Estimation of rice yield using multivariate analysis techniques based on meteorological parameters.

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Production and optimization of surfactin produced from locally isolated Bacillus halotolerans grown on agro-industrial wastes and its antimicrobial efficiency.

INTRODUCTION: Optimal exploitation of the huge amounts of agro-industrial residuals that are produced annually, which endangers the ecosystem and ultimately contributes to climate change, is one of the solutions available to produce value-added compounds. AIM AND OBJECTIVES: This study aimed at the economic production and optimization of surfactin. Therefore, the production was carried out by the microbial conversion of Potato Peel Waste (PPW) and Frying Oil Waste (FOW) utilizing locally isolated Bacillus halotolerans. Also, investigating its potential application as an antimicrobial agent towards some pathogenic strains. RESULTS: Screening the bacterial isolates for surfactin production revealed that the strain with the highest yield (49 g/100 g substrate) and efficient oil displacement activity was genetically identified as B. halotolerans. The production process was then optimized utilizing Central Composite Design (CCD) resulting in the amelioration of yield by 11.4% (from 49 to 55.3 g/100 g substrate) and surface tension (ST) by 8.3% (from 36 to 33 mN/m) with a constant level of the critical micelle concentration (CMC) at 125 mg/L. Moreover, the physiochemical characterization studies of the produced surfactin by FTIR, 1H NMR, and LC-MS/MS proved the existence of a cyclic lipopeptide (surfactin). The investigations further showed a strong emulsification affinity for soybean and motor oil (E24 = 50%), as well as the ability to maintain the emulsion stable over a wide pH (4-10) and temperature (10-100 °C) range. Interestingly, surfactin had a broad-spectrum range of inhibition activity against Bacillus subtilis, Staphylococcus aureus, Pseudomonas aeruginosa, klebsiella pneumonia, and Candida albicans. CONCLUSION: Subsequently, the screening of the isolates and the utilized food-processing wastes along with the extraction technique resulted in a high yield of surfactin characterized by acceptable ST and CMC levels. However, optimization of the cultural conditions to improve the activity and productivity was achieved using Factor-At-A-Time (OFAT) and Central Composite Design (CCD). In contrast, surface activity recorded a maximum level of (33 mN/n) and productivity of 55.3 g/100 g substrate. The optimized surfactin had also the ability to maintain the stability of emulsions over a wide range of pH and temperature. Otherwise, the obtained results proved the promising efficiency of the surfactin against bacterial and fungal pathogens.

Public healthcare disparities in Africa: the food production systems and its dichotomy in a South African context.

One of the major concerns of development in Africa is the issue of public health. In Africa, public healthcare has been and still is a problem most African countries are faced with. The problem of public healthcare seems to be unabated even though there are measures that are put in place for its effectiveness. There is hunger, malnutrition, high mortality rate, illnesses and deterioration of life expectancy in most developing countries of Africa. The dramatic unprecedented public health disparity has become a scourge in developing countries where it has purportedly impaired the developmental efforts, economic growth and prosperity. As a result, there is a need to scrutinize possible causes that exacerbates public health issues in developing countries. The paper argues that the current food production system (conventional) contributes to current status of public health as compared to the previous food production system (organic). The purpose of this paper is to conceptualize public healthcare disparities, juxtaposing organic and conventional food production that result as human food consumption. The paper employs literature-based analysis as a methodology to assemble data in respect of public healthcare disparities and food production systems.

Impact of biological manure substitution on grain yield, nitrogen recovery efficiency, and soil biochemical properties.

Fertilization plays a crucial role in ensuring global food security and ecological balance. This study investigated the impact of substituting innovative biological manure for chemical fertilization on rice (Oryza sativa L) productivity and soil biochemical properties based on a three-year experiment. Our results suggested rice yield and straw weight were increased under manure addition treatment. Specifically, 70% of total nitrogen (N) fertilizer substituted by biological manure derived from straw, animal waste and microbiome, led to a substantial 13.6% increase in rice yield and a remarkable 34.2% boost in straw weight. In comparison to the conventional local farmer practice of applying 165 kg N ha-1, adopting 70% of total N plus biological manure demonstrated superior outcomes, particularly in enhancing yield components and spike morphology. Fertilization treatments led to elevated levels of soil microbial biomass carbon and N. However, a nuanced comparison with local practices indicated that applying biological manure alongside urea resulted in a slight reduction in N content in vegetative and economic organs, along with decreases of 10.4%, 11.2%, and 6.1% in N recovery efficiency (NRE), respectively. Prudent N management through the judicious application of partial biological manure fertilizer in rice systems could be imperative for sustaining productivity and soil fertility in southern China.

Intercropping on Mars: A promising system to optimise fresh food production in future martian colonies.

Future colonists on Mars will need to produce fresh food locally to acquire key nutrients lost in food dehydration, the primary technique for sending food to space. In this study we aimed to test the viability and prospect of applying an intercropping system as a method for soil-based food production in Martian colonies. This novel approach to Martian agriculture adds valuable insight into how we can optimise resource use and enhance colony self-sustainability, since Martian colonies will operate under very limited space, energy, and Earth supplies. A likely early Martian agricultural setting was simulated using small pots, a controlled greenhouse environment, and species compliant with space mission requirements. Pea (Pisum sativum), carrot (Daucus carota) and tomato (Solanum lycopersicum) were grown in three soil types ("MMS-1" Mars regolith simulant, potting soil and sand), planted either mixed (intercropping) or separate (monocropping). Rhizobia bacteria (Rhizobium leguminosarum) were added as the pea symbiont for Nitrogen-fixing. Plant performance was measured as above-ground biomass (g), yield (g), harvest index (%), and Nitrogen/Phosphorus/Potassium content in yield (g/kg). The overall intercropping system performance was calculated as total relative yield (RYT). Intercropping had clear effects on plant performance in Mars regolith, being beneficial for tomato but mostly detrimental for pea and carrot, ultimately giving an overall yield disadvantage compared to monocropping (RYT = 0.93). This effect likely resulted from the observed absence of Rhizobia nodulation in Mars regolith, negating Nitrogen-fixation and preventing intercropped plants from leveraging their complementarity. Adverse regolith conditions-high pH, elevated compactness and nutrient deficiencies-presumably restricted Rhizobia survival/nodulation. In sand, where more favourable soil conditions promoted effective nodulation, intercropping significantly outperformed monocropping (RYT = 1.32). Given this, we suggest that with simple regolith improvements, enhancing conditions for nodulation, intercropping shows promise as a method for optimising food production in Martian colonies. Specific regolith ameliorations are proposed for future research.

Global synthesis on the response of soil microbial necromass carbon to climate-smart agriculture.

Climate-smart agriculture (CSA) supports the sustainability of crop production and food security, and benefiting soil carbon storage. Despite the critical importance of microorganisms in the carbon cycle, systematic investigations on the influence of CSA on soil microbial necromass carbon and its driving factors are still limited. We evaluated 472 observations from 73 peer-reviewed articles to show that, compared to conventional practice, CSA generally increased soil microbial necromass carbon concentrations by 18.24%. These benefits to soil microbial necromass carbon, as assessed by amino sugar biomarkers, are complex and influenced by a variety of soil, climatic, spatial, and biological factors. Changes in living microbial biomass are the most significant predictor of total, fungal, and bacterial necromass carbon affected by CSA; in 61.9%-67.3% of paired observations, the CSA measures simultaneously increased living microbial biomass and microbial necromass carbon. Land restoration and nutrient management therein largely promoted microbial necromass carbon storage, while cover crop has a minor effect. Additionally, the effects were directly influenced by elevation and mean annual temperature, and indirectly by soil texture and initial organic carbon content. In the optimal scenario, the potential global carbon accrual rate of CSA through microbial necromass is approximately 980 Mt C year-1, assuming organic amendment is included following conservation tillage and appropriate land restoration. In conclusion, our study suggests that increasing soil microbial necromass carbon through CSA provides a vital way of mitigating carbon loss. This emphasizes the invisible yet significant influence of soil microbial anabolic activity on global carbon dynamics.

Assessment of soil fertility and nutrient management strategies in calcareous soils of Khuzestan province: a case study using the Nutrient Index Value method.

Soil fertility (SF) is a crucial factor that directly impacts the performance and quality of crop production. To investigate the SF status in agricultural lands of winter wheat in Khuzestan province, 811 samples were collected from the soil surface (0-25 cm). Eleven soil properties, i.e., electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (TN), calcium carbonate equivalent (CCE), available phosphorus (Pav), exchangeable potassium (Kex), iron (Fe), copper (Cu), zinc (Zn), manganese (Mn), and soil pH, were measured in the samples. The Nutrient Index Value (NIV) was calculated based on wheat nutritional requirements. The results indicated that 100%, 93%, and 74% of the study areas for CCE, pH, and EC fell into the low, moderate, and moderate to high NIV classes, respectively. Also, 25% of the area is classified as low fertility (NIV < 1.67), 75% falls under medium fertility (1.67 < NIV value < 2.33), and none in high fertility (NIV value > 2.33). Assessment of the mean wheat yield (AWY) and its comparison with NIV showed that the highest yield was in the Ramhormoz region (5200 kg.ha-1), while the lowest yield was in the Hendijan region (3000 kg.ha-1) with the lowest EC rate in the study area. Elevated levels of salinity and CCE in soils had the most negative impact on irrigated WY, while Pav, TN, and Mn availability showed significant effects on crop production. Therefore, implementing SF management practices is essential for both quantitative and qualitative improvement in irrigated wheat production in Khuzestan province.

Spatial and temporal distribution of optimal maize sowing dates in Nigeria.

Climate change and inter-annual variability cause variation in rainfall commencement and cessation which has consequences for the maize growing season length and thus impact yields. This study therefore sought to determine the spatially explicit optimum maize sowing dates to enable site specific recommendations in Nigeria. Gridded weather and soil data, crop management and cultivar were used to simulate maize yield from 1981-2019 at a scale of 0.5°. A total of 37 potential sowing dates between 1 March and 7 November at an interval of 7 days for each year were evaluated. The optimum sowing date was the date which maximizes yield at harvest, keeping all other management factors constant. The results show that optimum sowing dates significantly vary across the country with northern Nigeria having notably delayed sowing dates compared to southern Nigeria which has earlier planting dates. The long-term optimal sowing dates significantly (p<0.05), shifted between the 1980s (1981-1990), and current (2011-2019), for most of the country. The most optimum planting dates of southern Nigeria shifted to later sowing dates while most optimum sowing dates of central and northern Nigeria shifted to earlier sowing dates. There was more variation in optimum sowing dates in the wetter than the drier agro-ecologies. Changes in climate explain changes in sowing dates in wetter agro-ecologies compared to drier agro-ecologies. The study concludes that the optimum sowing dates derived from this study and the corresponding methodology used to generate them can be used to improve cropping calendars in maize farming in Nigeria.

Variation and interrelationships in the growth, yield, and lodging of oat under different planting densities.

Background: Oat is a dual-purpose cereal used for grain and forage. The demand of oat has been increasing as the understanding of the nutritional, ecological, and economic values of oat increased. However, the frequent lodging during the growing period severely affect the high yielding potential and the quality of the grain and forage of oat. Methods: Therefore, we used the lodging-resistant variety LENA and the lodging-sensitive variety QY2 as materials, implementing four different planting densities: 2.25×106 plants/ha (D1), 4.5×106 plants/ha (D2), 6.75×106 plants/ha (D3), and 9×106 plants/ha (D4). At the appropriate growth and development stages, we assessed agronomic traits, mechanical characteristics, biochemical compositions, yield and its components. The study investigated the impact of planting density on the growth, lodging, and yield of oat, as well as their interrelationships. Additionally, we identified the optimal planting density to establish a robust crop structure. The research aims to contribute to the high-yield and high-quality cultivation of oat. Results: We observed that with increasing planting density, plant height, grass and grain yields of both varieties first increased and then decreased; root fresh weight, stem diameter, stem wall thickness, stem puncture strength, breaking strength, compressive strength, lignin and crude fiber contents, and yield components decreased; whereas the lodging rate and lodging coefficient increased. Planting density affects lodging by regulating plant height, height of center of gravity, stem wall thickness, internode length, and root fresh weight of oat. Additionally, it can impact stem mechanical strength by modulating the synthesis of lignin and crude fiber, which in turn affecting lodging resistance. Plant height, height of center of gravity, stem wall thickness, internode length, root fresh weight, breaking strength, compressive strength, lignin and crude fiber content, single-plant weight, grain yield and 1,000-grain weight can serve as important indicators for evaluating oat stem lodging resistance. We also noted that planting density affected grain yield both directly and indirectly (by affecting lodging); high density increased lodging rate and decreased grain yield, mainly by reducing 1,000-grain weight. Nonetheless, there was no significant relationship between lodging and grass yield. As appropriate planting density can increase the yield while maintaining good lodging resistance, in this study, 4.5×106 plants/ha (D2) was found to be the best planting density for oat in terms of lodging resistance and grass and grain yield. These findings can be used as a reference for oat planting.

Cover crops support the climate change mitigation potential of agroecosystems.

Cover crops have the potential to mitigate climate change by reducing negative impacts of agriculture on ecosystems. This study is first to quantify the net climate change mitigation impact of cover crops including land-use effects. A systematic literature and data review was conducted to identify major drivers for climate benefits and costs of cover crops in maize (Zea maize L.) production systems. The results indicate that cover crops lead to a net climate change mitigation impact (NCCMI) of 3.30 Mg CO2e ha-1 a-1. We created four scenarios with different impact weights of the drivers and all of them showing a positive NCCMI. Carbon land benefit, the carbon opportunity costs based on maize yield gains following cover crops, is the major contributor to the NCCMI (34.5% of all benefits). Carbon sequestration is the second largest contributor (33.8%). The climate costs of cover crops are mainly dominated by emissions from their seed production and foregone benefits due to land use for cover crops seeds. However, these two costs account for only 15.8% of the benefits. Extrapolating these results, planting cover crops before all maize acreage in the EU results in a climate change mitigation of 49.80 million Mg CO2e a-1, which is equivalent to 13.0% of the EU's agricultural emissions. This study highlights the importance of incorporating cover crops into sustainable cropping systems to minimize the agricultural impact to climate change.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

Intercropping with maize and sorghum-induced saikosaponin accumulation in Bupleurum chinense DC. by liquid chromatography-mass spectrometry-based metabolomics.

Bupleuri Radix is an important medicinal plant, which has been used in China and other Asian countries for thousands of years. Cultivated Bupleurum chinense DC. (B. chinense) is the main commodity of Bupleuri Radix. The benefits of intercropping with various crops for B. chinense have been recognized; however, the influence of intercropping on the chemical composition of B. chinense is still unclear yet. In this study, intercropping with sorghum and maize exhibited little effect on the root length, root diameter, and single root mass of B. chinense. Only the intercropping with sorghum increased the root length of B. chinense slightly compared to the monocropping. In addition, 200 compounds were identified by UHPLC-Q-TOF-MS, and metabolomic combined with the Venn diagram and heatmap analysis showed apparent separation between the intercropped and monocropped B. chinense samples. Intercropping with sorghum and maize could both increase the saikosaponins, fatty acyls, and organic acids in B. chinense while decreasing the phospholipids. The influence of intercropping on the saikosaponin biosynthesis was probably related with the light intensity and hormone levels in B. chinense. Moreover, we found intercropping increased the anti-inflammatory activity of B. chinense. This study provides a scientific reference for the beneficial effect of intercropping mode of B. chinense.

An evaluative technique for drought impact on variation in agricultural LULC using remote sensing and machine learning.

Drought events threaten freshwater reservoirs and agricultural productivity, particularly in semi-arid regions characterized by erratic rainfall. This study evaluates a novel technique for assessing the impact of drought on LULC variations in the context of climate change from 2018 to 2022. Various data sources were harnessed, encompassing Sentinel-2 satellite imagery for LULC classification, climate data from the CHIRPS and AgERA5 databases, geomorphological data from JAXA's ALOS satellite, and a drought indicator (Vegetation Health Index (VHI)) derived from MODIS data. Two classifier models, namely gradient tree boost (GTB) and random forest (RF), were trained and assessed for LULC classification, with performance evaluated by overall accuracy (OA) and kappa coefficient (K). Notably, the GTB model exhibited superior performance, with OA > 90% and a K > 0.9. Over the period from 2018 to 2022, Fez experienced LULC changes of 19.92% expansion in built-up areas, a 34.86% increase in bare land, a 17.86% reduction in water bodies, and a 37.30% decrease in agricultural land. Positive correlations of 0.81 and 0.89 were observed between changes in agricultural LULC, rainfall, and VHI. Furthermore, mild drought conditions were identified in the years 2020 and 2022. This study emphasizes the importance of AI and remote sensing techniques in assessing drought and environmental changes, with potential applications for improving existing drought monitoring systems.

Deer activity levels and patterns vary along gradients of food availability and anthropogenic development.

Animal activity reflects behavioral decisions that depend upon environmental context. Prior studies typically estimated activity distributions within few areas, which has limited quantitative assessment of activity changes across environmental gradients. We examined relationships between two response variables, activity level (fraction of each day spent active) and pattern (distribution of activity across a diel cycle) of white-tailed deer (Odocoileus virginianus), with four predictors-deer density, anthropogenic development, and food availability from woody twigs and agriculture. We estimated activity levels and patterns with cameras in 48 different 10.36-km2 landscapes across three larger regions. Activity levels increased with greater building density, likely due to heightened anthropogenic disturbance, but did not vary with food availability. In contrast, activity patterns responded to an interaction between twigs and agriculture, consistent with a functional response in habitat use. When agricultural land was limited, greater woody twig density was associated with reduced activity during night and evening. When agricultural land was plentiful, greater woody twig density was associated with more pronounced activity during night and evening. The region with the highest activity level also experienced the most deer-vehicle collisions. We highlight how studies of spatial variation in activity expand ecological insights on context-dependent constraints that affect wildlife behavior.

Effective food waste management model for the sustainable agricultural food supply chain.

The extensive research examines the current state of agricultural food supply chains, with focus on waste management in Bandung Regency, Indonesia. The study reveals that a significant proportion of food within the agricultural supply chain goes to waste and discusses the various challenges and complexities involved in managing food waste. The research presents a conceptual model based on the ADKAR change management paradigm to promote waste utilization, increase awareness and change people's behaviors. The model emphasizes the importance of creating awareness, fostering desire, providing knowledge, implementing changes, and reinforcing and monitoring the transformation process. It also addresses the challenges, barriers, and drivers that influence waste utilization in the agricultural supply chain, highlighting the need for economic incentives and a shift in public awareness to drive meaningful change. Ultimately, this study serves as a comprehensive exploration of food waste management in Bandung Regency, shedding light on the complexities of the issue and offering a systematic approach to transition towards more sustainable waste utilization practices.

Effects of experimental drought and plant diversity on multifunctionality of a model system for crop rotation.

In low-diversity productive grasslands, modest changes to plant diversity (richness, composition and relative abundance) may affect multiple ecosystem functions (multifunctionality), including yield. Despite the economic importance of productive grasslands, effects of plant diversity and environmental disturbance on multifunctionality are very rarely quantified. We systematically varied species richness, composition, and relative abundance of grassland ley communities and manipulated water supply (rainfed and drought) to quantify effects of diversity and environmental disturbance on multifunctionality. We then replaced the grassland leys with a monoculture crop to investigate 'follow-on' effects. We measured six agronomy-related ecosystem functions across one or both phases: yield, yield consistency, digestibility and weed suppression (grassland ley phase), legacy effect (effect on follow-on crop yield), and nitrogen fertiliser efficiency (full rotation). Drought reduced most ecosystem functions, although effects were species- and function-specific. Increased plant diversity affected mean performance, and reduced variation, across the six functions (contributing to multifunctional stability). Multifunctionality index values across a wide range of mixture diversity were higher than the best monoculture under both rainfed and drought conditions (transgressive over-performance). Higher-diversity, lower-nitrogen (150N) mixtures had higher multifunctionality than a low-diversity, higher-nitrogen (300N) grass monoculture. Plant diversity in productive grasslands is a practical farm-scale management action to mitigate drought impacts and enhance multifunctionality of grassland-crop rotation systems.

Harnessing Phytohormones: Advancing Plant Growth and Defence Strategies for Sustainable Agriculture.

Phytohormones, pivotal regulators of plant growth and development, are increasingly recognized for their multifaceted roles in enhancing crop resilience against environmental stresses. In this review, we provide a comprehensive synthesis of current research on utilizing phytohormones to enhance crop productivity and fortify their defence mechanisms. Initially, we introduce the significance of phytohormones in orchestrating plant growth, followed by their potential utilization in bolstering crop defences against diverse environmental stressors. Our focus then shifts to an in-depth exploration of phytohormones and their pivotal roles in mediating plant defence responses against biotic stressors, particularly insect pests. Furthermore, we highlight the potential impact of phytohormones on agricultural production while underscoring the existing research gaps and limitations hindering their widespread implementation in agricultural practices. Despite the accumulating body of research in this field, the integration of phytohormones into agriculture remains limited. To address this discrepancy, we propose a comprehensive framework for investigating the intricate interplay between phytohormones and sustainable agriculture. This framework advocates for the adoption of novel technologies and methodologies to facilitate the effective deployment of phytohormones in agricultural settings and also emphasizes the need to address existing research limitations through rigorous field studies. By outlining a roadmap for advancing the utilization of phytohormones in agriculture, this review aims to catalyse transformative changes in agricultural practices, fostering sustainability and resilience in agricultural settings.

Rice husk reuse as a sustainable energy alternative in Tolima, Colombia.

Colombia has great potential to produce clean energy through the use of residual biomass from the agricultural sector, such as residues obtained from the life cycle of rice production. This document presents a mixed approach methodology study to examine the combustion of rice husks as a possible energy alternative in the Tolima department of Colombia. First, the physicochemical characteristics of the rice husk were analyzed to characterize the raw material. Next, System Advisor Model (SAM) software was used to model a bioenergy plant to obtain biochar, bio-oil, and biogas from the combustion of rice husks and generate performance matrices, such as thermal efficiency, heat rate, and capacity factor. Then, the project was evaluated for financial feasibility using a mathematical model of net present value (NPV) with a planning horizon of 5 years. Finally, a subset of the local population was surveyed to assess perspectives on the project in the region. The results of the rice husk physicochemical analysis were the following: nitrogen content (0.74%), organic carbon (38.04%), silica (18.39%), humidity determination (7.68%), ash (19.4%), presence of carbonates (< 0.01%), and pH (6.41). These properties are adequate for the combustion process. The SAM simulation showed that the heat transferred in the boiler was 3180 kW, maintaining an efficiency between 50 and 52% throughout the 12 months of the year, meaning that the rice husk can generate electricity and thermal energy. The financial analysis showed that the internal rate of return (IRR) was 6% higher than the opportunity interest rate (OIR), demonstrating economic feasibility of the project. The design and creation of a rice husk processing plant is socially and environmentally viable and has the potential to contribute to the economic development of the Tolima community and reduce greenhouse gases. Likewise, this activity has the potential to promote energy security for consumers and environmental sustainability while at the same time being economically competitive.

Ecological weed management and square planting influenced the weed management, and crop productivity in direct-seeded rice.

Herbicide use may pose a risk of environmental pollution or evolution of resistant weeds. As a result, an experiment was carried out to assess the influence of different non-chemical weed management tactics (one hoeing (HH) at 12 DAS followed by (fb) one hand weeding at 30 DAS, one HH at 12 DAS fb Sesbania co-culture and its mulching, one HH at 12 DAS fb rice straw mulching @ 4t ha-1, one HH at 12 DAS fb rice straw mulching @ 6 t ha-1) on weed control, crop growth and yield, and economic returns in direct-seeded rice (DSR). Experiment was conducted during kharif season in a split-plot design and replicated thrice. Zero-till seed drill-sown crop (PN) had the lowest weed density at 25 days after sowing (DAS), while square planting geometry (PS) had the lowest weed density at 60 DAS. PS also resulted in a lower weed management index (WMI), agronomic management index (AMI), and integrated weed management index (IWMI), as well as higher growth attributes, grain yield (4.19 t ha-1), and net return (620.98 US$ ha-1). The cultivar Arize 6444 significantly reduced weed density and recorded higher growth attributes, yield, and economic return. In the case of weed management treatments, one HH at 12 DAS fb Sesbania co-culture and its mulching had the lowest weed density, Shannon-weinner index and eveness at 25 DAS. However, one hoeing at 12 DAS fb one hand weeding at 30 DAS (HH + WH) achieved the highest grain yield (4.85 t ha-1) and net returns (851.03 US$ ha-1) as well as the lowest weed density at 60 DAS. PS × HH + WH treatment combination had the lowest weed persistent index (WPI), WMI, AMI, and IWMI, and the highest growth attributes, production efficiency, and economic return.

Use of modelling tools to assess climate change impacts on smallholder oil seed yields in South Africa.

Oil seed crops are the second most important field crops after cereals in the agricultural economy globally. The use and demand for oilseed crops such as groundnut, soybean and sunflower have grown significantly, but climate change is expected to alter the agroecological conditions required for oilseed crop production. This study aims to present an approach that utilizes decision-making tools to assess the potential climate change impacts on groundnut, soybean and sunflower yields and the greenhouse gas emissions from the management of the crops. The Decision Support Tool for Agrotechnology Transfer (DSSAT v4.7), a dynamic crop model and the Cool Farm Tool, a GHG calculator, was used to simulate yields and estimate GHG emissions from these crops, respectively. Four representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5), three nitrogen (0, 75, and 150 kg/ha) and phosphorous (0, 30 and 60 P kg/ha) fertilizer rates at three sites in Limpopo, South Africa (Ofcolaco, Syferkuil and Punda Maria) were used in field trials for calibrating the models. The highest yield was achieved by sunflower across all crops, years and sites. Soybean yield is projected to decrease across all sites and scenarios by 2030 and 2050, except at Ofcolaco, where yield increases of at least 15.6% is projected under the RCP 4.5 scenario. Positive climate change impacts are predicted for groundnut at Ofcolaco and Syferkuil by 2030 and 2050, while negative impacts with losses of up to 50% are projected under RCP8.5 by 2050 at Punda Maria. Sunflower yield is projected to decrease across all sites and scenarios by 2030 and 2050. A comparison of the climate change impacts across sites shows that groundnut yield is projected to increase under climate change while notable yield losses are projected for sunflower and soybean. GHG emissions from the management of each crop showed that sunflower and groundnut production had the highest and lowest emissions across all sites respectively. With positive climate change impacts, a reduction of GHG emissions per ton per hectare was projected for groundnuts at Ofcolaco and Syferkuil and for sunflower in Ofcolaco in the future. However, the carbon footprint from groundnut is expected to increase by 40 to 107% in Punda Maria for the period up to 2030 and between 70-250% for 2050, with sunflower following a similar trend. We conclude that climate change will potentially reduce yield for oilseed crops while management will increase emissions. Therefore, in designing adaptation measures, there is a need to consider emission effects to gain a holistic understanding of how both climate change impacts on crops and mitigation efforts could be targeted.