Maximize Crop Production Research Articles

Performance of Rice-Wheat Cropping System under Different Production Systems in Irrigated Subtropics of Jammu

The present study was conducted during 2022-23 and 2023-24 at the Research Farm, FSR Centre, Sher-e-Kashmir University of Agricultural Sciences and Technology of Jammu, Main Campus Chatha. The experimental site is located at a latitude of 32°40′ N, longitude of 74°58′ E, and an altitude of 332 m above mean sea level. The region experiences an annual rainfall of 1050-1115 mm, with 70% occurring between June and September. The study evaluated the performance of a rice-wheat cropping system under four production systems: P1 (Absolute control), P2 (Organic farming), P3 (Conventional farming), and P4 (Natural farming). Results revealed that conventional farming consistently achieved significantly higher productivity in both crops. For rice, conventional farming recorded the highest grain yield (28.49 q/ha) and straw yield (55.06 q/ha), outperforming organic farming (25.53 q/ha and 53.45 q/ha) and natural farming (24.67 q/ha and 51.46 q/ha). Similarly, for wheat, conventional farming resulted in the highest grain yield (39.98 q/ha) and straw yield (53.32 q/ha), compared to organic farming (35.52 q/ha and 50.13 q/ha) and natural farming (30.87 q/ha and 48.09 q/ha). A slight improvement in grain and straw yields was observed during the second year across all production systems, except the absolute control. The findings demonstrate the superior performance of conventional farming for maximizing crop productivity in the irrigated subtropical conditions of Jammu while highlighting the potential for further optimization of organic and natural farming systems for sustainable agriculture.

Biofeedback control of photosynthetic lighting using real-time monitoring of leaf chlorophyll fluorescence.

Optimizing photosynthetic lighting is essential for maximizing crop production and minimizing electricity costs in controlled environment agriculture (CEA). Traditional lighting methods often neglect the impact of environmental factors, crop type, and light acclimation on photosynthetic efficiency. To address this, a chlorophyll fluorescence-based biofeedback system was developed to adjust light-emitting diode (LED) intensity based on real-time plant responses, rather than using a fixed photosynthetic photon flux density (PPFD). This study used the biofeedback system to maintain a range of target quantum yield of photosystem II (ΦPSII) and electron transport rate (ETR) values and to examine if the adjustment logic (ΦPSII or ETR-based) and crop type influence LED light intensity. The system was tested in a growth chamber with lettuce (Lactuca sativa) 'Green Towers' and cucumber (Cucumis sativus) 'Diva' to maintain six ETR levels (30, 50, 70, 90, 110, 130 μmol·m-2·s-1) and five ΦPSII levels (0.65, 0.675, 0.7, 0.725, 0.75) during a 16-hour photoperiod. The ETR-based biofeedback quickly stabilized the target ETR within 30-45 minutes, whereas the ΦPSII-based system needed more time. The system adjusted light intensities according to target values, acclimation status, and crop-specific responses. For example, to maintain a target ETR of 130 μmol·m-2·s-1, the gradual increase in ΦPSII over time due to light acclimation allowed the required PPFD to decrease by 35 μmol·m-2·s-1. Lettuce showed higher photosynthetic efficiency and lower heat dissipation than cucumber, leading to higher PPFD adjustments for lettuce. This biofeedback system effectively controls LED light, optimizing photosynthetic efficiency and potentially reducing lighting costs.

Optical detection of single and multiple seeding using an innovative shape-recognition algorithm

The ability to accurately and continuously monitor seeding quality allows farmers to improve their seeding practices, maintain appropriate seed spacing, and maximize crop productivity. The precise detection and counting of individual seeds, especially those that pass through and overlap in pairs or multiples, is a major challenge in developing seed monitoring systems. In this research, a novel shape-based algorithm is presented that utilizes seven pairs of 3-mm infrared LEDs to efficiently recognize and count both single seeds and overlapping seeds. The outputs of the receivers were converted to binary signals by comparing them to samples taken when no seed flow was present. Each infrared receiver acted as a pixel, and to recognize and count multiple seeds, their outputs were temporarily stored in a matrix with seven columns. When the seeds passed the sensor, their outputs were analyzed together using an innovative and systematic clustering algorithm. This enables shape-based processing and recognition of passing seeds regardless of type and size, effectively reducing counting errors. The developed system was subjected to testing using seven different seed types on a conveyor belt, while one seed type underwent testing on a precision corn seeder in the laboratory. The results indicated a high level of agreement between the sensor data and the actual seed rates. The average counting accuracy for popcorn, hybrid corn, chickpea, pinto beans, coated tomato, mung beans, and soybean seeds on the conveyor belt platform was 0.99, 0.99, 0.98, 0.96, 0.8, 0.99, and 0.99, respectively. The results showed that the accuracy of the algorithm increases with increasing sphericity and size of the seeds relative to the distance between the adjacent LEDs. Moreover, the counting accuracy exceeded 97% for all rates on the precision corn seeder platform, with an average accuracy of 0.986.

Daily Light Integral Maps for Agriculture Lighting Design in Spain

The Daily Light Integral (DLI) is a key metric to optimise plant cultivation, influence photosynthesis, morphology, yield, and nutritional quality. Our study focuses on (i) the creation of the spatiotemporal DLI map for a representative Mediterranean country, Spain, (ii) the presentation of the improved semi-automatic DLI mapping workflow, (iii) the challenge of the comparability of different DLI value scalings in global (continent) and local context (country). DLI maps provide essential insights into the spatial and temporal distribution of natural and artificial light, critical for optimizing agricultural practices and enhancing crop yields. Spain's diverse topography and climate create significant regional and seasonal variations in DLI values. Using data from SunTracker Technologies and global weather stations, we developed detailed DLI maps at 30-meter resolution. These maps reveal high DLI values in coastal and southern regions, especially in summer, and lower values in northern and interior regions during winter. Our findings emphasize the importance of tailored lighting strategies in agriculture, suggesting higher scaling (from 5 to 2 mol·m−2·d−1) resolution for European contexts. This research underpins the development of smart agricultural systems that both quantitatively and qualitatively regulate lighting conditions (supplementary light and shading management) to maximize crop productivity and sustainability. Integrating DLI maps with IoT and AI technologies offers predictive insights for precision agriculture, highlighting the need for advanced training to leverage these tools effectively.

Performance of Urea-Based Enhanced Efficiency Fertilizers Under Sidedress Nitrogen Placement Methods

Enhanced efficiency nitrogen fertilizer (EENF) is a viable N management tool that can minimize N loss and maximize crop productivity. Research on the efficiency of EENF under relatively newer N-sidedress placement methods for liquid fertilizers, such as Y-Drop dribble, is limited, hence the necessity to address this knowledge gap. A two-year field trial was conducted to evaluate ammonia volatilization, grain quality, and grain yield from surface-applied EENF forms of urea and EENF forms of urea ammonium nitrate (UAN) applied under four placement methods in a rainfed no-till corn production system. The EENF forms of urea evaluated in this trial included Environmentally Smart Nitrogen, SuperU, and ANVOL-treated urea, while ANVOL-treated UAN was the EENF form of UAN. All the urea fertilizers were surface broadcasted while the UAN fertilizers were broadcasted, dribbled in between rows, Y-Drop dribbled, and injected behind colters. Among the non-EENF, urea volatilized the most ammonia (23.1% of total N applied) compared to UAN. Broadcast UAN was much more susceptible to ammonia volatilization than dribbled UAN in between rows. In this trial, the EENF forms of urea and UAN reduced ammonia loss by 65 to 94 and 50 to 51%, respectively, compared to their corresponding non-EENF forms. In 2021, the grain yield of the non-EENF forms of UAN was similar to the EENF forms of the corresponding placement methods. In contrast, there was an additive yield effect from all the urea forms of EENF. A significant negative relationship was observed between ammonia loss and grain protein, grain N, and grain yield; hence, placement methods with higher ammonia loss potential are prone to yield loss.

Mapping Soil Surface Moisture of an Agrophytocenosis via a Neural Network Based on Synchronized Radar and Multispectral Optoelectronic Data of SENTINEL-1,2—Case Study on Test Sites in the Lower Volga Region

In this article, the authors developed a novel method for the moisture mapping of the soil surface of agrophytocenosis using a neural network based on synchronized radar and multispectral optoelectronic data from Sentinel-1,2. The significance of this research lies in its potential to enhance precision farming practices, which are increasingly vital in addressing global agricultural challenges such as water scarcity and the need for sustainable resource management. To verify the developed method, data from two experimental plots were utilized. These plots were located on irrigated soybean crops, with the first plot situated on the right bank (plot No. 1) and the second on the left bank (plot No. 2) of the lower Volga River. Two experimental soil moisture geodatasets were created through measurements and geo-referencing points using the gravimetric method (for plot No. 1) and the proximal sensing method (for plot No. 2) employing the Soil Moisture Sensor ML3-KIT (THETAKIT, Delta). The soil moisture retrieval algorithm was based on the use of a neural network to predict the reflection coefficient of an electro-magnetic wave from the soil surface, followed by inversion into soil moisture using a dielectric model that takes into account the soil texture. The input parameter of the neural network was the ratio of the microwave radar vegetation index (calculated based on Sentinel-1 data) to the index (calculated based on the data of multispectral optoelectronic channels 8 and 11 of Sentinel-2). The retrieved soil moisture values were compared with in situ measurements, showing a determination coefficient of 0.44–0.65 and a standard deviation of 2.4–4.2% for plot No. 1 and similar metrics for plot No. 2. The conducted research laid the groundwork for developing a new technology for remote sensing of soil moisture content in agrophytocenosis, serving as a crucial component of precision farming systems and agroecology. The integration of this technology promotes sustainable agricultural practices by minimizing water consumption while maximizing crop productivity. This aligns with broader environmental goals of conserving natural resources and reducing agricultural runoff. On a larger scale, data derived from such studies can inform policy decisions related to water resource management, guiding regulations that promote efficient water use in agriculture.

Optimizing yield and water productivity in summer mung bean (Vigna radiata L.) through crop residue management and irrigation strategies

A multi-season research trial entitled ‘crop residue management effects on yield and water productivity of summer mung bean (Vigna radiata L.) under different irrigation regimes in Indian Punjab’ was conducted at Punjab Agricultural University (PAU), Regional Research Station (RRS), Bathinda, during rabi 2020 and 2021. The field experiment was conducted in a split-plot layout with nine treatment combinations and replicated thrice. The treatments consisted of T1 (no wheat residue along with tillage), T2 (leftover wheat residue with zero tillage), and T3 (incorporated wheat residue along with tillage) in main plots and irrigation regimes viz., I1 (vegetative growth and flowering stage), I2 (vegetative growth, flowering, and pod filling stage) and I3 (vegetative growth, flowering, pod formation and pod filling stage) in sub-plots, respectively. The growth and yield attributing characters were significantly higher under T3 than T1 but statistically at par with T2 during both years. An increase of 24.1% and 19.0% in grain yield was found in residue incorporation (T3) and residue retention (T2) over residue removal (T1), respectively. Maximum crop and irrigation water productivity was observed under T3 due to reduced water use and increased yield. Among the irrigation regimes, the I3 recorded significantly higher grain yield (0.70 and 0.79 t ha− 1) than I1. It was at par with I2 during both years due to higher irrigation frequency at the pod formation and pod filling stage. Crop water productivity (CWP) was higher under I3, whereas irrigation water productivity (IWP) was higher under I1 during both years. Additional irrigation at the pod-filling stage increased the grain yield by 36.5%, and two additional irrigations at the pod-formation and pod-filling stage further increased yield by 46.2% compared to only two irrigations at the vegetative and flowering stages. The treatment combinations of T2I2 and T3I2 outperformed T1I3 in terms of growth and yield attributing characters viz. plant height, dry matter accumulation (DMA), leaf area index (LAI), pods plant− 1, seeds pod− 1, and 1000-seed weight, which resulted in higher grain yield in these treatment combinations over T1I3. Applying crop residue can help minimize water use and increase crop water productivity. So, retaining crop residue in summer mung bean resulted in saving irrigation water due to lesser evapotranspiration from the soil surface.

Warm growing season activates microbial nutrient cycling to promote fertilizer nitrogen uptake by maize

The influence of nitrogen (N) inputs on soil microbial communities and N uptake by plants is well-documented. Seasonal variations further impact these microbial communities and their nutrient-cycling functions, particularly within multiple cropping systems. Nevertheless, the combined effects of N fertilization and growing seasons on soil microbial communities and plant N uptake remain ambiguous, thereby constraining our comprehension of the optimal growing season for maximizing crop production. In this study, we employed 15N isotope labeling, high-throughput sequencing, and quantitative polymerase chain reaction (qPCR) techniques to investigate the effects of two distinct growing seasons on microbial communities and maize 15N uptake ratios (15NUR). Our results showed that the warm growing season (26.6 °C) increased microbial diversity, reduced network complexity but enhanced stability, decreased microbial associations, and increased modularization compared to the cool growing season (23.1 °C). Additionally, the warm growing season favored oligotrophic species and increased the abundance of microbial guilds and functional genes related to N, phosphorus, and sulfur cycling. Furthermore, alterations in the characteristics of soil microbial keystone taxa were closely linked to variations in maize 15NUR. Overall, our findings demonstrate significant seasonal variations in soil microbial diversity and functioning, with maize exhibiting higher 15NUR during the warm growing season of the double cropping system.

Spatial Variation of Microclimatic Parameters Inside a Polyhouse with Fog Cooling System

The importance of protected cultivation is increasing day by day due to its capability of producing crop all round the year and increase in the crop productivity. This advantage of protected cultivation can be efficiently achieved by adapting the polyhouse cultivation. To obtain the maximum crop productivity inside a polyhouse, it is necessary to understand the variability of microclimate inside it. The present study aims to understand the spatial variability of microclimate inside a naturally ventilated polyhouse installed with fog cooling system during summer season. The main aim of the study is to understand the effect of fogging system in spatial variability of microclimate. For the study, the data of microclimatic parameters were collected daily from April to June at four different time intervals 8.30 AM, 12.30 PM, 4.30 PM and 8.30 PM. The data recorded was then analyzed in Golden Surfer Software to generate contour plots of weekly averages of microclimate parameters. The analysis revealed that the temperature and relative humidity varied across the polyhouse depending upon the wind velocity and its distribution pattern. The light intensity varied based upon the orientation of polyhouse and the position of sun. Carbon dioxide concentration varied based on temperature, light intensity, crop transpiration and respiration. It can be concluded that, inside a naturally ventilated polyhouse, the microclimate varied both spatially and temporally with fogging system also due to wind effect. Thus, having accurate understanding of the microclimate inside a polyhouse helps to maintain optimal conditions for plant growth and thus improve crop yield and productivity.

Insights from a 19-year field study: optimizing long-term nutrient supply strategies for enhanced crop productivity and nutritional security in rice–wheat systems

What nutrient supply options can ensure maximum productivity and optimize the nutrient uptake of rice–wheat system (R-W system)? an experiment started in the year 1998 (19-year-old) to examine the impact of optimal nutrient supply (NS) strategies to maximize crop productivity and nutritional security in R-W system. To determine the best nutrient management strategies (BNMS), seven different NS methods were tested. These included (organic and mineral fertilizers), as well as combinations such as integrated plant nutrition system (IPNS), IPNS + B (berseem), and IPNS + C (cowpea), with the aim of enhancing the productivity and nutrient absorption of R-W system. Results showed that rice grain yield notably wide-ranging from 1.61 to 5.81 t ha−1 under different NS options and highest rice grain yield (mean of 19 years) was observed at IPNS + C (5.81 t ha−1), which was at par with IPNS (5.79 t ha−1), STCR (soil test crop response) (5.76 t ha−1) and IPNS + B (5.67 t ha−1) followed by 100% recommended dose fertilizer NPK (4.41 t ha−1), which equality with OF (organic farming) (4.04 t ha−1) and lowest was recorded in control plot (1.61 t ha−1). Wheat grain yield varied significantly from 1.43 to 5.86 t ha−1 under different NS options. The highest yield (mean of 19 years) was observed in treatments IPNS + B (5.86 t ha−1), IPNS (5.77 t ha−1), IPNS + C (5.48 t ha−1), and STCR (5.45 t ha−1), followed by OF at 4.49 t ha−1, NPK at 3.76 t ha−1, with the lowest in the control plot at 1.43 t ha−1. Additionally, total phosphorus and sulfur accumulation in rice (grain: 8.41 to 39.09 kg ha−1; straw: 6.02 to 23.54 kg ha−1) varied significantly across nutrient supply treatments. Overall, adoption of IPNS integration with legumes (IPNS + B and IPNS + C), can significantly improve productivity and nutrient accumulation in R-W systems. Incorporating legumes into farming practices is advised for sustained productivity and nutritional benefits.

Natural variation in the adjustment of primary metabolism determines ammonium tolerance in the model grass Brachypodium distachyon.

Nitrogen (N) fertilization is essential to maximize crop production. However, around half of the applied N is lost to the environment, causing water and air pollution and contributing to climate change. Understanding the natural genetic and metabolic basis underlying plants N use efficiency is of great interest to attain an agriculture with less N demand and thus more sustainable. The study of ammonium (NH4+) nutrition is of particular interest, because it mitigates N losses due to nitrate (NO3-) leaching or denitrification. In this work, we studied Brachypodium distachyon, the model plant for C3 grasses, grown with NH4+ or NO3- supply. We performed gene expression analysis in the root of the B. distachyon reference accession Bd21 and examined the phenotypic variation across 52 natural accessions through analyzing plant growth and a panel of 22 metabolic traits in leaf and root. We found that the adjustment of primary metabolism to NH4+ nutrition is essential for the natural variation of NH4+ tolerance, notably involving NH4+ assimilation and phosphoenolpyruvate carboxylase (PEPC) activity. Additionally, genome-wide association studies (GWAS) indicated several loci associated with B. distachyon growth and metabolic adaptation to NH4+ nutrition. We found that the GDH2 gene was associated with the induction of root glutamate dehydrogenase activity under NH4+ nutrition and that two genes encoding malic enzyme were associated with leaf PEPC activity. Altogether, our work underlines the value of natural variation and the key role of primary metabolism to improve NH4+ tolerance.

Magnificent Bio-Management of Pesticides in Agriculture Soil

Following World War II, the agrochemical industry burgeoned, introducing diverse chemical agents like pesticides and herbicides to enhance crop production. These compounds, including herbicides, insecticides, and fungicides, are toxic and persist in the environment, causing soil, water, and air pollution and disrupting ecological balance. Farmers, heavily reliant on soil, initially use these chemicals to boost productivity, but over time, they become detrimental, accumulating in organisms and harming trophic levels. Addressing this challenge involves improving soil quality and eco-friendliness for maximum crop production. Indigenous microbial consortia, comprising bacteria and fungi, offer a cost-effective and eco-friendly solution by metabolizing and bio-remediating toxic compounds. This review focuses on their role in removing commercial xenobiotic pesticides that harm soil.

Laboratory Evaluation of Soil Moisture Sensors for Precision Irrigation in Agriculture

Water is essential for agricultural production and food security, making efficient water use critical. Accurate measurement of soil moisture is vital for scheduling irrigation, ensuring that crops receive the right amount of water at the right time. This prevents both under- and over-irrigation, conserving water and maximizing crop production. Currently, digital soil moisture sensors are used for their accuracy and instant measurement capabilities. The soil moisture sensor's functionality was evaluated through observations of four Capacitive soil moisture sensors 1.2 (SMS A, B, C, D) at various moisture levels, with gravimetric method verification. The calibrated linear equations demonstrated a strong linear relationship for each sensor. The coefficient of determination (R2) were also found to be 0.92, 0.93, 0.91 and 0.93 respectively. These R² values indicate a strong linear relationship for each sensor. An automated drip irrigation module was developed by integrating these soil moisture sensors with the Arduino platform. The system activates the irrigation motor when soil moisture content falls below the field capacity or a desired set value, and turns it off once the required moisture level is reached. This sensor-based automated drip irrigation module enables farmers to irrigate their fields precisely and efficiently, delivering the right quantity of water at the right time. The system is also suitable for deficit irrigation practices, improving crop yield and water use efficiency.

Sweet Pepper Farming Strategies in Response to Climate Change: Enhancing Yield and Shelf Life through Planting Time and Cultivar Selection

Climate change is an important concern worldwide. This huge change is causing a negative impact on crop productivity throughout the whole world and shows some drastic effects on sensitive crops including sweet pepper and other kinds of chilies. These stresses have a negative effect on vegetable growth, fruit quality, and fruit yield. Besides these stresses, accurate planting time is the utmost factor in increasing the crop potential and its productivity. Therefore, this study aimed to evaluate the optimal planting date and best-performing cultivar for sweet pepper to maximize crop productivity, growth, yield, and fruit shelf life under the impacts of climate change. The experiment was conducted in Multan, Pakistan (30° 25 N and 71° 30 E). In this experiment, four planting dates (10 January, 25 January, 10 February, and 25 February) with three cultivars (Ganga, Winner, and Savio) were grown with three replications (each year) during 2020 and 2021. Our findings showed that plant growth, fresh and dry biomass, yield, and yield attributes were statistically higher in earlier plantings, and lower in delayed planting. Regarding the cultivars in both years, ‘Winner’ performed better in all growth and yield parameters as compared to other growing cultivars. Lower weight loss and decay incidence were reported in ‘Winner’ cultivar. Additionally, ‘Winner’ cultivar showed a higher sensory score as compared to the other cultivar in both years. From the results, we conclude that the nursery of sweet pepper plants should be planted on the 25 January to attain higher crop productivity, and the cultivar ‘Winner’ showed promising effects so it should be planted for better yield.

NPK fertilization for the first years of physic nut cultivated in two regions

The physic nut is an oilseed plant that has stood out as an alternative source for biodiesel. However, using it in this way requires determining the edaphoclimatic conditions and adequate fertilization to guarantee maximum productivity. This study objectived to evaluate and establish adequate rates of Nitrogen (N), Phosphorus (P), Potassium (K) to maximize the growth and productivity of physic nut in two edaphoclimatic conditions. Two experiments were performed over two consecutive years, one under Quartzarenic Neosol in the municipality of Diamantina and the other under Red-Yellow Latosol in the municipality of Governador Valadares. The treatments comprised rates of N: 0, 25, 50, 100 kg ha−1; P: 0, 35, 70, 140 kg ha−1; and K: 0, 40, 80, 160 kg ha−1. Plant growth was evaluated every 30 days until flowering. Seed and oil productivity was also assessed, with one harvest in Diamantina and two harvests in Governador Valadares. The edaphoclimatic condition of Governador Valadares was a determining factor in providing greater growth and productivity of the physic nut when compared to that of Diamantina. The application of N rates was unnecessary for the growth and productivity of the physic nut in Diamantina. Thus, phosphate fertilization was unnecessary in Governador Valadares and potassium fertilization was unnecessary for both regions. In Diamantina, 84 kg ha−1 and 87 kg ha−1 of P is recommended for growth and maximum productivity, respectively. Whereas, in Governador Valadares, 49 kg ha−1 and 55 kg ha−1 of N is recommended for growth and maximum crop productivity, respectively.

AI ENABLED WATER CONSERVATION FOR IRRIGATION USING IOT

This project introduces a smart irrigation system that utilizes Internet of Things (IoT) technology and machine learning algorithms to enhance water management in agriculture. The system employs a series of IoT sensors distributed across the field to consistently monitor key environmental factors such as soil moisture levels, temperature, humidity, and others. The collected data is analysed using machine learning Cat Boosting algorithm. This algorithm analyze the data to determine the optimal irrigation schedule based on crop water requirements, soil conditions, weather forecasts, and historical data. The system controls irrigation equipment such as pumps, valves, and sprinklers to deliver precise amounts of water to crops at the right time. Continuous feedback from the system allows for refinement of irrigation schedules, leading to improved water conservation, increased crop yield, and cost savings for farmers. This project discusses the benefits of such a system, including water conservation, increased crop yield, cost savings, environmental sustainability, and remote monitoring and control capabilities. Overall, the integration of IoT and machine learning technologies offers a powerful solution for sustainable agriculture, enabling data-driven decision-making processes to optimize water usage and maximize crop productivity. Keywords— Smart irrigation system, Internet of Things(IOT)technology, Water conservation, Environmental parameters, Soil moisture monitoring, Increased crop yield, Machine learning algorithms

GIS-based Land Suitability Analysis for Mung Bean Production in Arba Minch Zuria District, Gamo Zone, Southern Ethiopia

Land suitability analysis is of vital importance to maximize crop productivity of farming households. This study has aimed to investigate land suitability for mung bean production using the Analytical Hierarchic Process model. Four main criteria (soil, climate, land use land cover and topography), and 8 sub-criteria (slope, soil texture, soil depth, soil pH, soil cation exchange capacity, temperature, rainfall, and soil drainage) were selected to analyze land suitability. The sub-criteria were reclassified into 5 suitability levels: high, moderate, marginal, not suitable, and permanently not suitable. The Analytic Hierarchy Process (AHP), was used to determine the perceived weights that each criterion and sub-criteria carry. The land suitability for mung bean production during the October-November-December (OND) season showed by the final map was 32% (high), 26% (moderate), and 42% (marginal). 14%, 62%, and 24%, respectively were the suitability levels designated as high, moderate, and marginal during the January-February-March (JFM) season. Due to the prevalence of the climatic conditions, topography, spatially low lands, and fertile soil, the production of mung bean during the OND season was high. The major factors that impede higher land suitability during the JFM season include rainfall, soil, poor drainage, and slope. The production of mung bean is important in the agricultural sector for many benefits like human nutrition, local market, animal feed, export commodities, and soil fertility maintenance. So, stakeholders such as the district agricultural office, and governmental, and non-governmental actors should encourage farmers to produce more in conducive seasons and relieve food security shortfalls and livelihood problems in the entirety. Keywords: Land suitability; Geographic Information System; Analytical Hierarchic Process; Mung bean

Efficient Smart Water Management Techniques to Enhance Crop Productivity for Maize

Maize is identified as the “Queen of Cereals” owing to its high yield. Water scarcity is a major problem, where agriculture consumes significant freshwater. Smart Irrigation with Internet of Things technology can help farmers to maximize crop production with less water. The field experiment on sensor-based drip Irrigation and farmer practice was conducted in the research field to enhance growth, yield, and water efficiency. The experiment comprised eight treatments such as T1: IoT based Drip Irrigation at 60% depletion level, T2: IoT based Drip Irrigation at 80% depletion level, T3: Drip Irrigation at 60% PE, T4: Drip Irrigation at 80% PE, T5: Drip irrigation as Normal Practice, T6: Surface Irrigation at 60% IW/CPE ratio, T7: Surface Irrigation at 80% IW/ CPE ratio and T8: Flood irrigation as Farmer’s Practice. The proposed systems-based Drip irrigation at a 60% depletion level can be recommended for hybrid maize to augment higher grain and straw yield. The highest water saving was recorded in IoT-based Drip Irrigation at a 60% depletion level(46.88%) followed by IoT-based Drip Irrigation at an 80% depletion level(40.63%).

Linear modeling of zonal level crop production in Ethiopia

Accounting for zonal-level variations and identifying factors that have linear effects on crop production help to make better decisions and plan new policies for effective crop production and food security. The main objective of this study is to identify potential subsets of covariates and estimate their linear effects on crop production. A linear mixed effects model (random--intercept) is used on agricultural sample survey data for Meher seasons from 2012/13 to 2019/20 to explore and identify the best variance-covariance structure for the longitudinal data on 90 zones with eight repeated observations and different sampling weights. The minimum, mean, and maximum crop production by farmers across the country are 1.616, 8.693, and 147.843 quintals, respectively, and about 98 % of farmers produced less than 25 quintals. There is a small rate of increase in mean and median crop production by farmers across the years, and the variability between zones is highest in the year 2019/20 and in the Somali region. The histogram, kernel density, and P–P plots suggested a common logarithm transformation on the crop production variable. Results from the data exploration and variance-covariance structure selection methods suggested a heterogeneous compound symmetry (CSH) structure. Covariates region, year, proportion of farmers who practice pure-agriculture and other-agriculture types, proportion of farmers who use any type of fertilizer, farmer's age, area used, farmer association crop production, indigenous seed used, improved seed used, UREA fertilizer used, other fertilizers used, and percentage of crop damaged are significant in linearly explaining/affecting log crop production, and among these area used, farmers association crop production, UREA fertilizer used, and indigenous seed used have relatively highest effect on log crop production. Zones Wolayita, North-Shewa (Am), West-Arsi, West-Welega, Dawro, and Guji are top/good performers while zones Southwest-Shewa, Waghimra, Guraghe, South-Omo, Keffa, North-Wello, South-Wello, and Eastern Tigray are bottom/poor performers in crop production. Model assumptions and influence diagnostics results suggested the linearity of the model and normality of random effects and residuals are not violated, even though some zones have influences on either model parameters, precisions of estimates of these parameters, and predicted values.

CRISHI :A Crop Recommendation System

Abstract: The Crop Recommendation System proposes a mixed model to suggest crops for Indian states, considering various factors such as soil pH, moisture, NPK levels, crop data, and temperature. This suggestion model is constructed as a sample model incorporating supervised machine learning algorithms. The technology- based agricultural crop identifier model aims to enhance farmers crop yield by identifying appropriate crops based on their parameters. The efficacy of the recommendation model lies in its ability to recommend the right crop for specific conditions, which in turn contributes to addressing agricultural and farming challenges and boosting the Indian economy by maximizing crop production. The system also includes a grading process to identify crop quality and signalling the presence of low and high-quality crops. The utilization of a set of classifiers facilitates improved decision-making through multiple classifiers. The assessment process includes a request for decisionmaking to determine the outcomes of the classifiers