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Pinellia ternata (Thunb.) Breit is an important traditional Chinese medicine. In North China, conventional flat planting of P. ternate is prone to root rot during the rainy season, leading to severe yield loss. Variations in planting patterns (e.g., ridge planting) can effectively alleviate this situation. However, the relationship between planting patterns and the changes induced by rhizosphere microbiome still needs to be determined. In this study, we clarified the effect of ridge planting on the yield of P. ternata and rhizosphere microbial community using high-throughput amplicon sequencing of 16S rRNA. Field experiments showed that ridge planting could increase the yield of P. ternata by 72.69% compared with flat planting. The high-throughput sequencing results demonstrated that fungal and bacterial communities in rhizosphere siols of flat and ridge planting showed obvious difference in diversity, structure, relative abundance, and community composition. The fungal phyla Zygomycota, Basidiomycota, Glomeromycota, and the bacterial phyla Chlamydiae, Tenericutes, and Hydrogenedentes were present in a higher relative abundance in the rhizosphere of ridge planting. Adonis multivariate analysis of variance results showed that 29 bacterial genera were significantly up/down-regulated, and only 4 fungal genera were changed considerably in ridge planting soil, indicating that the bacterial community composition varied significantly between the two treatments. Correlation analysis revealed that the yield of P. ternata was positively correlated with fungal genera Emericellopsis while negatively correlated with bacterial genera Acetobacter, Iamia, and fungal genera Thielavia. Overall, this study showed that ridge cropping significantly impacts the diversity and composition of the rhizosphere microbiome. It creates an environment favorable for crop growth and can be an effective planting strategy for P. ternata in areas with irrigation and high monsoon rainfall in North China. [ABSTRACT FROM AUTHOR]

The performance of three widely used thermal radiation models, the P-1 model, the surface-to-surface (S2S) model and the Discrete-Ordinates (DO) model, were evaluated for simulation temperature in Chinses solar greenhouse. The thermal radiation models were evaluated by comparing the numerical results with experimental data at representative points in the CSG. For indoor rear wall, the indoor soil and indoor air, the models showed good agreement between the experimental data and the simulated results correspond to P1, S2S and DO respectively. This work provides information for simulate greenhouse temperature and use specific radiation models for the most suitable thermal environment for crop growth. [ABSTRACT FROM AUTHOR]

In contrast to prolonged exposure to high temperatures, investigating short-term high-temperature stress can provide insights into the impact of varying heat stress durations on plant development and soil nutrient dynamics, which is crucial for advancing ecological agriculture. In this study, five heating temperatures were set at 200°C, 250°C, 300°C, 350°C, and 400°C, along with five heating time gradients of 6s, 10s, 14s, 18s, and 20s, including a control. A total of 26 treatment groups were analyzed, focusing on maize growth parameters and soil indicators. Principal component analysis was used for comprehensive evaluation. The results showed that high-temperature treatments with different heating times significantly influenced maize growth and soil properties. For instance, the treatment of 300°C+6s resulted in the longest total root length, while 200°C+6s led to the highest average root diameter. Plant height and leaf length were notably increased with the treatment of 400°C+6s. Most treatments resulted in decreased soil pH and organic matter content. Notably, the treatment of 350°C+16s showed the highest available phosphorus content, reaching 24.0 mg/kg, an increase of 4.5 mg/kg compared to the control. The study found that the average levels of active organic carbon and peroxidase were 1.26 mg/g and 3.91 mg/g, respectively. Additionally, the average mass fractions of clay, silt, and sand particles were 8.99%, 66.75%, and 24.26%, respectively. Through principal component analysis, six principal components were able to extract 19 indicators from the 26 treatments, covering 86.129% of the information. It was observed that 16 treatment methods performed better than the control in terms of soil comprehensive quality. The optimal treatment temperature and time identified for improving soil physicochemical properties and crop growth were 300°C+6s. These findings can be used to guide agricultural management and soil improvement practices, ultimately enhancing field productivity and providing valuable insights for sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Limax maximus, or great gray slug, is a common agriculture pest. The pest infests crops during their growth phase, creating holes in vegetable leaves, particularly in seedlings and tender leaves. A study was conducted to assess the insecticidal activity of Ageratina adenophora extract against these slugs. Factors such as fecundity, growth, hatching rate, offspring survival rate, protective enzyme activity, and detoxifying enzyme activity were examined in slugs exposed to the extract's sublethal concentration (LC50) for two different durations (24 and 48 h). The phytochemical variability of the extracts was also studied. The LC50 value of the A. adenophora extract against L. maximus was 35.9 mg/mL. This extract significantly reduced the hatching rate of eggs and the survival rate of offspring hatched from exposed eggs compared with the control. The lowest rates were observed in those exposed for 48 h. The survival, growth, protective enzyme, and detoxification activity of newly hatched and 40-day-old slugs decreased. The A. adenophora extract contained tannins, flavonoids, and saponins, possibly contributing to their biological effects. These results suggest that the extract could be used as an alternative treatment for slug extermination, effectively controlling this species. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is one of the primary macronutrients required for crop growth and yield. This nutrient is especially limiting wheat yields in the dry and low fertile agro-ecologies having low N in the root zone soil strata. Moreover, majority of farmers in India and South Asia are small to marginal with meagre capacity to invest in costly nitrogen fertilizers. Therefore, there is an immense need to identify lines that use nitrogen efficiently. A set of 50 diverse wheat genotypes consisting of indigenous germplasm lines (05), cultivars released for commercial cultivation (23) and selected elite lines from CIMMYT nurseries (22) were evaluated in an alpha-lattice design with two replications, a six-rowed plot of 2.5m length for 24 agro morphological, physiological and NUE related traits during two consecutive crop seasons in an N-depleted precision field under two different N levels of 50%-N50 (T1) and 100%-N100 (T2) of recommended N, i.e., 100 kg/ha. Analysis of variance revealed significant genetic variation among genotypes for all the traits studied. About 11.36% yield reduction was observed at reduced N levels. Significant correlations among NUE traits and yield component traits were observed which indicated pivotal role of N remobilization to the grain in enhancing yield levels. Among N-insensitive genotypes identified based on their yielding ability at low N levels, UASBW13356, UASBW13358, UASBW13354, UASBW13357 and KRL1-4 showed their inherent genotypic plasticity toward N application. The genotypes with more yield and high to moderate NUtE can be used as parents for the breeding of N efficient genotypes for marginal agro-ecologies. Low N tolerant genotypes identified from the current investigation may be further utilized in the identification of genomic regions responsible for NUE and its deployment in wheat breeding programs. The comprehensive data of 24 traits under different nitrogen levels for diverse genotypes from India and global sources (mainly CIMMYT) should be useful for supporting breeding for NUE and thus will be of great help for small and marginal farmers in India and South Asia. [ABSTRACT FROM AUTHOR]

Saline-alkali soil significantly impairs crop growth. This research employs the impacts of the modifier and water-soluble fertilizer, as well as their interaction, on the root systems of alfalfa and leymus chinensis in saline-alkali soil. The results exhibit that the hydrochar source modifier effectively enhances the root growth of both forage species. There are certain improvements in the root growth indicators of both crops at a dosage of 20 g/kg. Root enzyme activity and rhizosphere soil enzyme activity are enhanced in alfalfa, showing significant improvements in the first planting compared to the second planting. The application of water-soluble fertilizers also promotes root growth and root dehydrogenase activity. The root dehydrogenase activity of alfalfa and leymus chinensis are enhanced 62.18% and 10.15% in first planting than that of blank, respectively. Additionally, the two-factor variance analysis revealed a correlation between rhizosphere soil enzyme activity and changes in root traits. Higher rhizosphere soil enzyme activity is observed in conjunction with better root growth. The combined application of a modifier and water-soluble fertilizer has demonstrated a significant interaction effect on various aspects of the first planting of alfalfa and leymus chinensis. Moreover, the combined application of the modifier and water-soluble fertilizer has yielded superior results when compared to the individual application of either the modifier or the water-soluble fertilizer alone. This combined approach has proven effective in improving saline-alkali soil conditions and promoting crop growth in such challenging environments. [ABSTRACT FROM AUTHOR]

Sustainable intensification of agriculture requires understanding of the effect of soil characteristics and nutrient supply on crop growth. As farms are increasing in size by acquiring small fields from various farmers, the soil characteristics and nutrient supply might be very different from field to field, while at the same time specific soil properties might limit the nutrient uptake. As a result, there might be a large number of heterogeneous reasons why crop growth varies significantly. New data analysis techniques can help to explain variability in crop growth among fields. This paper introduces Exceptional Growth Mining (EGM) as a first contribution. EGM instantiates the data mining framework Exceptional Model Mining (EMM) such that subgroups of fields can be found that grow exceptionally in terms of three growth parameters (high/low maximum growth, steep/flat linear growth and early/late midpoint of maximum growth). As second contribution, we apply EGM to a case study by analyzing the dataset of a potato farm in the south of the Netherlands. EGM consists of (i) estimating growth curves by applying nonlinear mixed models, (ii) investigating the correlation between the estimated growth parameters, and (iii) applying EMM on these growth curve parameters using a growth curve-specific quality measure. By applying EGM on the data of the potato farm, we obtain the following results: 1) the estimated growth curves represent the variability in potato tuber growth very well (R2 of 0.92), 2) the steepness of the growth curve has a strong correlation with the maximum growth and the midpoint of maximum growth, and the correlation between the midpoint of maximum growth and maximum growth is weak, 3) the subgroup analyses indicate that: high values of K correspond to high maxima; low values of K correspond to low maxima, steep growth curves', and a late midpoint of halfway growth; Mg influences the midpoint of the growth curve; values of B are higher on dry soils with high tuber growth, while low values of B are found on wet soils with high tuber growth; high values of Zn, Mn, and Fe are found in subgroups with low tuber weight, probably related to the soil's low pH. In summary, this paper introduces EGM to obtain understanding in crop response to soil properties and nutrient supply. In addition, EGM provides a way to analyze only small parts of a large dataset, such that the impact of soil factors on growth can be analyzed on a more detailed level than existing methods. [ABSTRACT FROM AUTHOR]

Repeated measurements of crop height to observe plant growth dynamics in real field conditions represent a challenging task. Although there are ways to collect data using sensors on UAV systems, proper data processing and analysis are the key to reliable results. As there is need for specialized software solutions for agricultural research and breeding purposes, we present here a fast algorithm ALFA for the processing of UAV LiDAR derived point-clouds to extract the information on crop height at many individual cereal field-plots at multiple time points. Seven scanning flights were performed over 3 blocks of experimental barley field plots between April and June 2021. Resulting point-clouds were processed by the new algorithm ALFA. The software converts point-cloud data into a digital image and extracts the traits of interest–the median crop height at individual field plots. The entire analysis of 144 field plots of dimension 80 x 33 meters measured at 7 time points (approx. 100 million LiDAR points) takes about 3 minutes at a standard PC. The Root Mean Square Deviation of the software-computed crop height from the manual measurement is 5.7 cm. Logistic growth model is fitted to the measured data by means of nonlinear regression. Three different ways of crop-height data visualization are provided by the software to enable further analysis of the variability in growth parameters. We show that the presented software solution is a fast and reliable tool for automatic extraction of plant height from LiDAR images of individual field-plots. We offer this tool freely to the scientific community for non-commercial use. [ABSTRACT FROM AUTHOR]

Farmers coping strategies against harvest failures have implication for future adaptation to such shocks. Previous studies on farmers' vulnerability and response to shocks have emphasized on adaptation, at the expense of their coping to such shocks. Using a survey data from 299 farm households in northern Ghana, this study has analyzed farmers' coping strategies against harvest failure, and the drivers of the choice and intensity of the coping strategies. The empirical results show that most of the households used liquidation of productive assets, reduction in consumption, borrowing from family and friends, diversification of livelihoods, and migration to cities for off-farm jobs as coping measures in response to harvest failure. The empirical results from a multivariate probit model indicate that the choice of coping strategies is influenced by farmers' access to radio, net value of livestock produced per man-equivalent (ME), experience of yield loss in the previous year, farmers' perception about the fertility status of their crop fields, access to credit, distance to market, farm-to-farmer extension, location of the respondent, cropland per ME, and access to off-farm income. Empirical results from a zero-truncated negative binomial regression model also indicate that the number of coping strategies adopted increases with the value of farm implements, access to radio, farmer-to-farmer extension and being located in the regional capital. It however decreases with the age of the household head, number of family members abroad, a positive perception about the fertility status of crop fields, access to government extension services, distance to market, and access to off-farm income. Limited access to credit, radio, and markets renders farmers more vulnerable and pushes them to adopt more costly coping strategies. In addition, an increase in income earned from secondary livestock products decreases incentive for farmers to adopt liquidation of productive assets as coping strategy after a harvest failure. Policy makers and stakeholders could make smallholder farmers less vulnerable to harvest failure by enhancing their access to radio, credit, off-farm income and market, promotion of farmer-to-farmer extension, implementing measure to improve the fertility of crop fields in the study area, and enhancing farmers' engagement in the production and selling of secondary livestock products [ABSTRACT FROM AUTHOR]

The climate has drastically changed over the past two decades. Rising temperatures and climate change may lead to increased evapotranspiration, specifically soil evaporation, causing water to evaporate and salt to accumulate in the soil, resulting in increased soil salinity. As a result, there is a need to evaluate methods for predicting and monitoring the effects of salinity on crop growth and production through rapid screening. Our study was conducted on 20 wheat genotypes, 10 sensitive and 10 tolerant, exposed to two salinity levels (90 and 120 mM NaCl) with the control under greenhouse conditions. Our results revealed significant differences in the genotypes' response to salinity. Salt stress decreased chlorophyll index in sensitive genotypes but increased chlorophyll a and carotenoids in tolerant genotypes at 90 mM. Salt stress also increased protein, proline, lipoxygenase, and reactive thiobarbituric acid levels in all wheat genotypes. The study suggests that plant photosynthetic efficiency is a reliable, non-destructive biomarker for determining the salt tolerance of wheat genotypes, while other biochemical traits are destructive and time-consuming and therefore not suitable for rapid screening. [ABSTRACT FROM AUTHOR]

Crop phenology is crucial information for crop yield estimation and agricultural management. Traditionally, phenology has been observed from the ground; however Earth observation, weather and soil data have been used to capture the physiological growth of crops. In this work, we propose a new approach for the within-season phenology estimation for cotton at the field level. For this, we exploit a variety of Earth observation vegetation indices (derived from Sentinel-2) and numerical simulations of atmospheric and soil parameters. Our method is unsupervised to address the ever-present problem of sparse and scarce ground truth data that makes most supervised alternatives impractical in real-world scenarios. We applied fuzzy c-means clustering to identify the principal phenological stages of cotton and then used the cluster membership weights to further predict the transitional phases between adjacent stages. In order to evaluate our models, we collected 1,285 crop growth ground observations in Orchomenos, Greece. We introduced a new collection protocol, assigning up to two phenology labels that represent the primary and secondary growth stage in the field and thus indicate when stages are transitioning. Our model was tested against a baseline model that allowed to isolate the random agreement and evaluate its true competence. The results showed that our model considerably outperforms the baseline one, which is promising considering the unsupervised nature of the approach. The limitations and the relevant future work are thoroughly discussed. The ground observations are formatted in an ready-to-use dataset and will be available at https://github.com/Agri-Hub/cotton-phenology-dataset upon publication. [ABSTRACT FROM AUTHOR]

Although green (G, 500 to 600 nm) and far-red (FR, 700 to 800 nm) light play important roles in regulating plant growth and development, they are often considered less useful at stimulating photosynthesis than red (R, 600 to 700 nm) and blue (B, 400 to 500 nm) light. Based on this perception, approaches to modifying the transmission of greenhouse glazing materials include (1) conversion of G photons from sunlight into R photons and (2) exclusion of the near-infrared (>700 nm) fraction of sunlight. We evaluated these approaches using simulated scenarios with light-emitting diodes to determine how partial and complete substitution of G with R light and exclusion of FR light affected the growth of lettuce and tomato grown indoors. The substitution of G with R light had little or no effect on fresh and dry mass of tomato. However, with the presence of FR light, fresh and dry mass of lettuce increased by 22–26% as G light was increasingly substituted with R light. In tomato, excluding FR inhibited plant height, leaf area, and dry mass by 60–71%, 10–37%, and 20–44%, respectively. Similarly, in lettuce, excluding FR inhibited plant diameter, leaf length, and dry mass by 15–23%, 23–33%, or 28–48%, respectively. We conclude that the spectral conversion of G-to-R photons can promote plant growth in at least some crop species, such as lettuce, while the exclusion of FR decreases crop growth and yield. [ABSTRACT FROM AUTHOR]

Soil microbiome disruption methods are regularly used to reduce populations of microbial pathogens, often resulting in increased crop growth. However, little is known about the effect of soil microbiome disruption on non-pathogenic members of the soil microbiome. Here, we applied soil microbiome disruption in the form of moist-heat sterilization (autoclaving) to reduce populations of naturally occurring soil microbiota. The disruption was applied to analyze bacterial community rearrangement mediated by four crops (corn, beet, lettuce, and tomato) grown in three historically distinct agroecosystem soils (conventional, organic, and diseased). Applying the soil disruption enhanced plant influence on rhizosphere bacterial colonization, and significantly different bacterial communities were detected between the tested crops. Furthermore, bacterial genera showed significant abundance increases in ways both unique-to and shared-by each tested crop. As an example, corn uniquely promoted abundances of Pseudomonas and Sporocytophaga, regardless of the disrupted soil in which it was grown. Whereas the promotion of Bosea, Dyadobacter and Luteoliobacter was shared by all four crops when grown in disrupted soils. In summary, soil disruption followed by crop introduction amplified the plant colonization of potential beneficial bacterial genera in the rhizosphere. [ABSTRACT FROM AUTHOR]

Agrivoltaics, which integrate photovoltaic power production with agriculture in the same plot of land, have the potential to reduce land competition, reduce crop irrigation, and increase solar panel efficiency. To optimize agrivoltaic systems for crop growth, energy pathways must be characterized. While the solar panels shade the crops, they also emit longwave radiation and partially block the ground from downwelling longwave radiation. A deeper understanding of the spatial variation in incoming energy would enable controlled allocation of energy in the design of agrivoltaic systems. The model also demonstrates that longwave energy should not be neglected when considering a full energy balance on the soil under solar panels. [ABSTRACT FROM AUTHOR]

Photosynthetic bacteria are beneficial to plants, but knowledge of photosynthetic bacterial community dynamics in field crops during different growth stages is scarce. The factors controlling the changes in the photosynthetic bacterial community during plant growth require further investigation. In this study, 35 microbial community samples were collected from the seedling, flowering, and mature stages of tomato, cucumber, and soybean plants. 35 microbial community samples were assessed using Illumina sequencing of the photosynthetic reaction center subunit M (pufM) gene. The results revealed significant alpha diversity and community structure differences among the three crops at the different growth stages. Proteobacteria was the dominant bacterial phylum, and Methylobacterium, Roseateles, and Thiorhodococcus were the dominant genera at all growth stages. PCoA revealed clear differences in the structure of the microbial populations isolated from leaf samples collected from different crops at different growth stages. In addition, a dissimilarity test revealed significant differences in the photosynthetic bacterial community among crops and growth stages (P<0.05). The photosynthetic bacterial communities changed during crop growth. OTUs assigned to Methylobacterium were present in varying abundances among different sample types, which we speculated was related to the function of different Methylobacterium species in promoting plant growth development and enhancing plant photosynthetic efficiency. In conclusion, the dynamics observed in this study provide new research ideas for the detailed assessments of the relationship between photosynthetic bacteria and different growth stages of plants. [ABSTRACT FROM AUTHOR]

Salinity in soil and water is one of the environmental factors that severely hinder the crop growth and production particularly in arid and semi-arid regions. A pot experiment was conducted to investigate the impact of salinity levels (1.5 dS m-1, 3.5 dS m-1, 7.5 dS m-1 and 11.5 dS m-1) on emergence, growth and biochemical traits of moringa landraces under completely randomized design having three replications. Four landraces of Moringa oleifera (Faisalabad black seeded moringa [MFB], Patoki black seeded moringa [MPB], Faisalabad white seeded moringa [MFW] and Rahim Yar Khan black seeded moringa [MRB]) were selected for experimentation. All the salinity levels significantly affected the emergence parameters (time to emergence start, time to 50% emergence, mean emergence time, emergence index and final emergence percentage) of moringa landraces. However, 1.5 dS m-1 and 3.5 dS m-1 were found more favorable. Higher salinity levels (7.5 dS m-1 and 11.5 dS m-1) significantly minimized the root surface area, root projected area, root volume and root density as compared to 1.5 dS m-1, 3.5 dS m-1. Number of branches, leaves, leaflets and leaf length were also adversely affected by 7.5 dS m-1 and 11.5 dS m-1. Maximum seedling fresh and dry weights, and seedling length were recorded at 1.5 dS m-1 followed by 3.5 dS m-1. Chlorophyll a and b contents, carotenoids and membrane stability index were also observed highest at salinity level of 1.5 dS m-1. In case of moringa landraces, MRB performed better regarding emergence attributes, growth parameters, and biochemical analysis followed by MFW as compared to MFB and MPB. Moringa landraces i.e. MRB and MFW were found more tolerant to salinity stress as compared to MFB and MPB. [ABSTRACT FROM AUTHOR]

Ethiopia's economy is dominated by agriculture which is mainly rain-fed and subsistence. Climate change is expected to have an adverse impact particularly on crop production. Previous studies have shown large discrepancies in the magnitude and sometimes in the direction of the impact on crop production. We assessed the impact of climate change on growth and yield of maize and wheat in Ethiopia using a multi-crop model ensemble. The multi-model ensemble (n = 48) was set up using the agroecosystem modelling framework Expert-N. The framework is modular which facilitates combining different submodels for plant growth and soil processes. The multi-model ensemble was driven by climate change projections representing the mid of the century (2021–2050) from ten contrasting climate models downscaled to finer resolution. The contributions of different sources of uncertainty in crop yield prediction were quantified. The sensitivity of crop yield to elevated CO2, increased temperature, changes in precipitations and N fertilizer were also assessed. Our results indicate that grain yields were very sensitive to changes in [CO2], temperature and N fertilizer amounts where the responses were higher for wheat than maize. The response to change in precipitation was weak, which we attribute to the high water holding capacity of the soils due to high organic carbon contents at the study sites. This may provide the sufficient buffering capacity for extended time periods with low amounts of precipitation. Under the changing climate, wheat productivity will be a major challenge with a 36 to 40% reduction in grain yield by 2050 while the impact on maize was modest. A major part of the uncertainty in the projected impact could be attributed to differences in the crop growth models. A considerable fraction of the uncertainty could also be traced back to different soil water dynamics modeling approaches in the model ensemble, which is often ignored. Uncertainties varied among the studied crop species and cultivars as well. The study highlights significant impacts of climate change on wheat yield in Ethiopia whereby differences in crop growth models causes the large part of the uncertainties. [ABSTRACT FROM AUTHOR]

While only 20% of harvested lands are actually irrigated, 40% of global agricultural production originates from irrigated areas. Therefore, assessing irrigation requirements is essential for the development of effective water-related policies for an efficient management of water resources. Moreover, global-scale analyses are becoming increasingly relevant, motivated by globalized production and international trade of food as well as by the need of common strategies to address climate change. In this study, a comprehensive model to estimate crop growth and irrigation requirements of 26 main crops at global scale is presented. The model computes a soil water balance using daily precipitation and reference evapotranspiration based on a high-resolution ERA5 reanalysis dataset from the European Copernicus Program. The irrigation requirement, defined as the minimum water volume to avoid water stress, is computed for year 2000 at the resolution of 5 arc-min (or 0.0833°) and aggregated at different spatial and temporal scales for relevant analyses. The estimated global irrigation requirements for 962 km3 is described in detail, also in relation to the spatial variability and to the monthly variation of the requirements. A focus on different areas of the world (California, Northern Italy and India) highlights the wealth of information provided by the model in different climatic conditions. National data of irrigation withdrawals have been used for an extensive comparison with model results. A crop-specific validation has also been made for the State of California, comparing model results with local data of irrigation volume and independent estimates of crop water use. In both cases, we found a good agreement between model results and real data. [ABSTRACT FROM AUTHOR]

The purposes are to monitor the nitrogen utilization efficiency of crops and intelligently evaluate the absorption of nutrients by crops during the production process. The research object is Chinese cabbage. The Chinese cabbage population with different agricultural parameters is constructed through different densities and nitrogen fertilizer application rates based on digital image processing technology, and an estimation NC (Nitrogen Content) model is established. The population is classified through the K-Means Clustering algorithm using the feature extraction method, and the Chinese cabbage population quality BPNN (Backpropagation Neural Network) model is constructed. The nonlinear mapping relationship between different agricultural parameters and population quality, and the contribution rate of each indicator, are studied. The nitrogen utilization of Chinese cabbage is monitored effectively. Results demonstrate that the proposed NC estimation model has correlation coefficients above 0.70 in different growth stages. This model can accurately estimate the NC of the Chinese cabbage population. The results of the Chinese cabbage population quality BPNN model show that the population planting density based on the seedling number is reasonable. The constructed population quality evaluation model has a high R2 value and a comparatively low RMSE (Root Mean Square Error) value for the quality evaluation of Chinese cabbage in different periods, showing that it applies to evaluate the population quality of Chinese cabbage in different growth stages. The constructed nitrogen utilization model and quality evaluation model can monitor the nutrient utilization of crops in different growth stages, ascertain the agricultural characteristics of other yield groups in different growth stages, and clarify the performance of agricultural parameters in different growth stages. The above results can provide some ideas for crop growth intelligent detection. [ABSTRACT FROM AUTHOR]

In controlled environments, crop models that incorporate environmental factors can be developed to optimize growth and development as well as conduct cost and/or resource use benefit analyses. The overall objective of this study was to model growth and development of dill 'Bouquet' (Anethum graveolens), parsley 'Giant of Italy' (Petroselinum crispum), and watercress (Nasturtium officinale) in response to photosynthetic daily light integral (DLI) and mean daily temperature (MDT). Plants were grown hydroponically in five greenhouse compartments with MDTs ranging from 9.7 to 27.2 °C under 0%, 30%, or 50% shade cloth to create DLIs ranging from 6.2 to 16.9 mol·m‒2·d‒1. MDT and DLI interacted to influence dill fresh mass and height, and watercress maximum quantum yield of dark adapted leaves (Fv/Fm), height, and branch number while only MDT affected dill leaf number and watercress fresh mass and branch length. Besides dry matter concentration (DMC), parsley was influenced by MDT and not DLI. Increasing MDT from ≈10 to 22.4 °C (parsley) or 27.2 °C (dill and watercress), linearly or near-linearly increased fresh mass. For dill, increasing DLI decreased fresh mass when MDT was low (9.7 to 13.9 °C) and increased fresh mass when MDT was high (18.4 to 27.2 °C). DMC of dill, parsley, and watercress increased as MDT decreased or DLI increased, indicating a higher proportion of plant fresh mass is water at higher MDTs or lower DLIs. With these data we have created growth and development models for culinary herbs to aid in predicting responses to DLI and MDT. [ABSTRACT FROM AUTHOR]

Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or tractor-based high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R2 = 0.96–0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations. [ABSTRACT FROM AUTHOR]

Fungal pathogens exert severe qualitative and quantitative damages to wheat crop. Karnal bunt of wheat caused by Tilletia indica Mitra, Mundkur is a severe threat to global food security. Nonetheless, T. indica is regulated as a quarantine pest in numerous countries, which further aggravates the situation. Tolerant varieties and appropriate management practices for Karnal bunt are imperative to meet the global wheat demands. This two-year study explored the impact of fungicide [Fosetyl-Aluminium (Aliette)] application timing on allometric traits, disease suppression and economic returns of bread wheat. Four bread wheat cultivars differing in their tolerance to Karnal bunt were used in the study. Fungicide was applied as either seed treatment (ST), foliar application at heading (FAH) or ST + FAH, whereas no application (NA) was taken as control. Lasani-08 performed better than the rest of the cultivars in terms of allometric traits (plant height, leaf area, crop growth rate, photosynthesis, and chlorophyll content), yield and economic returns. Nonetheless, minimal disease severity was recorded for Lasani-08 compared to other cultivars during both years. The ST improved allometric traits of all cultivars; however, ST + FAH resulted in higher yield and economic returns. Cultivar Pasban-90 observed the highest disease severity and performed poor for allometric traits, yield and economic returns. It is concluded that ST + FAH of Fosetyl-Aluminium could be a pragmatic option to cope Karnal bunt of wheat. Nonetheless, Pasban-90 must not be used for cultivation to avoid yield and quality losses. [ABSTRACT FROM AUTHOR]

In this study, we assessed the possible impacts of climate variability and change on growth and performance of maize using multi-climate, multi-crop model approaches built on Agricultural Model Intercomparison and Improvement Project (AgMIP) protocols in five different agro-ecological zones (AEZs) of Embu County in Kenya and under different management systems. Adaptation strategies were developed that are locally relevant by identifying a set of technologies that help to offset potential impacts of climate change on maize yields. Impacts and adaptation options were evaluated using projections by 20 Coupled Model Intercomparison Project—Phase 5 (CMIP5) climate models under two representative concentration pathways (RCPs) 4.5 and 8.5. Two widely used crop simulation models, Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT) was used to simulate the potential impacts of climate change on maize. Results showed that 20 CMIP5 models are consistent in their projections of increased surface temperatures with different magnitude. Projections by HadGEM2-CC, HadGEM2-ES, and MIROC-ESM tend to be higher than the rest of 17 CMIP5 climate models under both emission scenarios. The projected increase in minimum temperature (Tmin) which ranged between 2.7 and 5.8°C is higher than the increase in maximum temperature (Tmax) that varied between 2.2 and 4.8°C by end century under RCP 8.5. Future projections in rainfall are less certain with high variability projections by GFDL-ESM2G, MIROC5, and NorESM1-M suggest 8 to 25% decline in rainfall, while CanESM2, IPSL-CM5A-MR and BNU-ESM suggested more than 85% increase in rainfall under RCP 8.5 by end of 21st century. Impacts of current and future climatic conditions on maize yields varied depending on the AEZs, soil type, crop management and climate change scenario. Impacts are largely negative in the low potential AEZs such as Lower Midlands (LM4 and LM5) compared with the high potential AEZs Upper Midlands (UM2 and UM3). However, impacts of climate change are largely positive across all AEZs and management conditions when CO2 fertilization is included. Using the differential impacts of climate change, a strategy to adapt maize cultivation to climate change in all the five AEZs was identified by consolidating those practices that contributed to increased yields under climate change. We consider this approach as more appropriate to identify operational adaptation strategies using readily available technologies that contribute positively under both current and future climatic conditions. This approach when adopted in strategic manner will also contribute to further strengthen the development of adaptation strategies at national and local levels. The methods and tools validated and applied in this assessment allowed estimating possible impacts of climate change and adaptation strategies which can provide valuable insights and guidance for adaptation planning. [ABSTRACT FROM AUTHOR]

The phyllosphere epiphytic microbiome is composed of microorganisms that colonize the external aerial portions of plants. Relationships of plant responses to specific microorganisms–both pathogenic and beneficial–have been examined, but the phyllosphere microbiome functional and metabolic profile responses are not well described. Changing crop growth conditions, such as increased drought, can have profound impacts on crop productivity. Also, epiphytic microbial communities provide a new target for crop yield optimization. We compared Zea mays leaf microbiomes collected under drought and well-watered conditions by examining functional gene annotation patterns across three physically disparate locations each with and without drought treatment, through the application of short read metagenomic sequencing. Drought samples exhibited different functional sequence compositions at each of the three field sites. Maize phyllosphere functional profiles revealed a wide variety of metabolic and regulatory processes that differed in drought and normal water conditions and provide key baseline information for future selective breeding. [ABSTRACT FROM AUTHOR]

Appropriate flowering and maturity time are important for soybean production. Four maturity genes E1, E2, E3 and E4 have been molecularly identified and found to play major roles in the control of flowering and maturity of soybean. Here, to further investigate the effect of different allele combinations of E1-E4, we performed Kompetitive Allele Specific PCR (KASP) assays based on single nucleotide polymorphisms (SNPs) at these four E loci, and genotyped E1-E4 genes across 308 Chinese cultivars with a wide range of maturity groups. In total, twenty-one allele combinations for E1-E4 genes were identified across these Chinese cultivars. Various combinations of mutations at four E loci gave rise to the diversity of flowering and maturity time, which were associated with the adaptation of soybean cultivars to diverse geographic regions and farming systems. In particular, the cultivars with mutations at all four E loci reached flowering and maturity very early, and adapted to high-latitude cold regions. The allele combinations e1-as/e2-ns/e3-tr/E4, E1/e2-ns/E3/E4 and E1/E2/E3/E4 played important roles in the Northeast China, Huang-Huai-Hai (HHH) Rivers Valley and South China regions, respectively. Notably, E1 and E2, especially E2, affected flowering and maturity time of soybean significantly. Our study will be beneficial for germplasm evaluation, cultivar improvement and regionalization of cultivation in soybean production. [ABSTRACT FROM AUTHOR]

Genomic prediction (GP) is expected to become a powerful technology for accelerating the genetic improvement of complex crop traits. Several GP models have been proposed to enhance their applications in plant breeding, including environmental effects and genotype-by-environment interactions (G×E). In this study, we proposed a two-step model for plant biomass prediction wherein environmental information and growth-related traits were considered. First, the growth-related traits were predicted by GP. Second, the biomass was predicted from the GP-predicted values and environmental data using machine learning or crop growth modeling. We applied the model to a 2-year-old field trial dataset of recombinant inbred lines of japonica rice and evaluated the prediction accuracy with training and testing data by cross-validation performed over two years. Therefore, the proposed model achieved an equivalent or a higher correlation between the observed and predicted values (0.53 and 0.65 for each year, respectively) than the model in which biomass was directly predicted by GP (0.40 and 0.65 for each year, respectively). This result indicated that including growth-related traits enhanced accuracy of biomass prediction. Our findings are expected to contribute to the spread of the use of GP in crop breeding by enabling more precise prediction of environmental effects on crop traits. [ABSTRACT FROM AUTHOR]

Water stress (WS) and heat stress (HS) have a negative effect on soybean plant growth and crop productivity. Changes in the physiological characteristics, proteome, and specific metabolites investigated on molecular and cellular functions were studied in two soybean cultivars exposed to different heat and water stress conditions independently and in combination. Leaf protein composition was studied using 2-DE and complemented with MALDI TOF mass spectrometry. While the two cultivars displayed genetic variation in response to water and heat stress, thirty-nine proteins were significantly altered in their relative abundance in response to WS, HS and combined WS+HS in both cultivars. A majority of these proteins were involved in metabolism, response to heat and photosynthesis showing significant cross-tolerance mechanisms. This study revealed that MED37C, a probable mediator of RNA polymerase transcription II protein, has potential interacting partners in Arabidopsis and signified the marked impact of this on the PI-471938 cultivar. Elevated activities in antioxidant enzymes indicate that the PI-471938 cultivar can restore the oxidation levels and sustain the plant during the stress. The discovery of this plant's development of cross-stress tolerance could be used as a guide to foster ongoing genetic modifications in stress tolerance. [ABSTRACT FROM AUTHOR]

In order to determine correct dosage of chemotherapy drugs, the effect of the drug must be properly quantified. There are two important values that characterize the effect of the drug: εmax is the maximum possible effect of a drug, and IC50 is the drug concentration where the effect diminishes by half. There is currently a problem with the way these values are measured because they are time-dependent measurements. We use mathematical models to determine how the εmax and IC50 values depend on measurement time and model choice. Seven ordinary differential equation models (ODE) are used for the mathematical analysis; the exponential, Mendelsohn, logistic, linear, surface, Bertalanffy, and Gompertz models. We use the models to simulate tumor growth in the presence and absence of treatment with a known IC50 and εmax. Using traditional methods, we then calculate the IC50 and εmax values over fifty days to show the time-dependence of these values for all seven mathematical models. The general trend found is that the measured IC50 value decreases and the measured εmax increases with increasing measurement day for most mathematical models. Unfortunately, the measured values of IC50 and εmax rarely matched the values used to generate the data. Our results show that there is no optimal measurement time since models predict that IC50 estimates become more accurate at later measurement times while εmax is more accurate at early measurement times. [ABSTRACT FROM AUTHOR]

Chinese solar greenhouses are unique facility agriculture buildings and widely used in northeastern China, providing a favorable requirement for crop growth. The north wall configurations play an essential role in heat storage and thermal insulation and directly affect the management of the internal environment. This research is devoted to further improve the thermal performance of the greenhouse and explore the potential of the north wall. A mathematical model was designed to investigate the concave-convex wall configurations based on computational fluid dynamics. Four passive heat-storage north walls were analyzed by using the same constituent materials, including a plane wall, a vertical wall, a horizontal wall and an alveolate wall. The numerical model was validated by experimental measurements. The temperature distributions of the north walls were examined and a comparative analysis of the heat storage-release capabilities was carried out. The results showed that the heat-storage capacity of the north wall is affected by the surface structure. Moreover, the critical factor influencing the air temperature is the sum of the heat load released by the wall and the energy increment of greenhouse air. The results suggested that the alveolate wall has preferable thermal accumulation capacity. The concave-convex wall configurations have a wider range of heat transfer performance along the thickness direction, while the plane wall has a superior thermal environment. This study provides a basic theoretical reference to rationally design the internal surface structures of the north wall. [ABSTRACT FROM AUTHOR]

Availability of good quality irrigation water is a big challenge in arid and semi arid regions of the world. Drought stress results in poor plant growth and low yield; however, the rhizobacteria, capable of producing 1-aminocyclopropane-1-carboxylate (ACC)-deaminase are likely to improve crop growth and productivity under drought stress. Similarly, biochar could also ameliorate the negative impacts of drought stress. Therefore, this pot experiment was conducted to evaluate the role of ACC-deaminase producing plant growth promoting rhizobacteria (PGPR) alone and in combinations with timber-waste biochar in improving maize growth under drought stress. The ACC-deaminase producing rhizobacteria, Pseudomonas aeruginosa, Enterobacter cloacae, Achromobacter xylosoxidans and Leclercia adecarboxylata were studied along with two rates (0.75 and 1.50% of the soil weight) of biochar under three moisture levels i.e., normal moisture, mild drought stress and severe drought stress. The E. cloacae in conjunction with higher rate of biochar produced a significant improvement i.e., up to 60, 73, 43, 69, 76 and 42% respectively, in grain yield plant-1, photosynthetic rate, stomatal conductance, chlorophyll a, total chlorophyll and carotenoids contents of maize as compared to the control under mild drought stress. Similarly, A. xylosoxidans with higher rate of biochar also enhanced grain yield plant-1, photosynthetic rate, stomatal conductance, chlorophyll a, total chlorophyll and carotenoids contents of maize up to 200, 213, 113, 152, 148 and 284%, respectively over control under severe drought stress. In conclusion, combination of ACC-deaminase containing PGPR, A. xylosoxidans and biochar (0.75%) proved an effective technique to improve maize growth and productivity under drought stress. [ABSTRACT FROM AUTHOR]

Nitrogen (N) fertilization affects bioenergy crop growth and productivity and consequently carbon (C) and N contents in soil, it however remains unclear whether N fertilization and crop type individually or interactively influence soil organic carbon (SOC) and total N (TN). In a three-year long fertilization experiment in switchgrass (SG: Panicum virgatum L.) and gamagrass (GG: Tripsacum dactyloides L.) croplands in Middle Tennessee USA, soil samples (0–15cm) were collected in plots with no N input (NN), low N input (LN: 84 kg N ha-1 yr-1 in urea) and high N input (HN: 168 kg N ha-1 yr-1 in urea). Besides SOC and TN, the aboveground plant biomass was also quantified. In addition to a summary of published root morphology data based on a separated mesocosm experiment, the root leachable dissolved organic matter (DOM) of both crops was also measured using archived samples. Results showed no significant interaction of N fertilization and crop type on SOC, TN or plant aboveground biomass (ABG). Relative to NN, HN (not LN) significantly increased SOC and TN in both crops. Though SG showed a 15–68% significantly higher ABG than GG, GG showed a 9.3–12% significantly higher SOC and TN than SG. The positive linear relationships of SOC or TN with ABG were identified for SG. However, GG showed structurally more complex and less readily decomposed root DOM, a larger root volume, total root length and surface area than SG. Collectively, these suggested that intensive N fertilization could increase C and N stocks in bioenergy cropland soils but these effects may be more likely mediated by the aboveground biomass in SG and root chemistry and morphology in GG. Future studies are expected to examine the root characteristics in different bioenergy croplands under the field fertilization experiment. [ABSTRACT FROM AUTHOR]

Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement. [ABSTRACT FROM AUTHOR]

The issue of how to alleviate the negative effects imposed by water stress is an interesting problem. Plant growth-promoting rhizobacteria (PGPR) colonize the rhizosphere of plants and are known to promote the growth of crops. However, there are few studies characterizing the physiological response of plants to drought stress after PGPR inoculation. The aim of this study was to investigate the effectiveness of different PGPRs in arid environments and then investigated the effects of PGPR inoculation under drought stress on the physiological characteristics and growth of North China red elder (Sambucus williamsii) nursery container seedlings. The viable count of different PGPRs under drought stress varies widely, and the drought tolerance of Acinetobacter calcoaceticus X128 was significantly higher than that of other PGPRs. In comparison with non-inoculation, inoculation with X128 in an arid environment significantly increased stomatal conductance and mitigated the inhibition of net photosynthetic rate caused by drought stress; this mitigating effect of inoculation is enhanced as the level of drought stress increases. Relative to non-inoculated seedlings, cytokinin levels in the leaves increased by 91.17% under severe drought stress conditions in inoculated seedlings. However, X128 inoculation decreased this deficit to only 44.54%. Compared with non-inoculated seedlings, the relative water content of inoculated seedlings under severe drought stress increased by 15.06%, however the relative conductivity decreased by 12.48%. Consequently, X128 could increase dry matter accumulation of S. williamsii regardless of watering status, indicative of the greater benefits of PGPR on shoot growth than root. Therefore, inoculation of A. calcoaceticus X128 under drought conditions play a significant role for alleviating the negative effects imposed by water stress and promoting plant growth. [ABSTRACT FROM AUTHOR]

Classical crop models have been developed to predict crop yield and quality, and they are based on physiological and environmental inputs. After molecular discoveries, models should integrate genetic variation to allow predictions that are more genotype-dependent. An interesting approach, Quantitative Trait Locus (QTL)-based ecophysiological modeling, has shown promising results for the design of ideotypes that are adapted to biotic and abiotic stresses, but there are still limitations to attaining a fully integrated model. The aim of this case study is to clarify the impact of choosing different model equations (closely related and with different numbers of parameters) and optimization methods on the detection of QTLs controlling the parameters of crop growth. Different growth equations were parameterized based on a genetic population by following different approaches. The correlations between parameters were analyzed, and two different strategies were adopted to address the correlation issue. QTL analysis was performed on the optimized values of the parameters of the growth equations and on the observed dry mass (DM) data to validate the QTLs detected. Overall, models and strategies resulted in different QTLs being detected. Similar LOD profiles but with peaks of different heights were observed, some of which were significant, resulting in different numbers of QTLs. In some cases, peaks had slightly different positions or were absent. Even closely related growth models led to the detection of different QTLs. The goodness of fit and complexity of the growth models were found to be insufficient to select the best model. Calculating parameters independently of observed data may not be a good strategy, whereas setting parameters independent of the genotype is recommended. Given the large-scale global optimization problem and the strong correlations between parameters, the two algorithms tested showed poor performance. Currently, the lack of effective algorithms is the main obstacle to answering the question posed. The authors therefore suggest testing different model formulations and comparing the QTLs detected before choosing the best formulation to use in an ecophysiological modeling approach based on QTLs. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) can not only classify images but can also generate key features, e.g., the Google neural network that learned to identify cats by simply watching YouTube videos, for the classification. In this paper, crop models are distilled by CNN to evaluate the ability of deep learning to identify the plant physiology knowledge behind such crop models simply by learning. Due to difficulty in collecting big data on crop growth, a crop model was used to generate datasets. The generated datasets were fed into CNN for distillation of the crop model. The models trained by CNN were evaluated by the visualization of saliency maps. In this study, three saliency maps were calculated using all datasets (case 1) and using datasets with spikelet sterility due to either high temperature at anthesis (case 2) or cool summer damage (case 3). The results of case 1 indicated that CNN determined the developmental index of paddy rice, which was implemented in the crop model, simply by learning. Moreover, CNN identified the important individual environmental factors affecting the grain yield. Although CNN had no prior knowledge of spikelet sterility, cases 2 and 3 indicated that CNN realized about paddy rice becoming sensitive to daily mean and maximum temperatures during specific periods. Such deep learning approaches can be used to accelerate the understanding of crop models and make the models more portable. Moreover, the results indicated that CNN can be used to develop new plant physiology theories simply by learning. [ABSTRACT FROM AUTHOR]

Systematic monitoring of pasture quantity and quality is important to match the herd forage demand (pasture removal by grazing or harvest) to the supply of forage with adequate nutritive value. The aim of this research was to monitor, assess and manage changes in pasture growth, morphology and digestibility by integrating information from an Unmanned Aerial Vehicle (UAV) and two process-based models. The first model, Systems Approach to Land Use Sustainability (SALUS), is a process-based crop growth model used to predict pasture regrowth based on soil, climate, and management data. The second model, Morphogenetic and Digestibility of Pasture (MDP), uses paddock-scale values of herbage mass as input to predict leaf morphogenesis and forage nutritive value. Two field experiments were carried out on tall fescue- and ryegrass-based pastures under rotational grazing with lactating dairy cattle. The first experiment was conducted at plot scale and was used to calibrate the UAV and to test models. The second experiment was conducted at field scale and was used to test the UAV’s ability to predict pasture biomass under grazing rotation. The Normalized Difference Vegetation Index (NDVI) calculated from the UAV’s multispectral reflectance (n = 72) was strongly correlated (p < 0.001) to plot measurements of pasture biomass (R2 = 0.80) within the range of ~226 and 4208 kg DM ha-1. Moreover, there was no difference (root mean square error, RMSE < 500 kg DM ha-1) between biomass estimations by the UAV (1971±350 kg ha-1) and two conventional methods used as control, the C-Dax proximal sensor (2073±636 kg ha-1) and ruler (2017±530 kg ha-1). The UAV approach was capable of mapping at high resolution (6 cm) the spatial variability of pasture (16 ha). The integrated UAV-modeling approach properly predicted spatial and temporal changes in pasture biomass (RMSE = 509 kg DM ha-1, CCC = 0.94), leaf length (RMSE = 6.2 cm, CCC = 0.62), leaf stage (RMSE = 0.7 leaves, CCC = 0.65), neutral detergent fiber (RMSE = 3%, CCC = 0.71), digestibility of neutral detergent fiber (RMSE = 8%, CCC = 0.92) and digestibility of dry matter (RMSE = 5%, CCC = 0.93) with reasonable precision and accuracy. These findings therefore suggest potential for the present UAV-modeling approach for use as decision support tool to allocate animals based on spatially and temporally explicit predictions of pasture biomass and nutritive value [ABSTRACT FROM AUTHOR]

Wheat Grain Yield (GY) and quality are particularly susceptible to nitrogen (N) fertilizer management. However, in rain-fed Mediterranean environments, crop N requirements might be variable due to the effects of water availability on crop growth. Therefore, in-season crop N status assessment is needed in order to apply N fertilizer in a cost-effective way while reducing environmental impacts. Remote sensing techniques might be useful at assessing in-season crop N status. In this study, we evaluated the capacity of vegetation indices formulated using blue (B), green (G), red (R) and near-infrared (NIR) bands obtained with a consumer-grade camera to assess wheat N status. Chlorophyll Content Index (CCI) and fractional intercepted PAR (fIPAR) were measured at three phenological stages and GY and biomass were determined at harvest. Indices formulated using RG bands and the normalized difference vegetation index (NDVI) were significantly correlated with both CCI and fIPAR at the different phenological stage (0.71 < r < 0.81, P < 0.01). Moreover, indices formulated using RG bands were capable at differentiating unfertilized and fertilized plots. In addition, RGB indices and NDVI were found to be related to both crop biomass and GY at harvest, particularly when data were obtained at initial grain filling stage (r > 0.80, P < 0.01). Finally, RGB indices and NDVI obtained with a consumer-grade camera showed comparable capacity at assessing chlorophyll content and predicting both crop biomass and GY at harvest than those obtained with a spectroradiometer. This study highlights the potential of standard and modified consumer-grade cameras at assessing canopy traits related to crop N status and GY in wheat under Mediterranean conditions. [ABSTRACT FROM AUTHOR]

Soil salinization is a major factor limiting crop growth and development in many areas. Switchgrass (Panicum virgatum L.) is an important warm-season grass species used for biofuel production. The objective of this study was to investigate antioxidant metabolism, proline,and protein variation associated with alkali-salt tolerance among 30 switchgrass lines and identify metabolic markers for evaluating alkali-salt tolerance of switchgrass lines. The grass lines were transplanted into plastic pots containing fine sand. When the plants reached E5 developmental stage, they were subjected to either alkali-salt stress treatment (150 mM Na+ and pH of 9.5) or control (no alkali-salt stress) for 20 d. The 30 switchgrass lines differed in alkali-salt tolerance as determined by the level of leaf malondialdehyde (MDA), antioxidant enzyme activity [(superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX)], proline and protein. Alkali-salt stress increased MDA, proline, SOD, reduced CAT activity, but its effect on protein and APX varied depending on lines. Wide variations in the five parameters existed among the 30 lines. In general, the lines with higher CAT activity and lower proline content under alkali-salt stress had less MDA, exhibiting better alkali-salt tolerance. Among the five parameters, CAT can be considered as valuable metabolic markers for assessment of switchgrass tolerance to alkali-salt stress. [ABSTRACT FROM AUTHOR]

Agricultural crops play an important role in the global carbon and water cycle. Global climate change scenarios predict enhanced water scarcity and altered precipitation pattern in many parts of the world. Hence, a mechanistic understanding of water fluxes, productivity and water use efficiency of cultivated crops is of major importance, i.e. to adapt management practices. We compared water and carbon fluxes of paddy and rainfed rice by canopy scale gas exchange measurements, crop growth, daily evapotranspiration, transpiration and carbon flux modeling. Throughout a monsoon rice growing season, soil evaporation in paddy rice contributed strongly to evapotranspiration (96.6% to 43.3% from initial growth to fully developed canopy and amounted to 57.9% of total water losses over the growing seasons. Evaporation of rainfed rice was significantly lower (by 65% on average) particularly before canopy closure. Water use efficiency (WUE) was significantly higher in rainfed rice both from an agronomic (WUEagro, i.e. grain yield per evapotranspiration) and ecosystem (WUEeco, i.e. gross primary production per evapotranspiration) perspective. However, our results also show that higher WUE in rainfed rice comes at the expense of higher respiration losses compared to paddy rice (26% higher on average). Hence, suggestions on water management depend on the regional water availability (i.e. Mediterranean vs. Monsoon climate) and the balance between higher respiratory losses versus a potential reduction in CH4 and other greenhouse gas emissions. Our results suggest that a shift from rainfed/unsaturated soil to waterlogged paddy conditions after closure of the rice canopy might be a good compromise towards a sustainable use of water while preserving grain yield, particularly for water-limited production areas. [ABSTRACT FROM AUTHOR]

The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and forecasting applications. A spatially-enabled database is a key component of the software that can ingest a suite of satellite and model datasets while facilitating the interfacing with Geographic Information System (GIS) applications. The datasets ingested are obtained from numerous space-borne sensors and represent multiple components of the water cycle. The object-oriented design of the software allows for modularity and extensibility, showcased here with the coupling of the core hydrologic model with a crop growth model. RHEAS can exploit multi-threading to scale with increasing number of processors, while the database allows delivery of data products and associated uncertainty through a variety of GIS platforms. A set of three example implementations of RHEAS in the United States and Kenya are described to demonstrate the different features of the system in real-world applications. [ABSTRACT FROM AUTHOR]

Sugarcane, a major crop grown in the tropical and subtropical areas of the world, is produced mainly for sucrose, which is used as a sweetener or for the production of bioethanol. Among the numerous pests that significantly affect the yield of sugarcane, the sugarcane rhizome borer (Migdolus fryanus, a cerambycidae beetle) is known to cause severe damage to the crops in Brazil. The absence of molecular information about this insect reinforces the need for studies and an effective method to control this pest. In this study, RNA-Seq technology was employed to study different parts of M. fryanus larvae. The generated data will help in further investigations about the taxonomy, development, and adaptation of this insect. RNA was extracted from six different parts (head, fat body, integument, hindgut, midgut, and foregut) using Trizol methodology. Using Illumina paired-end sequencing technology and the Trinity platform, trimming and de novo assembly was performed, resulting in 44,567 contigs longer than 200 nt for a reunion of data from all transcriptomes, with a mean length of 1,095.27 nt. Transcripts were annotated using BLAST against different protein databanks (Uniprot/Swissprot, PFAM, KEEG, SignalP 4.1, Gene Ontology, and CAZY) and were compared for similarity using a Venn diagram. Differential expression patterns were studied for select genes through qPCR and FPKM comprising important protein families (digestive peptidases, glucosyl hydrolases, serine protease inhibitors and otopetrin), which allowed a better understanding of the insect’s digestion, immunity and gravity sensorial mechanisms. [ABSTRACT FROM AUTHOR]

High N loss and low N use efficiency (NUE), caused by high N fertilizer inputs and inappropriate fertilization patterns, have become important issues in the rice (Oryza sativa L.) growing regions of southern China. Changing current farmer fertilizer practice (FFP, 225 kg ha–1 N as three applications, 40% as basal fertilizer, 30% as tillering fertilizer and 30% as jointing fertilizer) to one—time root—zone fertilization (RZF, 225 kg ha–1 N applied once into 10 cm deep holes positioned 5 cm from the rice root as basal fertilizer) will address this problem. A two—year field experiment covering two rice growing regions was conducted to investigate the effect of urea one—time RZF on rice growth, nutrient uptake, and NUE. The highest NH4+–N content for RZF at fertilizer point at 30 d and 60 d after fertilization were 861.8 and 369.9 mg kg–1 higher than FFP, respectively. Rice yield and total N accumulation of RZF increased by 4.3–44.9% and 12.7–111.2% compared to FFP, respectively. RZF reduced fertilizer—N loss by 56.3–81.9% compared to FFP. The NUEs following RZF (mean of 65.8% for the difference method and 43.7% for the labelled method) were significantly higher than FFP (mean of 35.7% for the difference method and 14.4% for the labelled method). In conclusion, RZF maintained substantial levels of fertilizer—N in the root—zone, which led to enhanced rice biomass and N uptake during the early growth stages, increased fertilizer—N residual levels and reduced fertilizer—N loss at harvest. RZF produced a higher yield increment and showed an increased capacity to resist environmental threats than FFP in sandy soils. Therefore, adopting suitable fertilizer patterns plays a key role in enhancing agricultural benefits. [ABSTRACT FROM AUTHOR]

Salt stress is an important abiotic stressor affecting crop growth and productivity. Of the 20 percent of the terrestrial earth’s surface available as agricultural land, 50 percent is estimated by the United Nations Environment Program to be salinized to the level that crops growing on it will be salt-stressed. Increased soil salinity has profound effects on seed germination and germinating seedlings as they are frequently confronted with much higher salinities than vigorously growing plants, because germination usually occurs in surface soils, the site of greatest soluble salt accumulation. The growth of soybean exposed to 40 mM NaCl is negatively affected, while an exposure to 80 mM NaCl is often lethal. When treated with the bacterial signal compounds lipo-chitooligosaccharide (LCO) and thuricin 17 (Th17), soybean seeds (variety Absolute RR) responded positively at salt stress of up to 150 mM NaCl. Shotgun proteomics of unstressed and 100 mM NaCl stressed seeds (48 h) in combination with the LCO and Th17 revealed many known, predicted, hypothetical and unknown proteins. In all, carbon, nitrogen and energy metabolic pathways were affected under both unstressed and salt stressed conditions when treated with signals. PEP carboxylase, Rubisco oxygenase large subunit, pyruvate kinase, and isocitrate lyase were some of the noteworthy proteins enhanced by the signals, along with antioxidant glutathione-S-transferase and other stress related proteins. These findings suggest that the germinating seeds alter their proteome based on bacterial signals and on stress, the specificity of this response plays a crucial role in organ maturation and transition from one stage to another in the plants' life cycle; understanding this response is of fundamental importance in agriculture and, as a result, global food security. The mass spectrometry proteomics data have been deposited to the ProteomeXchange with identifier PXD004106. [ABSTRACT FROM AUTHOR]

Background: Long-term tillage has been shown to induce water stress episode during crop growth period due to low water retention capacity. It is unclear whether integrated water conservation tillage systems, such asspringdeepinter-row subsoiling with annual or biennial repetitions, can be developed to alleviate this issue while improve crop productivity. Methods: Experimentswere carried out in a spring maize cropping system on Calcaric-fluvicCambisolsatJiaozuoexperimentstation, northern China, in 2009 to 2014. Effects of threesubsoiling depths (i.e., 30 cm, 40 cm, and 50 cm) in combination with annual and biennial repetitionswasdetermined in two single-years (i.e., 2012 and 2014)againstthe conventional tillage. The objectives were to investigateyield response to subsoiling depths and soil water deficit(SWD), and to identify the most effective subsoiling treatment using a systematic assessment. Results: Annualsubsoiling to 50 cm (AS-50) increased soil water storage (SWS, mm) by an average of8% in 0–20 cm soil depth, 19% in 20–80 cm depth, and 10% in 80–120 cm depth, followed by AS-40 and BS-50, whereas AS-30 and BS-30 showed much less effects in increasing SWS across the 0–120 cm soil profile, compared to the CK. AS-50 significantly reduced soil water deficit (SWD, mm) by an average of123% during sowing to jointing, 318% during jointing to filling, and 221% during filling to maturity, compared to the CK, followed by AS-40 and BS-50. An integrated effect on increasing SWS and reducing SWD helped AS-50 boost grain yield by an average of 31% and biomass yield by 30%, compared to the CK. A power function for subsoiling depth and a negative linear function for SWD were used to fit the measured yields, showing the deepest subsoiling depth (50 cm) with the lowest SWD contributed to the highest yield. Systematic assessment showed that AS-50 received the highest evaluation index (0.69 out of 1.0) among all treatments. Conclusion: Deepinter-row subsoilingwith annual repetition significantly boosts yield by alleviating SWD in critical growth period and increasing SWS in 20–80 cm soil depth. The results allow us to conclude that AS-50 can be adopted as an effective approach to increase crop productivity, alleviate water stress, and improve soil water availability for spring maize in northern China. [ABSTRACT FROM AUTHOR]

Iron and zinc are important micronutrients for both the growth and nutrient availability of crop plants, and their absorption is tightly controlled by a metal uptake system. Zinc-regulated transporters, iron-regulated transporter-like proteins (ZIP), is considered an essential metal transporter for the acquisition of Fe and Zn in graminaceous plants. Several ZIPs have been identified in maize, although their physiological function remains unclear. In this report, ZmIRT1 was shown to be specifically expressed in silk and embryo, whereas ZmZIP3 was a leaf-specific gene. Both ZmIRT1 and ZmZIP3 were shown to be localized to the plasma membrane and endoplasmic reticulum. In addition, transgenic Arabidopsis plants overexpressing ZmIRT1 or ZmZIP3 were generated, and the metal contents in various tissues of transgenic and wild-type plants were examined based on ICP-OES and Zinpyr-1 staining. The Fe and Zn concentration increased in roots and seeds of ZmIRT1-overexpressing plants, while the Fe content in shoots decreased. Overexpressing ZmZIP3 enhanced Zn accumulation in the roots of transgenic plants, while that in shoots was repressed. In addition, the transgenic plants showed altered tolerance to various Fe and Zn conditions compared with wild-type plants. Furthermore, the genes associated with metal uptake were stimulated in ZmIRT1 transgenic plants, while those involved in intra- and inter- cellular translocation were suppressed. In conclusion, ZmIRT1 and ZmZIP3 are functional metal transporters with different ion selectivities. Ectopic overexpression of ZmIRT1 may stimulate endogenous Fe uptake mechanisms, which may facilitate metal uptake and homeostasis. Our results increase our understanding of the functions of ZIP family transporters in maize. [ABSTRACT FROM AUTHOR]

Weed competition is a significant problem in maize (Zea mays, L.) production in Sub-Saharan Africa. Better understanding of weed management and costs in maize intercropped with beans (Phaseolus vulgaris, L.) during transition to conservation agricultural systems is needed. Changes in weed population and maize growth were assessed for a period of three years at Bungoma where crops are grown twice per year and at Trans-Nzoia where crops are grown once per year. Treatments included three tillage practices: minimum (MT), no-till (NT) and conventional (CT) applied to three cropping systems: continuous maize/bean intercropping (TYPICAL), maize/bean intercropping with relayed mucuna after bean harvest (RELAY) and maize, bean and mucuna planted in a strip intercropping arrangement (STRIP). Herbicides were used in NT, shallow hand hoeing and herbicides were used in MT and deep hoeing with no herbicides were used in CT. Weed and maize performance in the maize phase of each cropping system were assessed at both locations and costs of weed control were estimated at Manor House only. Weed density of grass and forb species declined significantly under MT and NT at Manor House and of grass species only at Mabanga. The greatest declines of more than 50% were observed as early as within one year of the transition to MT and NT in STRIP and TYPICAL cropping systems at Manor House. Transitioning to conservation based systems resulted in a decline of four out of five most dominant weed species. At the same time, no negative impact of MT or NT on maize growth was observed. Corresponding costs of weed management were reduced by $148.40 ha-1 in MT and $149.60 ha-1 in NT compared with CT. In conclusion, farmers can benefit from effective and less expensive weed management alternatives early in the process of transitioning to reduced tillage operations. [ABSTRACT FROM AUTHOR]

Plant and soil properties cooperatively structure soil microbial communities, with implications for ecosystem functioning. However, the extent to which each factor contributes to community structuring is not fully understood. To quantify the influence of plants and soil properties on microbial diversity and composition in an agricultural context, we conducted an experiment within a corn-based annual cropping system and a perennial switchgrass cropping system across three topographic positions. We sequenced barcoded 16S ribosomal RNA genes from whole soil three times throughout a single growing season and across two years in July. To target the belowground effects of plants, we also sampled rhizosphere soil in July. We hypothesized that microbial community α-diversity and composition (β-diversity) would be more sensitive to cropping system effects (annual vs. perennial inputs) than edaphic differences among topographic positions, with greater differences occurring in the rhizosphere compared to whole soil. We found that microbial community composition consistently varied with topographic position, and cropping system and the rhizosphere influenced α-diversity. In July, cropping system and rhizosphere structured a small but specific group of microbes implying a subset of microbial taxa, rather than broad shifts in community composition, may explain previously observed differences in resource cycling between treatments. Using rank abundance analysis, we detected enrichment of Saprospirales and Actinomycetales, including cellulose and chitin degraders, in the rhizosphere soil and enrichment of Nitrospirales, Syntrophobacterales, and MND1 in the whole soil. Overall, these findings support environmental filtering for the soil microbial community first by soil and second by the rhizosphere. Across cropping systems, plants selected for a general rhizosphere community with evidence for plant-specific effects related to time of sampling. [ABSTRACT FROM AUTHOR]

Foxtail millet (Setaria italica) is an important food and fodder grain crop that is grown for human consumption. Production of this species is affected by several plant diseases, such as rust. The cultivar Shilixiang has been identified as resistant to the foxtail millet rust pathogen, Uromyces setariae-italicae. In order to identify signaling pathways and genes related to the plant’s defense mechanisms against rust, the Shilixiang cultivar was used to construct a digital gene expression (DGE) library during the interaction of foxtail millet with U. setariae-italicae. In this study, we determined the most abundant differentially expressed signaling pathways of up-regulated genes in foxtail millet and identified significantly up-regulated genes. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) analysis was used to analyze the expression of nine selected genes, and the patterns observed agreed well with DGE analysis. Expression levels of the genes were also compared between a resistant cultivar Shilixiang and a susceptible cultivar Yugu-1, and the result indicated that expression level of Shilixiang is higher than that of Yugu-1. This study reveals the relatively comprehensive mechanisms of rust-responsive transcription in foxtail millet. [ABSTRACT FROM AUTHOR]

Soil salinity is a major constraint to crop growth and yield. The primary and lateral roots of Arabidopsis thaliana are known to respond differentially to a number of environmental stresses, including salinity. Although the maize root system as a whole is known to be sensitive to salinity, whether or not different structural root systems show differential growth responses to salinity stress has not yet been investigated. The maize primary root (PR) was more tolerant of salinity stress than either the crown root (CR) or the seminal root (SR). To understand the molecular mechanism of these differential growth responses, RNA-Seq analysis was conducted on cDNA prepared from the PR, CR and SR of plants either non-stressed or exposed to 100 mM NaCl for 24 h. A set of 444 genes were shown to be regulated by salinity stress, and the transcription pattern of a number of genes associated with the plant salinity stress response differed markedly between the various types of root. The pattern of transcription of the salinity-regulated genes was shown to be very diverse in the various root types. The differential transcription of these genes such as transcription factors, and the accumulation of compatible solutes such as soluble sugars probably underlie the differential growth responses to salinity stress of the three types of roots in maize. [ABSTRACT FROM AUTHOR]

We integrated recent research on cardinal temperatures for phenology and early leaf growth, spikelet formation, early morning flowering, transpirational cooling, and heat- and cold-induced sterility into an existing to crop growth model ORYZA2000. We compared for an arid environment observed potential yields with yields simulated with default ORYZA2000, with modified subversions of ORYZA2000 and with ORYZA_S, a model developed for the region of interest in the 1990s. Rice variety ‘IR64’ was sown monthly 15-times in a row in two locations in Senegal. The Senegal River Valley is located in the Sahel, near the Sahara desert with extreme temperatures during day and night. The existing subroutines underestimated cold stress and overestimated heat stress. Forcing the model to use observed spikelet number and phenology and replacing the existing heat and cold subroutines improved accuracy of yield simulation from EF = −0.32 to EF =0.70 (EF is modelling efficiency). The main causes of improved accuracy were that the new model subversions take into account transpirational cooling (which is high in arid environments) and early morning flowering for heat sterility, and minimum rather than average temperature for cold sterility. Simulations were less accurate when also spikelet number and phenology were simulated. Model efficiency was 0.14 with new heat and cold routines and improved to 0.48 when using new cardinal temperatures for phenology and early leaf growth. The new adapted subversion of ORYZA2000 offers a powerful analytic tool for climate change impact assessment and cropping calendar optimisation in arid regions. [ABSTRACT FROM AUTHOR]