Farm Crops Research Articles

Extreme rainfall indices and their consequences on the local farming crop calendar: An agro-climatic zone based study

<p>The goal of this study is to investigate the extreme rainfall indices and their consequences on the local farming crop calendar among the agro-climatic zones (ACZs) of the Abiya watershed. Climate Hazards Group Infrared Precipitation (CHIRPS) provided long-term (1981–2019) rainfall data for 50 sample grid points with a spatial resolution of 5 × 5 km. Different crops are affected differently by the same extreme rainfall event depending on when it occurred and how extreme it was; this means crop calendars for a specific time may be properly governed by extreme climatic conditions. There hasn’t been a sufficient published study on extreme rainfall indices and their consequences on crop calendars by considering various ACZ in Ethiopia. The study defined local crop calendar timing and the consequences of the extreme rainfall indices through focus group discussions. INSTAT+ software was applied to calculate eight extreme rainfall indices. The indices were evaluated using Mann-Kendall’s (MK) and Sen’s slope techniques to identify trends and determine variations in the magnitude, respectively. Increase and decrease indications for various crop calendars were found in each ACZ. Further, going-up signals were seen in the highlands, midlands, and all ACZs for land preparation time (LPT), sowing and management time (SaMT), and harvesting and threshing time (HaTP), respectively. While HaTT was found to be uniform in all ACZs, some of the declining trends in the indices were detected for LPT and SaMT in the cold-highland and highland zones. The perceived trend in indices across the whole ACZ will have direct and unintended consequences for watershed crop production. The findings imply that to reduce the unfavourable consequences of these extreme rainfall occurrences in the agricultural sector, it is necessary to develop suitable crop varieties and drought-tolerant crops, as well as an effective early warning system.</p>

THE RELATIONSHIPS OF RISKS AND INCOMES IN FARMING IN WETLAND AND DRYLAND OF NORTH LOMBOK, INDONESIA

Risk is a constant threat to businesses, including to farms in North Lombok, Indonesia. This paper examines the relationships between risks of farming and incomes generated by the selected farms. Data related to farm risks and incomes were collected from samples of farmers in wetland and dryland in North Lombok Indonesia, through series of face-to-face interviews on the farms or farmers houses, guided by a semi-structured questionnaire. Quantitative analyses were carried out on farm risk levels and income, followed by descriptive comparative on examination of the relationships between the two main variables of this paper. Results of analyses revealed that incomes from farming crops in North Lombok were not obviously related to the risks associated with the crop production and market. However, agricultural producers have selected crops for their suitability to the land (soil) and local climate, by which farming risks were reduced or eliminated, for sustaining farming activities, and expecting high farm income. Since crops have been suitable to the land climate, as well as to the acceptable risk, then agricultural production is continued and income is generated for the farmers.

PROGRAM KEMITRAAN MASYARAKAT PADA PENGELOLAAN GREEN HOUSE SMART FARMING DI DUKUH TAMBAK, NGESTIHARJO, BANTUL

This community service was carried out in the Tambak Hamlet, Ngestiharjo Village, Kasihan, Bantul, Yogyakarta. Tambak Hamlet is a hamlet located in the Ngestiharjo Village, Bantul, which is on the outskirts of Yogyakarta. Therefore, the population in this area is quite dense, so that agricultural land becomes increasingly limited. Narrow land still exists in this area, which can be used for farming or planting horticultural crops. However, nowadays the weather is getting more and more erratic, so the time for planting is becoming more difficult. In Tambak Hamlet there is already an IoT-based green house for smart farming for horticultural crops, but it has not been managed, because it is still in the development stage. Based on this, the service team is trying to make a suggestion regarding the management of the green house, so that the results can be maximized and able to increase the economic value for the community.
 This service aims to develop agricultural digitalization through the Smart Farming Model, using Internet of Things (IoT) technology, to assist in crop monitoring, so that the growth and yield of horticultural crops is optimal. The purpose of this service is in line with the 3rd and 5th Main Performance Indicators (IKU) of Higher Education. The 3rd IKU, namely lecturers who carry out activities outside the campus, in this case the lecturer performs community service which is carried out in the Tambak Hamlet. The 5th IKU is the work of lecturers used by the community, namely in the form of ideas and thoughts in the development of smart farming. The expected outputs from this community service activity are services in the form of training and assistance for the management of green house smart farming, and publication in the form of scientific articles that will be published in national journals.
 Keywords: smart farming, green house, community service, digitalization, agriculture

The COLOSS BEEBOOK – an example of standard methods in insect research

Abstract Honey bees play an essential role in modern agriculture as farm animals and crop pollinators, and they contribute to one third of our diet. Over the last few decades, managed honey bees have faced large-scale losses worldwide. Various causes include the spread of pathogens and parasites, habitat loss and loss of forage, pesticide use, and climate change. Many scientists investigated these issues worldwide separately and independently, often using different methodologies, and this approach might lead to conflicting and sometimes erroneous findings. To provide global and sustainable solutions, a group of bee scientists established COLOSS, a non-profit association for the prevention of honey bee COlony LOSSes. Its mission is to investigate the causes of declining bee health and find effective means to improve the well-being of bees. COLOSS comprises various Core Projects and Task Forces focusing on specific topics identified by the association to receive priority attention. Among the core projects, the COLOSS BEEBOOK is a unique venture aiming to provide a standardised methods manual for studying the honey bee. The project’s goal has been to create a comprehensive collection of established methods and techniques for honey bee research, with the aim of making studies conducted by different groups across the world more comparable. The resulting practical manual contains over 2,000 standardized methods across all fields of honey bee research. It is definitive, but evolving, research manual. There is a strong need for standardisation in the fast-growing field of edible insect farming and research. The COLOSS BEEBOOK stands as a testament to the achievements that can be made when researchers collaborate towards a common goal, and it can serve as an inspiration for the establishment of standardized methods for the mass rearing of edible insects.

Feature Importance Ranking of Random Forest-Based End-to-End Learning Algorithm

Efficient land management and farming practices are critical to maintaining agricultural production, especially in Europe with limited arable land. It is very time consuming to rely on a manual field inspection of cultivated land to archive farm crops. But with the help of satellite monitoring data on the earth’s surface, it is a new vision to classify farmland based on deep learning. This article has studied the Sentinel 2 (S2) data, which are top-of-atmosphere (TOA) reflectance values at the processing level-1C (L1C) observed from some areas of Germany and France. Aiming at the problem that the interference of atmosphere and cloud coverage weakens the recognition accuracy of subsequent algorithms, a method of combining feature expansion and feature importance analysis is proposed to optimize the raw S2 data. Specifically, the new 13 spectral features are expanded based on the linear and nonlinear combination of the raw 13 spectral bands of S2. The random forest (RF) algorithm is used to score the importance of features, and the important features of each time series are selected to form a new dataset. Then, an end-to-end deep learning model has been used for training. The structure of the model is a two-layer unidirectional recurrent neural network with long short-term memory (LSTM) as the backbone. And two linear layers as the output, which form two decision-making heads, respectively, representing output classification probability and the stop decision. The results show that adding features and selecting features is beneficial for the model to improve classification accuracy and predict the classification without all of the input data. This end-to-end classification pattern with early prediction would support intelligent monitoring of farm crops with a great advantage to the implementation of various agricultural policies.

Between eustress and distress: UVB induced changes in carotenoid accumulation in halophytic Salicornia europaea

Halophytes are potential future crops with a valuable nutritional profile. Produced in indoor farming, they are considered to contribute to sustainable and resilient food systems. Indoor farms operate using artificial light. In this context narrowband and low dose UVB radiation can be used to increase plant secondary metabolites, such as carotenoids, and provide an improved nutritional profile for a human diet. UVB radiation can cause eustress or distress in the plant depending on the lighting situation. The aim of this study was to identify the doses of UVB that lead to either eustress or distress and to analyze these responses in Salicornia europaea. Therefore, S. europaea plants were exposed to different UVB radiation levels, low, medium and high, and analyzed for reactive oxygen species (ROS), plant hormones, amino acids, and photosynthetic pigments. High UVB treatment was found to affect phenotype and growth, and the metabolite profile was affected in a UVB dose-dependent manner. Specifically, medium UVB radiation resulted in an increase in carotenoids, whereas high UVB resulted in a decrease. We also observed an altered oxidative stress status and increased SA and decreased ABA contents in response to UVB treatment. This was supported by the results of menadione treatment that induces oxidative stress in plants, which also indicated an altered oxidative stress status in combination with altered carotenoid content. Thus, we show that a moderate dose of UVB can increase the carotenoid content of S. europaea. Furthermore, the UVB stress-dependent response led to a better understanding of carotenoid accumulation upon UVB exposure, which can be used to improve lighting systems and in turn the nutritional profile of future crops in indoor farming.

Cash crop or food crop? socioeconomic and geopolitical factors affecting smallholder farmer crop selection in times of crisis in southwestern Tajikistan

Tajikistan's agricultural sector, primarily dominated by cotton cultivation, has experienced significant changes since the Soviet era. Although farmers introduced food crops into agricultural production to ensure food security after the collapse of the Soviet Union in 1991, the government still mandates that cotton cultivation occupy a considerable portion of Tajikistan's limited irrigated land. However, following the recent Covid-19 pandemic crisis, farmers have encountered the need to reassess their agricultural practices, given constrained governmental assistance and a range of socioeconomic considerations. This research adopts resilience as a conceptual framework to examine the coping strategies of smallholder farmers in the Khatlon region of Tajikistan, with a specific emphasis on the period spanning from 2019 to 2022. At the outset, our research employed the snowballing technique to increase respondent participation, later transitioning to a representative sample size that facilitated the collection of qualitative data from around 100 semi-structured interviews, 10 focus groups, and personal visits to agricultural fields over the course of four years. The paper not only demonstrates the overall creativity of Tajikistani farmers in growing and selecting non-cotton crops in this most recent period of crisis, but also points to wealthier farmers’ generally greater willingness to adopt technological innovations and gain new knowledge to apply to them. Although these strategies have been taken in order to address farmers’ immediate needs in troubled times against the backdrop of the existing state cotton policy, their sustainability remains uncertain. While the qualitative focus of this study bears certain inherent limitations, the data collected nevertheless show that this period of crisis has been generative for many farmers as they seek out new methods of subsistence.

Decision support algorithm to revamp homestead vegetable production for climate resilience and nutrition security

Inadequate micronutrient diversity in diets remains a serious cause of malnutrition which is prevalent in rural areas especially in developing countries. The current study proposed a decision support algorithm for nutrition-sensitive vegetable production in semi-subsistence farming systems to improve their access to micronutrient-diverse and dense foods without compromising on economics of cultivation. Multi-dimensional nutri-economic data was modelled to compute micronutrient profiles along with the relative abundance of vegetables in diets, giving an advantage over other nutrition indices that measured diversity. Besides, a novel micro-planning algorithm was developed for climate-resilient and micronutrient-sensitive vegetable production that generated multiple production plans as per farm properties, crop preferences and consumption patterns of farm households. The algorithm consisted of a cropcombination generator, uncertainty analysis and optimization procedure. The uncertainty analysis incorporated variations in climate, market rates, yields and cost of cultivation. The algorithm was demonstrated using 8 case studies based in India and investigated whether micronutrient diversity, micronutrient density, production diversity and net returns could be improved at the production stage. The results suggested that the generated plans performed better than existing plans for all four parameters in every case study. The improvement in micronutrient diversity, micronutrient density, production diversity and net returns ranged from 5.8 to 60 %, 7.2 to 79 %, 7.8 to 31 % and 5.8 to 60 % respectively. This study provides a simple framework to process large multi-dimensional nutri-economic data to measure essential well-being indices and produces sound food policies at field and regional levels.

Socioeconomic Determinants of Modern Agricultural Technology Adoption among Farmers in Rivers State: A Case Study of Etche Local Government Area.

This research examined the socio-economic determinants of modern agricultural technology adoption among farmers in Rivers State. The specific objectives were to examine how education; gender; farm size; marital status and annual farm income influences the adoption of modern agricultural technology. Primary data was collected and inferential statistics (linear regression, Probit regression and Logistic regression) were used in the analysis of collected data. The results showed that 72% of the respondents were female, 30% being majority of the respondents fell between the age category of 36-43 years, 64% were married, 34% being majority have primary education as their highest educational qualification, 46% of the farmers earn ?120,000 and above annually. The results also revealed that only 14% of the farmers adopted high yielding varieties as their modern agricultural technology while 86% reported not adopting any agricultural technology. 30 % of the farmers grow cassava as their main farm crop. None of the farmers reported using reported using inorganic fertilizers; only 5% reported using poultry droppings. 76% reported not using high yielding varieties, while only 24% reported using high yielding varieties. 86% reported getting their high yielding varieties from other sources other than ADP and government sources. 100% of the respondents reported the absence of extension training to be a limiting factor to adoption of modern agricultural technology. 38% reported lack of awareness. The logistic regression results had an R 2 of 74% indicating that a 74% variation in modern agricultural technology adoption is determined by variations in the independent variables. The independent variables had significance values of 0.04 for level of education, 0.00 for f.,[arm size, annual farm income has a p-value of 0.03, 0.714 for gender and 0.701 for age. Only annual farm income, farm size and educational level had significant influence on farmers’ adoption of

Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system

Smallholder farmers reside in marginal environments typified by dryland maize-based farming systems. Despite the significant contribution of smallholder farmers to food production, they are vulnerable to extreme weather events such as hailstorms, floods and drought. Extreme weather events are expected to increase in frequency and intensity under climate change, threatening the sustainability of smallholder farming systems. Access to climate services and information, as well as digital advisories such as Robust spatially explicit monitoring techniques from remotely piloted aircraft systems (RPAS), could be instrumental in understanding the impact and extent of crop damage. It could assist in providing adequate response mechanisms suitable for bolstering crop productivity in a spatially explicit manner. This study, therefore, sought to evaluate the utility of drone-derived multispectral data in estimating crop productivity elements (Equivalent water thickness (EWT), Chlorophyll content, and leaf area index (LAI)) in maize smallholder croplands based on the random forest regression algorithm. A hailstorm occurred in the study area during the reproductive stages 2 to 3 and 3 to 4. EWT, Chlorophyll content, and LAI were measured before and after the storm. Results of this study showed that EWT, Chlorophyll content, and LAI could be optimally estimated based on the red edge and its spectral derivatives. Specifically, EWT was estimated to a rRMEs 2.7% and 59%, RMSEs of 5.31 gm−2 and 27.35 gm-2, R2 of 0.88 and 0.77, while chlorophyll exhibited rRMSE of 28% and 25%, RMSEs of 87.4 µmol m−2 and 76.2 µmol m−2 and R2 of 0.89 and 0.80 and LAI yielded a rRMSE of 10.9% and 15.2%, RMSEs of 0.6 m2/m2 and 0.19 m2/m2 before and after the hail damage, respectively. Overall, the study underscores the potential of RPAS-based remote sensing as a valuable resource for assessing crop damage and responding to the impact of hailstorms on crop productivity in smallholder croplands. This offers a means to enhance agricultural resilience and adaptability under climate change.

Linking indigenous with scientific knowledge about enset (Ensete ventricosum) disease management in Gamo highlands of Ethiopia: Evidence from local people response, soil physicochemical and microbial dynamics

CONTEXTEnset (Ensete ventricosum) is a staple food for millions in Ethiopia and characterized by climate resilient crop, but enset bacterial wilt (EBW) disease caused by Xanthomonas is the major threat to enset growing regions in the country that puts the food security of smallholder farmers at risk. Enset is a drought tolerant plant used for nutritional, medicinal and cultural activities for over 20 million people in Ethiopia. However, enset bacterial wilt (EBW) disease is a serious challenge. Although there is a good EBW disease management system from local people, the link between indigenous and scientific knowledge is not explored. OBJECTIVEThis study aimed to assess indigenous-scientific knowledge nexus about enset (Ensete ventricosum) disease management in Gamo highlands of Ethiopia using data generated from local people response, soil physicochemical and microbial dynamics. METHODSSamples (soil and plant) were collected from different farm sites (with no enset, with enset and with enset-medicinal) at three districts for physicochemical, antibacterial and microbial analysis. We considered various physicochemical parameters (moisture, temperature, pH, macro- and micro-nutrients); extracted phytochemical compounds from medicinal plant leaves using different solvents (water, ethanol, methanol and n-hexane); and 16S rRNA gene sequence using Illumina sequencing technique. RESULTS And CONCLUSIONSWe found that farmers have no knowledge gap in classifying healthy enset from sick/infected one. However, farmers were unable to classify the different stages of infected enset. Farmers were able to recognize that farm tool > animals > plant debris > air as a means of disease transmission. Nearly all respondents (97.3%) did not use chemicals to control EBW, rather they remove infected enset by uprooting. We found that farm tool cleaning, crop rotation, wrapping, bone smoking, and inter-planting medicinal plants were the main local EBW disease management practices employed by farmers. Medicinal plant extracts (E. abyssinica > D. afromontana > P. fruticosus) showed promising antibacterial activity (1.33 ± 0.28–8.81 ± 0.32 mm) against Xanthomonas. Planting medicinal plants with enset also significantly enhanced some soil physicochemical parameters and shifted the soil bacterial abundance from Proteobacteria to Firmicutes. Although indigenous practice enhanced soil physicochemical parameters and non-pathogenic microbial community in the soil, integrating it with modern knowledge (applying chemicals) were recommended as an effective EBW management strategy. SIGNIFICANTSoil physicochemical and microbial shift due to inter-planting medicinal plants with enset indicates that medicinal plants may have means of protection against EBW disease through optimizing soil physicochemical and microbial dynamics in the Gamo highlands. Additionally, the inhibition of medicinal plants against Xanthomonas suggests the inclusive influence of medicinal plants on the EBW causing bacteria.

Soil properties affect crop yield changes under conservation agriculture: A systematic analysis

AbstractConservation agriculture (CA) has the potential to sustain soil productivity and benefit agroecosystems, yet it is not fully understood how yield responses of different cropping systems are affected by inherent soil characteristics, for example, texture and dynamic soil properties, such as aggregation, nutrients and erosion. In this study, we conducted a systematic review to compare crop yield from cropland with conventional management versus different CA practices, specifically reduced‐ or no‐tillage, agroforestry, organic farming and cover crops. The data were first analysed for different climatic regions, soil textures and cash crop types. We then quantified how yield responses correlated with soil properties change under different CA practices. The results showed that CA practices were associated with an overall mean crop yield increase of 12%. This response was primarily driven by corn, which had a mean yield increase of almost 41% after CA implementation, whereas other cash crops did not have significant yield responses or showed slight decreases, as rotation with mixtures of multiple cash crops had a mean decrease of 6% when using CA. The increase in corn yield after CA may be related to the enhanced ability of that crop to absorb nutrient elements (e.g. nitrogen) and reduce nutrient leaching. Agroforestry increased crop yield by 66% and cover cropping increased yield by 11%, likely due to increases in soil water content and nutrient availability and decreases in erosion and surface runoff. However, other agricultural systems showed no significant increase after CA compared with conventional row cropping practices. Using CA practices had the greatest yield benefit in tropical climates and when farming in coarse‐textured soils. In addition, legumes and grass‐legume mixtures resulted in significant cash crop yield increases, possibly because legumes promoted the increase of soil nitrogen and depleted soil moisture less compared with other cover crops. The results provide new insight into how interactions between soil properties and CA practices affect crop yield and at the same time can help guide the development of practical, evidence‐based guidelines for using conservation practices to improve yield in corn and other cash crops.

Preface

It is with great honor; we present the proceeding of the 4th International Conference of Food Security and Sustainable Agriculture in The Tropics (IC-FSSAT) 2023, which was organized by the Faculty of Agriculture, Universitas Hasanuddin, and held online in the 15 – 16 February 2023. The proceeding of the previous conferences in this series has been published in the IOP conference series: Earth and Environmental Sciences (EES).The proceeding of the 4th IC-FSSAT consists of 225 reviewed papers across 10 main scopes; (i) Industrial-scale Agriculture, (ii) Food Agribusiness and Rural Development, (iii) Food Agribusiness and Rural Development, (iv) Biodiversity, Soil Health and Agricultural Practices (v) Integrated Pest and Disease Management, (vi) Plant Breeding and Biotechnology (vii) Agricultural Engineering, (viii) Digital Farming, (ix) Food Processing, Safety and Diversification and (x) Crop Production System. In addition to the excellent paper presentations at the conference, the conference also featured nine keynote and invited speakers with expertise in the field of the conference’s subjects. Please allow us to take this opportunity to thank the keynote speaker and the invited speakers: Dr. Tony Liwang (Oil Palm Plantation Fund Management Agency, Indonesia; Prof. Siti Kusujiarti (Warren Wilson College, USA); Prof. Bunyamin Tar’an (University of Saskatchewan, Canada); Prof. Sikstus Gusli (Hasanuddin University, Makassar- Indonesia); Dr. Thong Nguyen-Huy (The University of Southern Queensland, Australia); Dr. Didier Lesueur (Centre de cooperation Internationale en Recherche Agronomique pour le Developpement (CIRAD), France; Dr. Anwar Syed H. Rizvi Animal and Plant Health Inspection Service, USA; Prof. Hasil Sembiring (IRRI Indonesia); and Dr. Inamullah Khan (The University of Agriculture, Pakistan).I would like to express our deepest gratitude to all committees for this conference’s success and smooth running. Last but not least, a warm thank you to all academics, researchers, and observers of Faculties of Agriculture, Science, Animal Husbandry, and Forestry for their participation in submitting their article in the IOP proceedings series EES 2023. I sincerely look forward to all the contributions that will benefit and promote the development of a sustainable agricultural innovation sector toward achieving the SDGs and dealing with the triple planetary issues.Warm Regards,Muhammad JUNAID, SP., MP., PhDConference Chairman Faculty of Agriculture, Hasanuddin UniversityList of Conference Committees, Conference Photograph are available in this pdf.

The new normal? Cluster farming and smallholder commercialization in Ethiopia

AbstractCluster farming is increasingly recognized as a viable means of improving smallholder economic integration and commercialization in many developing countries. However, little is known about its impact on smallholder welfare and livelihoods. We examine the relationship between cluster farming and smallholder commercialization using a large‐scale survey of 3969 farm households in Ethiopia cultivating high‐acreage crops such as teff, wheat, maize, barley, and sesame. Using switching regressions and instrumental variable estimators, we show that cluster farming is associated with commercialization measured as commercialization index, market surplus value, and market price. To further deal with endogeneity concerns, we also employ some pseudo‐panel models where we observe similar insights. Beyond this, we account for heterogeneities by disaggregating households based on farm scales and crops cultivated. Our findings show that cluster farming is positively associated with commercialization for all farms and crop types despite this disaggregation. However, the related gains are higher among medium and large farms and vary per crop type. These findings imply that cluster farming is crucial in improving smallholder commercialization and may be a critical entry and leveraging point for policy. We thus lend support to initiatives and plans that seek to upscale cluster farming as they can potentially improve smallholder commercialization with ensuing impacts on rural livelihoods and welfare.

Assessment of the Impact of Forest Reclamation Measures for the Adaptation of Agriculture to Climate Change in the South of the Russian Plain

The aim of this study was to study the effectiveness and economic efficiency of the impact of agroforestry complexes on the adaptation of agriculture to climate change in the south of the Russian Plain. It has been established that this manifests quantitatively in a significant decrease (by almost a third) in the area of wind-destroyed lands and drought-dead crops in farms protected by forest strips compared with open agricultural territories. The calculation of direct damage prevented by protective forest plantations from degradation and loss of soil fertility as a result of dust storms and indirect damage prevented by protective forest plantations from crop loss as a result of extreme droughts shows that the total amount of remuneration received by farmers from agroforestry in connection with the placement of a forest-forming element in their fields is EUR 317–1239 ha−1 year−1. This value is the contribution of agroforestry to adaptation to climate change and is subject to zonal dynamics—it depends on natural and climatic conditions. The application developed as part of this research has value for decision makers, since it allows for preliminary assessment of the effectiveness and efficiency of agroforestry for various areas of farms and various natural zones.

Combination of observational and functional trait-based approaches in developing a polyculture design tool

ABSTRACT Models of species composition in diversified cropping systems utilize mostly functional trait-based or observation-based approaches. We argue that a combination of these two approaches makes polyculture design tools more robust. We assessed quantity, quality, and complementarity of information from multiple sources for designing diversified cropping systems with vegetables and spice crops for cold temperate climate. Trait and observational data were integrated from: (i) two grower-oriented and one academic crop database, (ii) a survey of farmers practicing community-supported agriculture, and (iii) a systematic literature review on the use of spice crops in vegetable farming. Survey results reveal that the farmers were capable of achieving medium to good levels of their main goals, but failed to reach desired multifunctionality with their polycultures, which can be potentially improved with computational tools. None of the analyzed data sources provided a comprehensive dataset for all target crops and functional traits. However, source combination allowed for design from known crop companions (farmers survey and grower-oriented databases), to addressing specific pest problems (literature review), and increasing functional complementarity and facilitation by trait matching (academic and grower-oriented trait databases). Integrating information from different sources increased the number of crop combination options but also planning and management complexity.

Determination of bio-accumulated Cd, Cr, Cu, Ni and Pb in a wild plant Lantana camara L. Verbenaceae, grown on waste dump site and assessment of its phyto-extraction potential for studied metals

The inputs of chemical fertilizers, street runoffs and regular use of heavy metal contaminated water to irrigate farm crops may result in elevated levels of heavy metals in agri-soil. The food crops growing on such soils may selectively bio-accumulate these metals, which usually depend on the concerned plant’s bio- accumulation potential for concerned metal and characteristics of the soil. This study was aimed to evaluate the bio-accumulation of Cd, Cr, Cu, Ni and Pb in the root, stem and leaves of the wild plant Lantana camara L., Verbenaceae, grown on waste dump site, by analyzing levels of studied metals in soil and plant parts. The bio-concentration factors (BCFs) and translocation factors (TFs) for metals under study were also evaluated, to find out the suitability of Lantana plant for phytoextration of the tested metals. The observed order of metal concentrations in soil samples was Pb (8.91± 0.54) > Cr (6.42 ±0.43) >Cu (1.91± 0.17) > Ni (1.42± 0.13) > Cd (0.86± 0.09, mg/Kg). The observed levels of studied metals in roots, stem and leaves of Lantana were, Pb(8.73±0.32)> Cr (2.96± 0.18)> Cu (2.16±0.15)> Ni (1.38±0.12)>Cd (0.54±0.03mg/kg); Pb(5.18±0.24)>Cr (3.18±0.18)>Cu (2.42±0.17) >Ni (1.49±0.12) >Cd (0.28±0.02, mg/Kg) and Pb (6.07±0.27)> Cr (1.30±0.13) >Cu (0.88±0.07) Ni (0.16±0.02) >Cd (0.10 ±0.01, mg/Kg), respectively. The bio-concentration factors for Lantana from soil to roots (BCFRoot) and from roots to shoots (BCFShoot) for Cd, Cr, Cu, Ni and Pb were 0.63, 0.46, 1.13, 0.97 (~ 1.00) and 0.98 (~ 1.00) and 0.70, 1.51, 1.49, 1.20 and 1.29, respectively. The translocation factors from root to stem (TFStem) and from stem to leaves (TFLeaf) for tested metals in Lantana camara were, 0.52, 1.07, 1.12, 1.08, 0.59 and 0.36, 0.41, 0.36, 0.11, 1.17, respectively. Thus, the recorded BCF values, mostly being >1, makes Lantana suitable for phyto-extraction of tested metals from agri-soil and medium to good translocation factors (TF) suggested the potential of the plant to translocate the tested metals into the stem and leaves.

Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)

In the present study, we used device visualization in tandem with deep learning to detect weeds in the wheat crop system in actual time. We selected the PMAS Arid Agriculture University research farm and wheat crop fields in diverse weather environments to collect the weed images. Some 6000 images were collected for the study. Throughout the season, tfhe databank was assembled to detect the weeds. For this study, we used two different frameworks, TensorFlow and PyTorch, to apply deep learning algorithms. PyTorch’s implementation of deep learning algorithms performed comparatively better than that of TensorFlow. We concluded that the neural network implemented through the PyTorch framework achieves a superior outcome in speed and accuracy compared to other networks, such as YOLO variants. This work implemented deep learning models for weed detection using different frameworks. While working on real-time detection models, it is very important to consider the inference time and detection accuracy. Therefore, we have compared the results in terms of execution time and prediction accuracy. In particular, the accuracy of weed removal from wheat crops was judged to be 0.89 and 0.91, respectively, with inference times of 9.43 ms and 12.38 ms on the NVIDIA RTX2070 GPU for each picture (640 × 640).

Efficacy of organic inputs on nutrient management and cost-benefit ratio on pulse crops in organic farming

The wide range of external solid organic sources of nutrients viz., FYM, vermicompost, goat manure, etc., are available, out of them, FYM & vermicompost are found to good in terms of yield and quality of the different pulse crops. Several options are available for liquid manures, among them higher yield, quality and cost-benefit ratio of the produce reported with the application of Panchagavya and jeevamrut. Crop residue mulching with organic manures was also found effective. Integrated application of organic and natural inputs as solid and liquid manures gave better results than sole application.

Factors Influencing Biofuel Production in Western Kenya

Biofuel production is at the forefront of the agenda of many countries across the globe in the quest to lower greenhouse gas emissions and improve energy access. The increase in oil prices and climate change have been the drivers to ensure the adoption of biofuels. With the global consumption of oil projected to increase, biofuel provides a viable option for a clean, affordable, and climate-friendly source of fuel. However, biofuel production is in Kenya faced with various challenges. Therefore, the study sought to assess the factors that influence biofuel production in Western Kenya. The qualitative research design was adopted with both primary and secondary data collected. Data collection was through interviews, focus group discussions, case studies, and a review of secondary data. The results established that there was a link between food security and biofuel production. Factors influencing the production of biofuel in Western Kenya included the lack of a specific national biofuel policy framework that promotes sustainable development and use of biofuels, limited research, insufficient feedstock to increase production, over-reliance on rain-fed agriculture to grow energy crops, inadequate technology and technical expertise and some knowledge among stakeholders regarding the need and importance of biofuel deployment across the country. There is need for alternative sustainable farming methods that can incorporate cane farming and food crop farming to ensure food security, better farming practices to increase the cane yield, and research on consumer attitudes and behaviours towards biofuels to promote the adoption of the technology.