Biotic and abiotic modulation of drought effects on aphids and plants

Purpose Agricultural crops play a crucial role in food security and require commensurating environmental conditions, including adequate rainfall to ensure optimum growth. However, in the recent past, a reduction in the agriculture crop yield has been observed due to the deteriorating rainfall pattern. This paper aims to present a novel mathematical model to analyze the impact of rainfall on the growth of agriculture crops, as well as the impact of cloud seeding for promoting the rainfall, in case of less rainfall to ensure the optimum growth of agriculture crops. Design/methodology/approach The authors formulate a mathematical model assuming that the growth of agriculture crops wholly depends on rainfall. Also, agricultural crops can sustain and give optimal yields at a threshold of rainfall, after which rainfall negatively affects the growth rate of agriculture crops. Further, if the agriculture crops get insufficient rain to grow, the authors assume that cloud seeding agents are introduced in the regional atmosphere in proportion to the density of cloud droplets to increase rainfall. Findings This research shows that while cloud seeding agents boost crop yield, excessive rainfall poses significant risks on the yield. For any given value of π1 (conversion of cloud droplets into raindrops because of introduced cloud seeding agents), we have identified the threshold value of ϕ (introduction rate of cloud seeding agents into clouds) where crop yield can be maximized. Research limitations/implications This model highlights the delicate balance between rainfall and cloud seeding, offering policymakers valuable insights for maximizing agricultural crop yields. Originality/value This research provides strategies to mitigate crop loss due to unpredictable rainfall patterns.

The article provides a comprehensive assessment of the current state of the land potential of rural areas of the municipality. In order to assess the land potential of rural areas, using the example of the Krasnoarmeysky municipal district of the Krasnodar Territory, an analysis of the current use of the land fund of the municipal district was carried out. The concept of "land potential" is considered. The dynamics of changes in the land fund over the past 24 years is shown. The sectoral structure of the economy of the municipal district is shown. It was revealed that the main direction of development of the economy of the district is crop production, namely the cultivation of rice and other agricultural crops. The distribution of the land fund of the municipal district by land categories is considered. The composition of the land fund of the district by ownership is considered. The largest share is occupied by lands in state and municipal ownership, since most of the property of legal entities was seized. The influence of modern economic and environmental factors on the state of the land potential is analyzed and the main problems associated with its rational use are identified. To assess the effectiveness of the implementation of the land potential of the district, the indicators of sown area, crop yield in dynamics by year (2016-2021) are analyzed. The coefficient of arable land use is calculated. The effective indicator in plant growing is considered - the yield of agricultural crops, which showed the dynamics of the increase in grain and leguminous crops. The article emphasizes the importance of monitoring land resources for the formation of a strategy for sustainable development of rural areas and improving the quality of life of the population. It is concluded that not all the potential of the municipal district has been realized. The possibilities of increasing the efficiency of land ownership through the introduction of sustainable agricultural technologies are considered.

We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR) scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC) simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting it and comparing its forecasts against available historical data (1987-2011) for spring wheat (Triticum aestivum L.). The model was also validated for the 2012 growing season by comparing its forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1-4 % in mid-season and over-estimated by 1 % at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space.

1. Elevated CO2 can alter plant physiology and morphology, and these changes are expected to impact diet quality for insect herbivores. While the plastic responses of insect herbivores have been well studied, less is known about the propensity of insects to adapt to such changes. Genetic variation in insect responses to elevated CO2 and genetic interactions between insects and their host plants may exist and provide the necessary raw material for adaptation. 2. We used clonal lines of Rhopalosiphum padi (L.) aphids to examine genotype-specific responses to elevated CO2. We used the host plant Schedonorus arundinaceus (tall fescue; Schreb), which is capable of asexual reproduction, to investigate host plant genotype-specific effects and possible host plant-by-insect genotype interactions. The abundance and density of three R. padi genotypes on three tall fescue genotypes under three concentrations of CO2 (ambient, 700, and 1000 ppm) in a controlled greenhouse environment were examined. 3. Aphid abundance decreased in the 700 ppm CO2 concentration, but increased in the 1000 ppm concentration relative to ambient. The effect of CO2 on aphid density was dependent on host plant genotype; the density of aphids in high CO2 decreased for two plant genotypes but was unchanged in one. No interaction between aphid genotype and elevated CO2 was found, nor did we find significant genotype-by-genotype interactions. 4. This study suggests that the density of R. padi aphids feeding on tall fescue may decrease under elevated CO2 for some plant genotypes. The likely impact of genotype-specific responses on future changes in the genetic structure of plant and insect populations is discussed.

Agriculture plays a crucial role in the global economy and social stability, and accurate crop yield prediction is essential for rational planting planning and decision-making. This study focuses on crop yield Time-Series Data prediction. Considering the crucial significance of agriculture in the global economy and social stability and the importance of accurate crop yield prediction for rational planting planning and decision-making, this research uses a dataset containing multiple crops, multiple regions, and data over many years to deeply explore the relationships between climatic factors (average rainfall, average temperature) and agricultural inputs (pesticide usage) and crop yield. Multiple hybrid machine learning models such as Linear Regression, Random Forest, Gradient Boost, XGBoost, KNN, Decision Tree, and Bagging Regressor are adopted for yield prediction. After evaluation, it is found that the Random Forest and Bagging Regressor models perform excellently in predicting crop yield with high accuracy and low error.As agricultural data becomes increasingly rich and time-series prediction techniques continue to evolve, the results of this study contribute to advancing the practical application of crop yield prediction in agricultural production management. The integration of time-series analysis allows for more dynamic, data-driven decision-making, enhancing the accuracy and reliability of crop yield forecasts over time.

Drought evolution and its impact on the crop yield in the North China Plain

Deficit irrigation and magnetically treated water can be sustainable agronomic techniques to improve water use efficiency, plant development, and agricultural crop yield. A pot study was conducted to investigate the impact of distinct deficit irrigation regimes using magnetically treated and untreated water on the development and grain yield of mungbean [Vigna radiata (L.) R. Wilczek.] crops in a clayey soil of the Brazilian Cerrado. Mungbean plants were irrigated with magnetized water (MW) and non-magnetized water (N-MW) using four deficit irrigation regimes [20, 40, 60, and 80% of soil field capacity (FC)], considering a 2×4 factorial scheme, with four repetitions. The plants were maintained under distinct deficit irrigation regimes with MW or N-MW throughout the crop's entire growth and development cycle (80 days). Plant growth rate, relative chlorophyll index, plant morphological traits, production components, and crop grain yield were measured. The results showed that the deficit irrigation regime of 60% and 80% of FC improves the rate of plant growth and development and grain yield of the mungbean crop. Although mungbean is considered a drought-tolerant crop, low soil water availability causes severe inhibition of plant growth and development and drastically reduces the crop's grain yield. Irrigation with magnetized water had no beneficial effect on mungbean crops' plant growth and development and grain yield. Plant growth rate, plant height, shoot dry matter, relative chlorophyll index, and number of grains per pod positively correlate with grain yield. In contrast, root length has a highly negative correlation with the grain yield of mungbean crops.

<p>Climate change impacts on agriculture are subject to large uncertainties from a variety of sources. One of the most important sources of uncertainty is the uncertainty in the realization of climate change itself. In the absence of clear climate mitigation strategies and substantial uncertainties on population growth, economic development, technology and lifestyles, a very broad set of greenhouse gas emission scenarios has been developed to inform climate modeling. Climate models often differ in the spatial patterns of projected changes in particular with respect to changes in precipitation. The Coupled Model Intercomparison Project (CMIP5, CMIP6) provides a broad range of future climate change projections.</p><p>Crop models are often applied at selected sites or with global coverage, as in the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Global crop model applications have been shown to have some skill, but also add additional uncertainty, given that many processes cannot be calibrated properly for the lack of suitable reference data and because management information is largely absent (Müller et al., 2017).</p><p>However, already the computational power required to compute the comprehensive set of climate projections prohibits such applications. Instead, typically, small and largely random selections of climate scenarios are used to project impacts, such as agricultural crop yields. McSweeney and Jones (2016) find that a selection of 5 climate models as often applied, is insufficient to cover the range of projections in all regions.</p><p>Here we present initial results of a comprehensive global climate impact assessment for crop yields that explores the full range of the CMIP6 climate projection archive. For this, we use a set of 9 global gridded crop model emulators (Franke et al., 2019b) that were trained on a very large systematic input sensitivity analysis with up to 1404 global-coverage, 31-year simulation data sets per crop and crop model (Franke et al., 2019a). The training domain includes variations in atmospheric carbon dioxide (CO2) concentrations (4 levels from 360 ppm to 810 ppm), air temperature (7 levels from -1 to +6°C), water supply (8 levels from -50 to +30% and full irrigation), nitrogen fertilization (3 levels from 10 to 200 kgN/ha) and adaptation (2 levels: none and regained growing seasons) and thus represents an unprecedented rich data base for emulator training. The emulators, in form of grid-cell specific regression models with 27 coefficients, are computationally light-weight and can thus be applied to the full CMIP6 data archive.</p><p>We here present first results from this analysis, breaking down the different sources of uncertainty (emission concentration pathways, climate model, crop model). Results will help to interpret crop model simulations in general: the unstructured reduction of the uncertainty space from selecting a small number of climate scenarios by e.g. first availability and/or individual crop models has so far hampered to quantify the uncertainty in crop model projections.</p><p> </p><p>Franke (2019a) Geoscientific Model Development Discuss, 2019:1-30.</p><p>Franke (2019b)  Geoscientific Model Development, submitted</p><p>McSweeney & Jones, (2016) Climate Services, 1:24-29.</p><p>Müller (2017) Geoscientific Model Development, 10:1403-1422.</p><p> </p>

Crop stress has been identified as one of the problems that threaten global food security. Crop stress is an injurious deviation from the normal physiological processes that result into a decline in crop yields. It could be due to biotic factors (biotic stress, i.e. insect pests and disease pathogens) or abiotic factors (abiotic stress, i.e. drought, flooding, radiation and nutrient deficiencies). Agricultural crops normally undergo series of physiological processes (photosynthesis, respiration, stomatal functions and nutrition) during developmental stages of their life cycles that are sensitive to environmental conditions. The stressed environmental impact on the crops during growth and development leads to biochemical and morphological modifications in plant species. This chapter details the predominantly occurring abiotic stresses of crops that are directly or indirectly associated with the disruption of the normal growth and developmental processes in crops. The effects of abiotic stress in plants range from the qualitative and quantitative changes in the synthesis of type of proteins in crops to the disruption of the flower bud formation and pollination process in plant, as well as impaired nutrient uptake resulting in poor crop yields. About 51–82% crop yield in world agriculture is lost annually due to abiotic stress. The mechanisms of the four principal abiotic stresses (temperature, water, radiation, nutrients, etc.) are presented in the chapter. An understanding of the mechanisms of abiotic stress in agricultural crops could help farmers to optimize the crop productivity under the changing climate. This chapter therefore focuses on causes of the abiotic stresses affecting world food crops, their effects and possible stress coping strategies to promote global food sufficiency.

Crop yield and weed management in rainfed conservation agriculture

The increasing need for integrative and alternative medical therapies, especially in the aftermath of the COVID-19 epidemic, has emphasized the importance of medicinal plants in worldwide healthcare. These plants, which contain abundant bioactive secondary metabolites, provide a sustainable and cost-effective option for medicinal, adaptogenic, and immune-boosting purposes. Blooming medicinal plants that exist are at risk of becoming extinct because of excessive harvesting, deforestation, and wildfires. Medicinal plants have complex physiological defenses against stress, which are strengthened by their symbiotic relationship with endophytes. Endophytes are microbial colonies that live within plant tissues without causing harm and play a vital role in maintaining the health of plants by helping them to tolerate stress, promoting development, acquiring nutrients, synthesizing phytohormones, breaking down toxic substances, and improving plant resistance to environmental pressures such as high salt levels, lack of water, and exposure to heavy metals. In addition, endophytes have a role in managing biotic stress by engaging in antibiosis, synthesizing lytic enzymes, producing secondary metabolites, and regulating hormones. Their function in preserving the health and well-being of the host, ensuring proper nutrition intake, and enhancing resistance against pathogens highlights their potential as agents for biological control and biofertilization, providing a safer option compared to chemical pesticides. Endophytic inoculants have the potential to significantly transform crop yield in agriculture by reducing the impact of abiotic problems and improving soil health. This review critically evaluates causal studies and recent omics-based advances, highlighting their crucial significance for sustainable bioinoculant development and practical applications in climate-resilient agriculture.

The agricultural sector is complex, and farmers and agri-businesses face numerous decisions every day, influenced by various factors. Precise yield estimation is critical for effective agricultural planning. Data mining methods provide practical and effective solutions for these problems. Agriculture has always been a target for prediction and estimation techniques. Environmental and climatic conditions, soil variability, nutrient input levels, fertilizer combinations, and commodity/market prices make it necessary for farmers and businesses to use critical in-formation to make informed decisions. This project focuses on analyzing agricultural conditions and scenarios to find optimal parameters and data to maximize crop yield using data mining and machine learning techniques like k-Nearest Neighbors (k-NN), Naïve Bayes, Support Vector Machine (SVM), Linear Regression, etc. By mining crop, nutrient, soil, location, and climatic data and analyzing new, non-experimental data, the project aims to optimize yield and production, and make the agricultural sector more resilient to climatic change.

This is a proposed business model for a systems approach in adapting or optimizing plants towards novel products that briefly explains the growth and recent trends in plant systems using three modeling approaches: L-systems, Dynamical-biosystems analysis and cellular automata. The model discusses some of the attempts made by scientists in this field such as model development in Faba bean crops and the auto regulation in nodulation of plants and some other simulated models (ALAMEDA). Its application in horticulture or life style industry is proposed based on the meristem model applied in the roots of Zea mays. L. A grid based approach that effectively simulates and models competition among chosen species of plants on a simulated environment helps in the study of species survival amidst competition from other species, threat from pests, weeds etc. A model for the ecological risk assessment enables the study of impacts of a newly introduced plant variety on the environment and the implications on plants by the environment that helps in decision making and public acceptance. There are attempts in building the virtual cells, by choosing to apply living cell conditions with some basic cell components working effectively, leading to a thrust in the field of systems biology where this would be an application on all kind of cells in any organisms. A plant competition model in an agronomic perspective uses some of the plant models that help in increase of crop yield in agriculture.

Abstract: The shoot apical meristem of seed plants gives rise to the above-ground parts of the plants during development, including leaves, stems, and lateral meristems. Among them, two types of meristems, axillary meristem and vascular cambium, support the main lateral growth. In this review, we will discuss the regulatory network of lateral growth, focusing on recent progress made mainly in Arabidopsis thaliana, tomato and maize, including the identification of genes and their roles in controlling lateral meristems. In addition, we will summarize the latest evidence about how meristem affects yield-related traits, and discuss the strategies for modulating meristem regulatory genes so as to increase crop yield in agriculture.

In 1997, the Canadian Space Agency (CSA) and Canadian industry began developing enabling technologies for hyperspectral satellites. Since then, the CSA has conducted mission and payload concept studies in preparation for launch of the first Canadian hyperspectral earth observation satellite. This Canadian hyperspectral remote sensing project is now named the Hyperspectral Environment and Resource Observer (HERO) Mission. In 2005, the Preliminary System Requirement Review (PSRR) and the Phase A (Preliminary Mission Definition) were concluded. Recent developments regarding the payload include an extensive comparison of potential optical designs. The payload uses separate grating spectrometers for the visible near-infrared and short-wave infrared portions of the spectrum. The instrument covers a swath of >30 km, has a ground sampling distance of 30 m, a spectral range of 400-2500 nm, and a spectral sampling interval of 10 nm. Smile and keystone are minimized. Recent developments regarding the mission include requirements simplification, data compression studies, and hyperspectral data simulation capability. In addition, a Prototype Data Processing Chain (PDPC) has been defined for 3 key hyperspectral applications. These are: geological mapping in the arctic environment, dominant species identification for forestry, and leaf area index for estimating foliage cover as well as forecasting crop growth and yield in agriculture.