter investment in vegetative parts rather than reproductive parts. In the case of soybean, leaf redundancy prevails, which reduces the light transmittance and seed yield. However, moderate defoliation of soybean canopy could reduce leaf redundancy and improve soybean yield, especially under high-rainfall conditions. Therefore, the effects of three defoliation treatments (T1, 15%; T2, 30%; and T3, 45% defoliation from the top of the soybean canopy; defoliation treatments were applied at the pod initiation stage of soybean) on the growth and yield parameters of soybean were evaluated through field experiments in the summer of 2017, 2018, and 2019. All results were compared with nondefoliated soybean plants (CK) under high-rainfall conditions. Compared with CK, treatment T1 significantly (p < 0. 05) improved the light transmittance and photosynthetic rate of soybean. Consequently, the leaf greenness was enhanced by 22%, which delayed the leaf senescence by 13% at physiological maturity. Besides, compared to CK, soybean plants achieved the highest values of crop growth rate in T1, which increased the total dry matter accumulation (by 6%) and its translocation to vegetative parts (by 4%) and reproductive parts (by 8%) at physiological maturity. This improved soybean growth and dry matter partitioning to reproductive parts in T1 enhanced the pod number (by 23%, from 823.8 m-2 in CK to 1012.7 m-2 in T1) and seed number (by 11%, from 1181.4 m-2 in CK to 1311.7 m-2 in T1), whereas the heavy defoliation treatments considerably decreased all measured growth and yield parameters. On average, treatment T1 increased soybean seed yield by 9% (from 2120.2 kg ha-1 in CK to 2318.2 kg ha-1 in T1), while T2 and T3 decreased soybean seed yield by 19% and 33%, respectively, compared to CK. Overall, these findings indicate that the optimum defoliation, i.e., T1 (15% defoliation), can decrease leaf redundancy and increase seed yield by reducing the adverse effects of mutual shading and increasing the dry matter translocation to reproductive parts than vegetative parts in soybean, especially under high-rainfall conditions. Future studies are needed to understand the internal signaling and the molecular mechanism controlling and regulating dry matter production and partitioning in soybean, especially from the pod initiation stage to the physiological maturity stage.

The optimization of wheat yield through adaptive crop management in a changing climate: evidence from China

BACKGROUND

Adaptive crop management is critical to food security in a changing climate, but the respective contributions of climate change and crop management to yields remain unclear. Thus, we distinguished and quantified the respective contribution of climate change and crop management on wheat yield between 1981 and 2018 in China, using first-difference multivariate regression model.

RESULTS

Wheat production in China has increased over the past four decades. Under the sole impact of climate change, wheat yield generally decreased (-5.45 to +1.09% decade^-1 ). Crop management increased the wheat yield from 7.11 to 19.94% decade^-1 . Sensitivities of wheat yield to climatic variables (average temperature, accumulated sunshine hours, accumulated precipitation) were spatially heterogeneous; notably, in spring-wheat planting areas, wheat yield was more susceptible to the negative impact of warming. In terms of relative contribution, the contribution of climate change to spring wheat yield was -24.08% to -5.41%, and the contribution to winter wheat was -4.98% to +34.69%. Crop management had a positive contribution to all wheat-growing areas (65.31-96.84%).

CONCLUSION

Crop management had a greater effect on wheat yield than climate change did. Among the three climatic variables investigated, average temperature had the dominant effect on wheat yield change; the impact of precipitation was minimal but mostly negative. The results provide insight regarding the contribution of climate change and crop management to wheat yield; adaptation measures may be more effective in planting areas where crop management contributes more, which will help stakeholders optimize crop management and adaptation strategies. © 2020 Society of Chemical Industry.

Machine Learning in Agriculture: A Comprehensive Updated Review

The digital transformation of agriculture has evolved various aspects of management into artificial intelligent systems for the sake of making value from the ever-increasing data originated from numerous sources. A subset of artificial intelligence, namely machine learning, has a considerable potential to handle numerous challenges in the establishment of knowledge-based farming systems. The present study aims at shedding light on machine learning in agriculture by thoroughly reviewing the recent scholarly literature based on keywords' combinations of "machine learning" along with "crop management", "water management", "soil management", and "livestock management", and in accordance with PRISMA guidelines. Only journal papers were considered eligible that were published within 2018-2020. The results indicated that this topic pertains to different disciplines that favour convergence research at the international level. Furthermore, crop management was observed to be at the centre of attention. A plethora of machine learning algorithms were used, with those belonging to Artificial Neural Networks being more efficient. In addition, maize and wheat as well as cattle and sheep were the most investigated crops and animals, respectively. Finally, a variety of sensors, attached on satellites and unmanned ground and aerial vehicles, have been utilized as a means of getting reliable input data for the data analyses. It is anticipated that this study will constitute a beneficial guide to all stakeholders towards enhancing awareness of the potential advantages of using machine learning in agriculture and contributing to a more systematic research on this topic.

Sustainable Wheat Production and Food Security of Domestic Wheat in Tajikistan: Implications of Seed Health and Protein Quality

Staple crop yield, quality and sustainable production are critical for domestic food security in developing countries. In Tajikistan, both seed-borne diseases and protein quality impair the yield and the quality of the major staple crop, wheat. Here, we used a detailed two-year survey of fields on 21 wheat-producing farms in Tajikistan, combined with lab analyses on seed health and protein quality, to investigate the presence of seed-borne diseases and bread-making quality in Tajik wheat. Seed samples were collected for the analysis of: (i) the presence of common bunt (Tilletia spp.) using the centrifuge wash test, (ii) the major pathogenic fungi on/in the seed using the agar plate test and (iii) the protein amount and size distribution using size-exclusion high-performance liquid chromatography (SE-HPLC). Field occurrence of common bunt and loose smut was generally low (3 farms in year one (14%) showed common bunt occurrence), but the presence of fungi was observed microscopically on most seed samples (on seeds from 19 out of 21 farms = 91%). Tilletia laevis was the dominant agent in common bunt (present in 19 farms compared to T. tritici present in 6 farms). Altogether, 18 different fungi were identified from seed samples by microscopy. Protein composition, measured with high-performance liquid chromatography as protein amount and size distribution (known to correlate with bread-making quality), differed significantly between samples from different farms and years, although the farm type and land elevation of the farm were not the determinants of the protein composition. The presence of certain fungi on the seed correlated significantly with the protein quality and could then have an impact on the bread-making quality of the Tajik wheat. The presence of seed-borne diseases, a low protein content and weak gluten were the characteristics of the majority of the grain samples, mostly irrespective of farm type and farmer’s knowledge. For sustainable development of the Tajik farming systems, and to strengthen the food security of the country, the knowledge of Tajik farmers needs to be increased independently of farm type; in general, plant breeding is required and certified seeds need to be made available throughout the country.

Challenges for Adoption of Integrated Pest Management (IPM): the Soybean Example

Soybean is considered one of today's most important crops. Planted on millions of hectares worldwide, the management of soybean pests usually requires large amounts of chemicals. However, a key component to meet the increasing demand for food due to the rapidly growing global population is protecting crops from pests while maintaining environmental quality through ecologically and economically sound integrated pest management (IPM) practices. Not only can IPM result in more profitable agriculture due to the reduction of pest control costs but also assures equitable, secure, sufficient, and stable flows of both food and ecosystem services. Despite those ecological and economic benefits, the vast areas of cultivated soybean as well as the convenience of spraying insecticides are encouraging the adoption of prophylactic pest control as a relatively inexpensive safeguard compared to IPM practices. Thus, in this forum, we discuss the reasons for soybean IPM not reaching its potential. We give examples of how we can revive this once successful pest management program with a focus on experiences in Brazil and the USA. We analyze IPM case studies to illustrate the need for growers to have easy and fast access to IPM information on its medium- and long-term benefits. Overall, this forum highlights the importance of IPM for agricultural sustainability including ecological and financial benefits.

Predicting the fundamental thermal niche of crop pests and diseases in a changing world: A case study on citrus greening

Predicting where crop pests and diseases can occur, both now and in the future under different climate change scenarios, is a major challenge for crop management. One solution is to estimate the fundamental thermal niche of the pest/disease to indicate where establishment is possible. Here, we develop methods for estimating and displaying the fundamental thermal niche of pests and pathogens and apply these methods to Huanglongbing (HLB), a vector-borne disease that is currently threatening the citrus industry worldwide.We derive a suitability metric based on a mathematical model of HLB transmission between tree hosts and its vector Diaphorina citri, and incorporate the effect of temperature on vectortraits using data from laboratory experiments performed at different temperatures. We validate the model using data on the historical range of HLB.Our model predicts that transmission of HLB is possible between 16 and 33°C with peak transmission at ~25°C. The greatest uncertainty in our suitability metric is associated with the mortality of the vectors at peak transmission, and fecundity at the edges of the thermal range, indicating that these parameters need further experimental work.We produce global thermal niche maps by plotting how many months each location is suitable for establishment of the pest/disease. This analysis reveals that the highest suitability for HLB occurs near the equator in large citrus-producing regions, such as Brazil and South-East Asia. Within the Northern Hemisphere, the Iberian peninsula and California are HLB suitable for up to 7 months of the year and are free of HLB currently.Policy implications. We create a thermal niche map which indicates the places at greatest risk of establishment should a crop disease or pest enter these regions. This indicates where surveillance should be focused to prevent establishment. Our mechanistic method can be used to predict new areas for Huanglongbing transmission under different climate change scenarios and is easily adapted to other vector-borne diseases and crop pests.

Lessons From 20 Years of Studies of Wheat Genotypes in Multiple Environments and Under Contrasting Production Systems

Identifying opportunities and limitations for closing yield gaps is essential for setting right the efforts dedicated to improve germplasm and agronomic practices. This study analyses genotypes × environments interaction (G × E), genetic progress, and grain yield stability under contrasting production systems. For this, we analyzed datasets obtained from three Swiss trial-networks of winter wheat that were designed to evaluate genotypes under organic farming conditions, conventional management with low-inputs (150 kg nitrogen (N) ha^-1 with no fungicide application) and conventional management with high-inputs (170 kg N ha^-1 with fungicide application). The datasets covered the periods from 1998 to 2018 for organic and conventional management with low-inputs and from 2008 to 2018 for conventional management with high-inputs. The trial-networks evaluated each year an average of 36 winter wheat genotypes that included released varieties, advanced breeding lines, and lines for registration and post-registration in Switzerland. We investigated within each trial-network the influence of years, genotypes, environments and their interactions on the total variance in grain yield and grain N concentration using variance components analyses. We further applied mixed models with regression features to dissect genetic components due to breeding efforts from non-genetic components. The genotype as a single factor or as a factor interacting with the environment or the year (G × E, G × year, and G × E × year) explained 13% (organic), 20% (conventional low-inputs), and 24% (conventional high-inputs) of the variance in grain yield, while the corresponding values for grain N concentration were 29%, 25%, and 32%. Grain yield has stagnated since 1990 for conventional systems while the trend under organic management was slightly negative. The dissection of a genetic component from the grain yield trends under conventional management showed that genetic improvements contributed with 0.58 and 0.68 t ha^-1 y^-1 with low- and high- inputs, respectively. In contrast, a significant genetic source in the grain yield trend under organic management was not detected. Therefore, breeding efforts have been less effective on the wheat productivity for organic farming conditions than for conventional ones.

Machine Learning in Agriculture: A Review

Machine learning has emerged with big data technologies and high-performance computing to create new opportunities for data intensive science in the multi-disciplinary agri-technologies domain. In this paper, we present a comprehensive review of research dedicated to applications of machine learning in agricultural production systems. The works analyzed were categorized in (a) crop management, including applications on yield prediction, disease detection, weed detection crop quality, and species recognition; (b) livestock management, including applications on animal welfare and livestock production; (c) water management; and (d) soil management. The filtering and classification of the presented articles demonstrate how agriculture will benefit from machine learning technologies. By applying machine learning to sensor data, farm management systems are evolving into real time artificial intelligence enabled programs that provide rich recommendations and insights for farmer decision support and action.

Weed Diversity Affects Soybean and Maize Yield in a Long Term Experiment in Michigan, USA

Managing production environments in ways that promote weed community diversity may enhance both crop production and the development of a more sustainable agriculture. This study analyzed data of productivity of maize (corn) and soybean in plots in the Main Cropping System Experiment (MCSE) at the W. K. Kellogg Biological Station Long-Term Ecological Research (KBS-LTER) in Michigan, USA, from 1996 to 2011. We used models derived from population ecology to explore how weed diversity, temperature, and precipitation interact with crop yields. Using three types of models that considered internal and external (climate and weeds) factors, with additive or non-linear variants, we found that changes in weed diversity were associated with changes in rates of crop yield increase over time for both maize and soybeans. The intrinsic capacity for soybean yield increase in response to the environment was greater under more diverse weed communities. Soybean production risks were greatest in the least weed diverse systems, in which each weed species lost was associated with progressively greater crop yield losses. Managing for weed community diversity, while suppressing dominant, highly competitive weeds, may be a helpful strategy for supporting long term increases in soybean productivity. In maize, there was a negative and non-additive response of yields to the interaction between weed diversity and minimum air temperatures. When cold temperatures constrained potential maize productivity through limited resources, negative interactions with weed diversity became more pronounced. We suggest that: (1) maize was less competitive in cold years allowing higher weed diversity and the dominance of some weed species; or (2) that cold years resulted in increased weed richness and prevalence of competitive weeds, thus reducing crop yields. Therefore, we propose to control dominant weed species especially in the years of low yield and extreme minimum temperatures to improve maize yields. Results of our study indicate that through the proactive management of weed diversity, it may be possible to promote both high productivity of crops and environmental sustainability.

A Novel Remote Sensing Approach for Prediction of Maize Yield Under Different Conditions of Nitrogen Fertilization

Maize crop production is constrained worldwide by nitrogen (N) availability and particularly in poor tropical and subtropical soils. The development of affordable high-throughput crop monitoring and phenotyping techniques is key to improving maize cultivation under low-N fertilization. In this study several vegetation indices (VIs) derived from Red-Green-Blue (RGB) digital images at the leaf and canopy levels are proposed as low-cost tools for plant breeding and fertilization management. They were compared with the performance of the normalized difference vegetation index (NDVI) measured at ground level and from an aerial platform, as well as with leaf chlorophyll content (LCC) and other leaf composition and structural parameters at flowering stage. A set of 10 hybrids grown under five different nitrogen regimes and adequate water conditions were tested at the CIMMYT station of Harare (Zimbabwe). Grain yield and leaf N concentration across N fertilization levels were strongly predicted by most of these RGB indices (with R (2)~ 0.7), outperforming the prediction power of the NDVI and LCC. RGB indices also outperformed the NDVI when assessing genotypic differences in grain yield and leaf N concentration within a given level of N fertilization. The best predictor of leaf N concentration across the five N regimes was LCC but its performance within N treatments was inefficient. The leaf traits evaluated also seemed inefficient as phenotyping parameters. It is concluded that the adoption of RGB-based phenotyping techniques may significantly contribute to the progress of plant breeding and the appropriate management of fertilization.

Captures of MFO-resistant Cydia pomonella adults as affected by lure, crop management system and flight

The main resistance mechanism of codling moth (Cydia pomonella) in the tree fruit area of Lleida (NE Spain) is multifunction oxidases (MFO). We studied the frequency of MFO-resistant adults captured by different lures, with and without pear ester, and flights in orchards under different crop management systems. The factor year affected codling moth MFO-resistance level, particularly in the untreated orchards, highlighting the great influence of codling moth migration on the spread of resistance in field populations. Chemical treatments and adult flight were also very important but mating disruption technique showed no influence. The second adult flight showed the highest frequency, followed by the first flight and the third flight. In untreated orchards, there were no significant differences in the frequency of MFO-resistant individuals attracted by Combo and BioLure. Red septa lures baited with pear ester (DA) captured sufficient insects only in the first generation of 2010, obtaining a significantly lower proportion of MFO-resistant adults than Combo and BioLure. In the chemically treated orchards, in 2009 BioLure caught a significantly lower proportion of MFO-resistant adults than Combo during the first and third flight, and also than DA during the first flight. No significant differences were found between the lures or flights in 2010. These results cannot support the idea of a higher attractiveness of the pear ester for MFO-resistant adults in the field but do suggest a high influence of the response to the attractant depending on the management of the orchard, particularly with regard to the use of chemical insecticides.

Understanding the Impacts of Soil, Climate, and Farming Practices on Soil Organic Carbon Sequestration: A Simulation Study in Australia

Carbon sequestration in agricultural soils has the capacity to mitigate greenhouse gas emissions, as well as to improve soil biological, physical, and chemical properties. The review of literature pertaining to soil organic carbon (SOC) dynamics within Australian grain farming systems does not enable us to conclude on the best farming practices to increase or maintain SOC for a specific combination of soil and climate. This study aimed to further explore the complex interactions of soil, climate, and farming practices on SOC. We undertook a modeling study with the Agricultural Production Systems sIMulator modeling framework, by combining contrasting Australian soils, climates, and farming practices (crop rotations, and management within rotations, such as fertilization, tillage, and residue management) in a factorial design. This design resulted in the transposition of contrasting soils and climates in our simulations, giving soil-climate combinations that do not occur in the study area to help provide insights into the importance of the climate constraints on SOC. We statistically analyzed the model's outputs to determinate the relative contributions of soil parameters, climate, and farming practices on SOC. The initial SOC content had the largest impact on the value of SOC, followed by the climate and the fertilization practices. These factors explained 66, 18, and 15% of SOC variations, respectively, after 80 years of constant farming practices in the simulation. Tillage and stubble management had the lowest impacts on SOC. This study highlighted the possible negative impact on SOC of a chickpea phase in a wheat-chickpea rotation and the potential positive impact of a cover crop in a sub-tropical climate (QLD, Australia) on SOC. It also showed the complexities in managing to achieve increased SOC, while simultaneously aiming to minimize nitrous oxide (N2O) emissions and nitrate leaching in farming systems. The transposition of contrasting soils and climates in our simulations revealed the importance of the climate constraints on SOC.

A meta-analysis of responses of canopy photosynthetic conversion efficiency to environmental factors reveals major causes of yield gap

Improving plant energy conversion efficiency (εc) is crucial for increasing food and bioenergy crop production and yields. Using a meta-analysis, the effects of greenhouse gases, weather-related stresses projected to intensify due to climate change, and management practices including inputs, shading, and intercropping on εc were statistically quantified from 140 published studies to identify where improvements would have the largest impact on closing yield gaps. Variation in the response of εc to treatment type and dosage, plant characteristics, and growth conditions were also examined. Significant mean increases in εc were caused by elevated [CO2] (20%), shade (18%), and intercropping (15%). εc increased curvilinearly up to 55% with nitrogen additions whereas phosphorus application was most beneficial at low levels. Significant decreases in εc of -8.4% due to elevated [O3], -16.8% due to water stress, and -6.5% due to foliar damage were found. A non-significant decrease in εc of -17.3% was caused by temperature stress. These results identify the need to engineer greater stress tolerance and enhanced responses to positive factors such as [CO2] and nitrogen to improve average yields and yield potential. Optimizing management strategies will also enhance the benefits possible with intercropping, shade, and pest resilience. To determine optimal practices for εc improvement, further studies should be conducted in the field since several responses were exaggerated by non-field experimental conditions.

Geographic differences in patterns of genetic differentiation among bitter and sweet manioc (Manihot esculenta subsp. esculenta; Euphorbiaceae)

PREMISE OF THE STUDY

Manioc (Manihot esculenta subsp. esculenta), one of the most important tropical food crops, is commonly divided according to cyanide content into two use-categories, "sweet" and "bitter." While bitter and sweet varieties are genetically differentiated at the local scale, whether this differentiation is consistent across continents is yet unknown. •

METHODS

Using eight microsatellite loci, we genotyped 522 manioc samples (135 bitter and 387 sweet) from Ecuador, French Guiana, Cameroon, Gabon, Ghana, and Vanuatu. Genetic differentiation between use-categories was assessed using double principal coordinate analyses (DPCoA) with multivariate analysis of variance (MANOVA) and Jost's measure of estimated differentiation (D(est)). Genetic structure was analyzed using Bayesian clustering analysis. •

KEY RESULTS

Manioc neutral genetic diversity was high in all sampled regions. Sweet and bitter manioc landraces are differentiated in South America but not in Africa. Correspondingly, bitter and sweet manioc samples share a higher proportion of neutral alleles in Africa than in South America. We also found seven clones classified by some farmers as sweet and by others as bitter. •

CONCLUSIONS

Lack of differentiation in Africa is most likely due to postintroduction hybridization between bitter and sweet manioc. Inconsistent transfer from South America to Africa of ethnobotanical knowledge surrounding use-category management may contribute to increased hybridization in Africa. Investigating this issue requires more data on the variation in cyanogenesis in roots within and among manioc populations and how manioc diversity is managed on the farm.

Plant ionome diagnosis using sound balances: case study with mango (Mangifera Indica)

Plant ionomes and soil nutrients are commonly diagnosed in agronomy using concentration and nutrient ratio ranges. However, both diagnoses are biased by redundancy of information, subcompositional incoherence and non-normal distribution inherent to compositional data, potentially leading to conflicting results and wrong inferences. Our objective was to present an unbiased statistical approach of plant nutrient diagnosis using a balance concept and mango (Mangifera indica) as test crop. We collected foliar samples at flowering stage in 175 mango orchards. The ionomes comprised 11 nutrients (S, N, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe). Traditional multivariate methods were found to be biased. Ionomes were thus represented by unbiased balances computed as isometric log ratios (ilr). Soil fertility attributes (pH and bioavailable nutrients) were transformed into balances to conduct discriminant analysis. The orchards differed more from genotype than soil nutrient signatures. A customized receiver operating characteristic (ROC) iterative procedure was developed to classify tissue ionomes between balanced/misbalanced and high/low-yielders. The ROC partitioning procedure showed that the critical Mahalanobis distance of 4.08 separating balanced from imbalanced specimens about yield cut-off of 128.5 kg fruit tree(-1) proved to be a fairly informative test (area under curve = 0.84-0.92). The [P | N,S] and [Mn | Cu,Zn] balances were found to be potential sources of misbalance in the less productive orchards, and should thus be further investigated in field experiments. We propose using a coherent pan balance diagnostic method with median ilr values of top yielders centered at fulcrums of a mobile and the critical Mahalanobis distance as a guide for global nutrient balance. Nutrient concentrations in weighing pans assisted appreciating nutrients as relative shortage, adequacy or excess in balances.

Rice Pest Constraints in Tropical Asia: Characterization of Injury Profiles in Relation to Production Situations

A protocol for characterizing patterns of rice cropping practices and injuries due to pathogens, insects, and weeds was developed and used in six sites in tropical Asia covering a wide range of environments where lowland rice is cultivated. The data collected in a total population of 456 individual farmers' fields were combined to site-specific weather data and analyzed using non-parametric multivariate techniques: cluster analyses with chi-square distance and correspondence analyses. The main results are: (i) patterns of cropping practices that are common across sites can be identified; (ii) conversely, injury profiles that are common across sites can be determined; (iii) patterns of cropping practices and injury profiles are strongly associated at the regional scale; (iv) weather patterns are strongly associated with patterns of cropping practices and injury profiles; (v) patterns of cropping practices and injury profiles allow for a good description of the variation in actual yield; and (vi) patterns of cropping practices and injury profiles provide a framework that accurately reflects weather variation and site diversity, and reliably accounts for variation in yield. The mean estimated yield across sites (4.12 t ha^-1) corresponds to commonly cited averages in the region and indicates the potential for increased productivity with better management practices, especially an improved water supply. Injuries due to pests are secondary compared with other yield-limiting factors. Injury profiles were dominated by stem rot and sheath blight (IN1); bacterial leaf blight, plant hoppers, and leaf folder (IN2); and sheath rot, brown spot, leaf blast, and neck blast (IN3). IN1 was associated with high (mineral) fertilizer inputs, long fallow periods, low pesticide use, and good water management in (mostly) transplanted rice crops of a rice-rice rotation. IN2 was associated with direct-seeded rice crops in an intensive rice-rice rotation, where fertilizer and pesticide inputs are low and water management is poor, or where fertilizer and pesticide inputs are high and water management is adequate. IN3 corresponds to low input, labor intensive (hand weeding and transplanting) rice crops in a diverse rotation system with uncertain water supply. Weed infestation was an omnipresent constraint. This study shows the potential for developing pest management strategies that can be adapted throughout the region, rather than being site-specific.