Crop Selection Research Articles

Enhancing resilience to climate change through prospective strategies for climate-resilient agriculture to improve crop yield and food security

It is important to highlight that agriculture is one of the sectors that will be directly affected by climate change scenarios in recent years. There are a number of processes such as drought, floods, temperature, salinity etc along with other forms of biotic factors posing significant impacts on crop yields of various crops due to their fluctuating scenario in the environment. Espousal of smart technologies and practices like smart crop and variety selection, efficient climate-based cropping system, agricultural water management (AWM), balanced fertilization, contingency planning, recarbonization of soils, no-till system, integrated farming system, site specific nutrient management etc are being advised to exercise in many regions for climate-resistant agriculture. These approaches minimize soil disruption and energy usages, enhance soil health and alleviate greenhouse gas discharges, minimize unproductive losses and improve efficiency of land and water use result in greater crop production with reduced fertilizer usage. As a part of this strategy, weather stations and mini-weather lookouts are set up at the village stage to register relevant weather observations like temperature, rainfall, wind speed and relative humidity etc to furnish customized agro-advisories to farmers, which reduce detrimental consequences attributed to the climate. A climate smart approach integrates farmer’s practices with related technologies, plans, institutes, policies and financial packages. So, initiating the choice of site-specific crops, development of customized technologies and tools, diversification of crops, improvement of climate-resistant crop varieties, syndication of forecasting tools and proper management of resources at the community level can effectively enhance climate resilience in agriculture.

Pinpointing the distinctive impacts of ten cover crop species on the resident and active fractions of the soil microbiome

Cover crops are used in agriculture to minimize soil erosion, prevent nutrient leaching and increase soil organic matter content. Cover crops can also be grown to stimulate the soil microbial community to improve soil biological conditions. Despite their widespread use, little is known about the impact of different cover crop species on the composition and activity of the soil microbiome. Here we investigate the effect of distinct cover crop species on the rhizosphere microbiome and characterize both the resident (ribosomal (r)DNA-based) and the potentially active (rRNA-based) fractions of the bacterial, fungal, protist and metazoan communities in the cover crops rhizosphere. We conducted a field experiment using 70-l bottomless containers in which we grew ten monocultures of commonly used cover crop species belonging to five plant families, and an unplanted control treatment (fallow). The total DNA and RNA were extracted from soil and the bacterial, fungal, protistan and metazoan communities were characterized using Illumina MiSeq sequencing. We found that all cover crop species significantly impacted the resident and the potentially active microbial communities in their rhizospheres. Cover crops exerted distinct selection strengths on the native microbial communities. For individual cover crops, the impacts on the resident and the potentially active microbial communities differed while showing similar overall tendencies. Oilseed radish (Brassicaceae) was shown to provoke the strongest microbial shifts, in part attributable to a promotion of the bacterial family Pseudomonadaceae and a repression of Microascaceae in the rhizosphere. Lentil (Fabaceae) induced a widespread stimulation of fungal taxa, including Trichocomaceae and fungal members of the Glomerales order, whereas black oat and hybrid ryegrass (both Poaceae) gave rise to relatively mild changes in the soil microbial communities. Analyses of rRNA-based rhizobiome data revealed that, except for phacelia, all cover crops induced an increase in microbial network complexity as compared to the fallow control. Data presented here provide a broad baseline for the effects of cover crops on four organismal groups, which may facilitate future cover crop selection to advance soil health.

Crop Yield Prediction Data Analytics in Indian Agriculture Using Deep Learning

India is a nation where agriculture and industries associated with it are the main sources of income for the populace. The country's economy primarily depends on agriculture. It is also one of the nations that experience severe natural disasters like floods or droughts, which ruin the crops. The current system uses regression approaches to estimate yield, such as Kernel Ridge, Lasso, and ENet algorithms, and it also employs the idea of stacking regression to improve the algorithms' performance. Utilise technology like data analytics and machine learning to analyse and mine this agricultural data to produce results that will be valuable to farmers for more productive and efficient crop yields. We suggest creating efficient methods to forecast agricultural yield under various climatic situations, which can assist farmers and other stakeholders in making knowledgeable decisions regarding agronomy and crop selection. The DNN algorithm, Multilayer Perceptrons (MLP), was employed. Additionally, the DL (Deep Learning) model's time and space complexity will increase with the addition of new characteristics that have minimal impact on the model's performance. The findings show that compared to the current classification technique, an ensemble technique provides more accurate prediction.

Women Farmer's Perspectives on Climate Change and Intention to Adopt Sustainable Agriculture

Purpose: This study highlights the significance of sustainable agriculture and delves into the views of women farmers on climate change and their aspirations to adopt sustainable farming practices. Theoretical framework: The opportunity comes in creating fresh methods, abilities, and interventions that foster innovation. The challenge is figuring out ways to mitigate its effects and search for alternatives. Women farmers in particular need creative behaviour and decision-making power to adapt to climate change since they face resource, cultural, societal, and personal constraints. Design/methodology/approach: To better understand how farmers think about climate change, sustainable agriculture, and related topics, we conducted structured, open-ended interviews with them. Twenty-six female farmers in the Himalayan state of Sikkim were chosen using convenience sampling. Qualitative data analysis tools were used to analyse the interview data. A web-based tool was used for sentiment analysis, which also contributed to identifying the themes. Findings: Positive sentiments were identified in the use of organic farming for sustainable agriculture. Most respondents believed that government support is required for large-scale use of organic farming. The respondents also acknowledged the positive effects of vermicomposting. The analysis showed that vermicomposting provides good health and growth to plants and crops. Resilient crop selection was identified as a method that assists in standing against weather fluctuations. Research, Practical & Social Implications: Crop selection based on seasons is vital for good yields. The thematic analysis also presented a positive response toward applying crop rotation, intercropping, and companion cropping. Compared to women with lower decision-making and inventive skills, women with these traits embraced more sustainable agricultural practises. Originality/Value: Giving women in agriculture greater autonomy will thereby enhance the adoption of sustainable agricultural techniques, so enhancing food security and reducing climate change. This outcome will help achieve the Sustainable Development Goals of gender equality and climate action set by the United Nations.

Optimizing organic no‐till planted soybean with cover crop selection and termination timing

AbstractResearch is needed to develop and optimize agroecological practices that can replace unsustainable managementand increase agricultural diversification. Field experiments were conducted in Maryland and New York to assess the effect of cover crop cultivar selection and cover crop termination soybean [Glycine max (L.) Merr.] planting (roll‐plant) date on cover crop growth stage, biomass, and reseeding; weed biomass; and soybean density and yield in organically managed no‐till planted soybean production. Twelve winter cereal cultivars—four each for barley (Hordeum vulgare L.), cereal rye (Secale cereale L.), and triticale (× Triticosecale Wittm. ex A. Camus.)—were seeded at equivalent densities and terminated at four roll‐plant dates. Although many cover crop cultivars had not yet reached the recommended growth stage for adequate termination (anthesis) at the earlier roll‐plant dates, greater soybean yields and similar cover crop reseeding, compared with later dates, were observed. Weed biomass was low across treatments despite cover crop biomass production below the recommended threshold of 8 Mg ha−1. When pooled over cultivars, triticale performed as well as cereal rye in terms of biomass production, weed suppression, and soybean yield. Within each species, differences in weed biomass and soybean yield were observed among cultivars, but inconsistent across site‐years. Results from this research also show that planting soybean before cover crop anthesis can help maximize yield. Farmers can improve management in cover crop‐based no‐till planted soybean by selecting regionally adapted winter cereal species and cultivars that perform well across a range of outcomes, rather than focusing exclusively on biomass production.

Optimizing Crop Selection and Production Planning in Agriculture: Applications of Linear Programming for Profit Maximization and Sustainability

Optimizing Crop Selection and Production Planning in Agriculture: Applications of Linear Programming for Profit Maximization and Sustainability

Drought Impact on the Morpho-Physiological Parameters of Perennial Rhizomatous Grasses in the Mediterranean Environment

The selection of non-food crops for bioenergy production in limiting environments is a priority for energy security and climate change mitigation. Therefore, more studies are needed on the interactions between species and environmental factors in specific sites which allows their selection for biomass production. The objective of this work is to study the impact of drought on the morpho-physiological parameters of perennial rhizomatous grasses Panicum virgatum L., Miscanthus × giganteus, and Arundo donax L. in the Mediterranean environment. Plants were grown on field and trials were carried out under support-irrigation and rainfed conditions during two consecutive years. Morpho-physiological parameters were measured in May, June and August, and dry biomass at the end of the experiment. Under rainfed conditions, A. donax presented the highest photosynthesis rate (25, 15 and 10 CO2 m−2 s−1), relative water content (85–90%), and dry biomass (~4500 g plant−1) compared with P. virgatum (20, 5 and 5 CO2 m−2 s−1, 65–85% RWC and ~1400 g plant−1) and Miscanthus (18, 4 and 0 CO2 m−2 s−1, 80–10% RWC and ~260 g plant−1). It is concluded that A. donax would be the best perennial rhizomatous grass to be used as bioenergy crop under Mediterranean conditions.

Design, synthesis and biological activity of novel triketone herbicides containing natural product fragments

4-Hydroxyphenylpyruvate dioxygenase (EC 1.13.11.27, HPPD) belongs to the non-heme Fe2+ − containing enzyme family and is an important enzyme in tyrosine decomposition. HPPD is crucial to the discovery of novel bleaching herbicides. To develop novel HPPD inhibitor herbicides containing the β-triketone motif, a series of 4-hydroxyl-3-(substituted aryl)-pyran-2-one derivatives were designed using the active fragment splicing method. The title compounds were synthesized and characterized through infrared spectroscopy (IR), 1H nuclear magnetic resonance (1H NMR), 13C nuclear magnetic resonance (13C NMR), and high-resolution mass spectrometry (HRMS). The X-ray diffraction method determined the single crystal structure of I-17. Preliminary bioassay data revealed that several novel compounds, especially I-12 and II-3, showed excellent herbicidal activity against broadleaf and monocotyledonous weeds at a dose of 150 g ai/ha. The results of crop selectivity and carotenoids determination indicated that compound I-12 is more suitable for wheat and cotton fields than mesotrione. Additionally, compound II-3 is safer for soybeans and peanuts than mesotrione. The inhibitory activity of Arabidopsis thaliana HPPD (AtHPPD) verified that compound II-3 showed the most activity with an IC50 value of 0.248 μM, which was superior to that of mesotrione (0.283 μM) in vitro. The binding mode of compound II-3 and AtHPPD was confirmed through molecular docking and molecular dynamics simulations. This study provides insights into the future development of natural and efficient herbicides.

Grass variety selection of microbial community composition is associated with differences in soil CO2 emissions

Plant-soil interactions play an important role in regulating C cycling in soils, with their management representing a potential means to regulate CO2 emissions and storage of C as soil organic matter (SOM). Therefore, crop selection, explicitly targeting beneficial plant-soil interactions may be a tool for sustainable agriculture and to support the maintenance of soil health, particularly in grassland systems which are important known stores of C. However, there is a need to understand whether the selection of different grass species and varieties can control the rates and products of C cycling, and the extent to which this is driven by the interaction with below ground microbial communities. In this study we used controlled environment microcosms to determine the emission of CO2, mineralization of soil organic matter (SOM) and plant driven microbial community selection from soil planted with one of 10 different grasses, representing 2 varieties from 5 different grass species. Total CO2 emissions significantly varied between soil planted with different grasses, with species explaining 43 % of the variation in CO2 emissions and variety 10 %. SOM mineralization and the composition of the microbial community as characterised by 16SrRNA gene profiling was also found to vary between soils planted with different grasses. Root biomass was a significant predictor (r2 = 0.17, P < 0.005) of total CO2 emissions, but not soil organic matter mineralization, while microbial community composition was found to explain 9 % of CO2 emissions. These results suggest that there is the potential to mitigate the production of CO2 from grasslands through the selection of grass varieties that promote the immobilization of C rather than its emission as CO2 and that this is both a function of plant traits and the soil microbial community.

Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review

Solar irradiation data is essential for the feasibility of solar energy projects. Notably, the intermittent nature of solar irradiation influences solar energy use in all forms, whether energy or agriculture. Accurate solar irradiation prediction is the only solution to effectively use solar energy in different forms. The estimation of solar irradiation is the most critical factor for site selection and sizing of solar energy projects and for selecting a suitable crop selection for the area. But the physical measurement of solar irradiation, due to the cost and technology involved, is not possible for all locations across the globe. Numerous techniques have been implemented to predict solar irradiation for this purpose. The two types of approaches that are most frequently employed are empirical techniques and artificial intelligence (AI). Both approaches have demonstrated good accuracy in various places of the world. To find out the best method, a thorough review of research articles discussing solar irradiation prediction has been done to compare different methods for solar irradiation prediction. In this paper, articles predicting solar irradiation using AI and empirical published from 2017 to 2022 have been reviewed, and both methods have been compared. The review showed that AI methods are more accurate than empirical methods. In empirical models, modified sunshine-based models (MSSM) have the highest accuracy, followed by sunshine-based (SSM) and non-sunshine-based models (NSM). The NSM has a little lower accuracy than MSSM and SSM, but the NSM can give good results in sunshine data unavailability. Also, the literature review confirmed that simple empirical models could predict accurately, and increasing the empirical model's polynomial order cannot improve results. Artificial neural networks (ANN) and Hybrid models have the highest accuracy among AI methods, followed by support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS). The increase in efficiency by hybrid models is minimal, but the complexity of models requires very sophisticated programming knowledge. ANN's most important input factors are maximum and minimum temperatures, temperature differential, relative humidity, clearness index and precipitation.

Wildfire and climate change amplify knowledge gaps linking mountain source-water systems and agricultural water supply in the western United States

Agricultural production in the western United States relies on water supplies from mountain source-water systems that are sensitive to impacts from wildfire and a changing climate. The resultant challenges to water supply forecasting directly impact agricultural producers and irrigation managers who rely on snowmelt and streamflow forecasts for crop selection and irrigation scheduling. To date, much research has focused on source-water system processes and agricultural production separately, but in this short communication we highlight a substantial need for new research connecting these disparate systems to improve forecasting accuracy. We identify key knowledge and data gaps regarding the functioning of source watersheds and their contributions to agricultural water resources with associated uncertainties in the context of wildfire and changing climate. In doing so, we encourage researchers, resource managers, and agricultural producers to consider the interdependency of water supply source and sink relationships through improved observations, monitoring, and modeling to ensure sustainable food production in the western US.

Real-Time Soil Monitoring with IoT Enabled System for Crop Prediction

Background: Sustainable crop production in the vast populated countries such as India has always been challenging problem since ancient times. The mismanagement of crops has led to reduced yields. It is of utmost important to study the Soil conditions and predict the suitable crop for cultivation under such conditions. Methods: In the present investigation a gadget is developed based on IoT and Arduino to monitor the soil based on parameters like pH, temperature and moisture, which helps in identifying soil conditions and decide the suitable crop and their management practices. For this purpose, the dataset of different crops has been passed with their parameters. Blynk application is used for remote monitoring. Hence the user can remotely view sensor data, log and view data, etc. Result: The results suggested that even after 24 hours of irrigation the black soil holds the high volumetric moisture. The device was correctly able to identify the suitable crops under the investigating environment viz Wheat, Lemon and Sunflower for Black soil. The system recommends the type of fertilizers that may be used and also the microbial condition of the soil. The system would enable farmers for better selection of crops under different conditions.

Engineering the Crop Microbiota Through Host Genetics.

The microbiota populating the plant-soil continuum defines an untapped resource for sustainable crop production. The host plant is a driver for the taxonomic composition and function of these microbial communities. In this review, we illustrate how the host genetic determinants of the microbiota have been shaped by plant domestication and crop diversification. We discuss how the heritable component of microbiota recruitment may represent, at least partially, a selection for microbial functions underpinning the growth, development, and health of their host plants and how the magnitude of this heritability is influenced by the environment. We illustrate how host-microbiota interactions can be treated as an external quantitative trait and review recent studies associating crop genetics with microbiota-based quantitative traits. We also explore the results of reductionist approaches, including synthetic microbial communities, to establish causal relationships between microbiota and plant phenotypes. Lastly, we propose strategies to integrate microbiota manipulation into crop selection programs. Although a detailed understanding of when and how heritability for microbiota composition can be deployed for breeding purposes is still lacking, we argue that advances in crop genomics are likely to accelerate wider applications of plant-microbiota interactions in agriculture.

Multi-criteria Agriculture Recommendation System using Machine Learning for Crop and Fertilizesrs Prediction.

Agriculture plays an essential role in the economies of developing countries such as India and contributes significantly to the gross domestic product (GDP). The escalation in population has led to an upsurge in food demand. Numerous challenges such as the selection of crops, fertilizers, and pesticides without considering the various parameters like types of soil, water requirement, temperature conditions, and profitability analysis of crops for a particular region may lead to degradation in the quality of crop, yield and profitability. With the advancement of Computational technologies, researchers are working on recommending crops according to soil condition, water requirement, and market profitability along with fertilizers recommendation, disease identification, and pesticide recommendation. Through this research, we propose a machine learning-based crop and fertilizer recommendation algorithm called AgriRec. We have utilized soil properties, water level, farm size, and minimum support price of crop and design a machine learning model which predicts crops for different seasons. Further, we propose another mechanism that processes the properties/details of soil, crop, and fertilizer to envisage a combination of fertilizer(s) for a given pair of soil and crop. Our algorithm is tested for 5000 land samples of Gujarat region with 24 different crop and it successfully recommends crops with 95.85% accuracy and fertilizer with 92.11% accuracy with 4 times better performance as compared to existing benchmark recommendation approaches.

Hybrid Intuitionistic Fuzzy Entropy-SWARA-COPRAS Method for Multi-Criteria Sustainable Biomass Crop Type Selection

To select a biomass crop type of the highest sustainability for the purpose of producing biofuel is recognized as a problem of the multi-criteria decision analysis (MCDA) type, as it comprises different conflicting criteria. To effectively address this problem, the present paper introduces a novel integrated approach using the complex proportional assessment (COPRAS) method under the intuitionistic fuzzy sets (IFSs). The proposed approach works based on the IFSs operators as well as an innovative process utilized in evaluating the attributes’ weights. To evaluate these weights, the subjective weights using the step-wise weight assessment ratio analysis (SWARA) model are integrated with the objective weights achieved using an entropy-based approach in order to attain more realistic weights. As MCDA problems inevitably suffer from different degrees of uncertainty, the proposed approach could be of great help to those who are required to make decisions in uncertain settings. The paper took into consideration a sustainable biomass crop selection problem to exemplify the effectiveness of the presented approach in handling real MCDA problems. Moreover, a sensitivity analysis with respect to the diverse values of the attributes is presented in order to assess the stability of the introduced model. This study reveals that the combination of the objective and subjective weights enhances the stability of the introduced approach with diverse attribute weights. Finally, the results of the introduced model are compared to some existing intuitionistic fuzzy information-based methods. The findings of the comparison confirm the efficiency of the present approach in performing the defined tasks under uncertain environments.

Decision Support System for Suitability of Horticultural Agricultural Plant Types with Land Conditions Using Interpolation Profile Matching

Horticultural crops are the most produced by farmers in the Napugera Village area because they are more profitable to cultivate because they are short in life and from an economic point of view they are obtained faster. However, has often experienced obstacles, one of which is determining the suitability of agricultural land for horticultural crops. Determining the selection of horticultural crops for the types of red onion, red red chili, and tomato crops on a field based on land conditions is very necessary for farmers in the Napugera Village area as a support for decision-making. There are 8 criteria assessed including temperature, rainfall, air humidity, soil type, soil texture, soil ph, land slope and topography. In this study, a profile-matching interpolation method was developed for a decision-making system that can be used as a tool in providing decisions on plants that are suitable for planting in a field easily, quickly, and accurately. The profile-matching interpolation method is one of the methods of scoring data. The results showed that the type of onion horticultural crop with a final value of 3,764 is suitable for cultivation in the Napugera Village area.

A Review of Sustainable Pillars and their Fulfillment in Agriculture, Aquaculture, and Aquaponic Production

Focusing on new food production methods and sustainable pillars’ accomplishments has changed the definition of sustainable pillars themselves. Moreover, some general characteristics of the main pillars can be redefined in separate dimensions to better explain their positive sustainable impacts. Therefore, the main objective of this research is to redefine the sustainable pillars linked to food production and review the most important cultural and technological sustainability impacts they have, in addition to the three classic pillars: economic, social, and environmental sustainability. Cultural and technological sustainability are increasingly important complements to the traditional sustainability concept. Furthermore, new food production technologies and systems are influenced by ancient production methods, as well as by profitable crop selection. Traditional agricultural and aquaculture production in relation to more recent aquaponic production concepts are still a major part of global food security, but the better usage of waste materials or residues generates a more favorable agroecological impact. In conclusion, constantly redefining the sustainable pillars in the context of sustainable food production methods and proving the viability of their general production impacts is important.

Адаптивная система функционирования агромелиоративного ландшафта в условиях Кабардино-Балкарской Республики

The article proposes an adaptive system for the functioning of the agro-reclamation landscape in the conditions of the Kabardino-Balkarian Republic. A procedure for the formation and implementation of the technological scheme of the specified system has been developed. Using the developed system, the selection of appropriate agricultural crops for 60 microsubzones of three functional zones of the Kabardino-Balkarian Republic was made. An algorithm for solving some functional problems of managing production processes is given. Keywords: AGRICULTURAL LANDSCAPE, MONITORING, FUNCTIONING, VARIABILITY, STABILITY, FUNCTIONAL AND ADAPTIVE POTENTIAL

Crop Prediction using PySpark

Abstract: Agriculture is a crucial sector for many economies worldwide. The success of agriculture is largely dependent on the crops grown, which in turn rely on various factors such as soil properties. To aid farmers in making informed decisions about crop selection based on soil properties, crop prediction systems have been developed. This work presents a crop prediction system that utilizes soil properties and PySpark. The main aim of this work is to forecast the most suitable crop to be grown in a given area based on its soil properties. The system takes input data of different soil parameters, such as pH, Nitrogen, etc., and uses machine learning algorithm to create a model for crop prediction. PySpark is used to manage large-scale soil data. The work's results demonstrate the effectiveness of PySpark in handling and analyzing large-scale data for crop prediction. The accuracy and performance of the system can be further improved by incorporating additional features and refining the Random forest algorithm is used in the model.

Markov Chain Rainfall Probability Model and Rainy Season Length in Kendrapara District, Odisha for Efficient Crop Management Strategies

Systematic information on rainfall patterns and distribution is essential for crop selection, adopting improved agronomic practices, designing rainwater harvesting structures, and conserving rainwater. This study analyzed the probabilities of rainfall occurrence to know the rainfall onset, withdrawal, and rainy season length for different blocks of Kendrapara district, Odisha. The rainfall analysis showed that the kharif season shares 75-78% of annual rainfall with July and August being the wettest. Seasonal rainfall showed a significant increase during kharif season while the decreasing trend was observed during the rabi and summer seasons. Markov chain analysis showed that the weekly probability for 20-mm rainfall and consecutive two weeks is more than 70% during SMW 25-38. During the June-October period, assured rainfall at 80% probability is greater than 800 mm in four blocks, while in other blocks it ranged between 610-779 mm. Assured rainfall during the rabi and summer season is uncertain, however, a 105-126 mm rainfall is received in May in many blocks, useful for summer ploughing and green manure crops. Rainy season length analysis at an 80% probability level revealed that only in 14-16 weeks’ rain occurs, thereby, only short-duration paddy can be grown with lower risk. By utilizing October rainfall and conserved soil moisture, short-duration pulses/oilseeds could be grown in all the blocks. However, kharif paddy must be sown/transplanted at the optimum time