Feed Demand Research Articles

Forage Seed Systems to Close the Ruminant Feed Deficit in Eastern Africa

This study examines key challenges and opportunities for improving ruminant productivity in East Africa, with a focus on enhancing access to forage seeds critical for livestock systems in Ethiopia, Tanzania, Kenya, Uganda, Rwanda, and Burundi. Despite high potential for increased livestock production, the region faces a significant feed deficit—nearly 40% of annual feed demand remains unmet—due to the limited availability and affordability of forage seeds. The research identifies a critical gap in quality seed access, with many farmers relying on outdated materials. We propose the promotion of recently improved forage varieties and local seed production as a solution to reduce dependence on costly imports and enhance adoption. Our analysis suggests that bridging the forage deficit would require the cultivation of 2 million hectares and the involvement of 1.5 million farmers, highlighting the scale of intervention needed. Additionally, the regional forage seed market presents an economic opportunity, potentially valued at USD 877 million over the next decade, underlining the importance of government policies, the development of seed systems, and market incentives. The study concludes with recommendations for fostering seed production, improving seed distribution, and addressing socio-economic barriers to ensure widespread adoption and enhance livestock productivity in the region.

Biofeed through bioconversion process with the engineered methyl-microbium buryatense strain 5GBC1-RO1

This study presents a novel approach to mitigate methane emissions while simultaneously addressing the growing demand for sustainable animal feed through the development of an engineered methanotrophic strain, Methylomicrobium buryatense 5GB1C-RO1. Utilizing advanced genetic engineering techniques, including CRISPR/Cas9 and horizontal gene transfer, we have optimized the ribulose monophosphate (RuMP) cycle and enhanced oxidase activity in this strain. The bioconversion process is facilitated by innovative bioreactor designs, including Two-Phase Partitioning Bioreactors (TPPBs) and Inverse Membrane Bioreactors (IMBRs), which significantly improve methane solubility and mass transfer. Through metabolic flux analysis and computational modeling, we have achieved high biomass yields and efficient methane utilization. The resulting biofeed demonstrates a superior nutritional profile, with optimized macronutrient content and essential components. This integrated approach not only contributes to greenhouse gas mitigation but also offers a promising solution for sustainable animal nutrition. Our findings suggest that the 5GB1C-RO1 strain and associated bioprocesses have the potential to revolutionize both environmental protection and agricultural sustainability.

Value Chain Analysis of Cattle Feed Market in Banaskantha District, India

India has vast resources of livestock and poultry, which play a vital role in improving the socio-economic conditions of rural masses. Livestock sector contributed 4.90 per cent of total GVA in 2020-21. Birthal and Jha [1] has found feed scarcity as the main limiting factor to improving livestock productivity. Therefore, to increase the productivity potential of milch animals the use of commercial compound feed is inevitable as supply of feed & fodder is shortening due to shrinking pasture land in the country. The Indian feed industry is about 57 years old. India is deficient in fodder and its availability is shrinking day by day due to decrease in cultivable area and increased share in food crop production so more thrust should be directed towards compound feeds by identifying the constraints and removing them.This study focused on the effectiveness of the feed industry and the performance of the value chain when mapping the cattle feed value chain in the Banaskantha district of Gujarat state. Total of ten feed manufacturing units and 120 farmers were selected for the study. The study was based on primary data and secondary data, the primary data were collected through personal interviews with the help of a structured survey schedule. Secondary data on livestock population were collected through the livestock census, Department of Animal Husbandry, Dairying and Fisheries, Ministry of Agriculture, Govt. of India. A multi stage random sampling method was adopted as an appropriate sampling procedure for the study. Banaskantha district was purposely selected for the study as Banaskantha district has highest livestock population in overall Gujarat State. Four talukas from Banaskantha district were selected randomly and feed manufacturing units were selected by snowball sampling method. From each taluka, five villages were selected randomly. From each village, six farmers were selected randomly. The study focused on buying behavior of farmers regarding cattle feed purchase and to estimate demand for concentrated feed requirements for the future in Banaskantha district. The study found that among the eight explanatory (independent) variables, five variables viz., livestock population, farm size, feed price, income from livestock, total feed fed per day were found to be significantly associated with expenditure of cattle feed price per animal per month and other variables such as average age of the animal, technology adoption and distance from buying location were statistically non-significant. The required feed demand for future livestock populations in Banaskantha district are 3651480 metric tonnes, 4487461 metric tonnes, 5514543 metric tonnes, 6774097 metric tonnes and 8315523 metric tonnes for the year 2024, 2029,2034, 2039 and 2044 respectively.

Leveraging genomic prediction to surpass current yield gains in spring barley

Key messageGenetic gain in Nordic spring barley varieties was estimated to 1.07% per year. Additionally, genomic predictive ability for yield was 0.61 in a population of breeding lines.Barley is one of the most important crops in Europe and meeting the growing demand for food and feed requires continuous increase in yield. Genomic prediction (GP) has the potential to be a cost-efficient tool in breeding for complex traits; however, the rate of yield improvement in current barley varieties is unknown. This study therefore investigated historical and current genetic gains in spring barley and how accounting for row-type population stratification in a breeding population influences GP results. The genetic gain in yield was estimated using historical data from field trials from 2014 to 2022, with 22–60 market varieties grown yearly. The genetic gain was estimated to 1.07% per year for all varieties, serving as a reference point for future breeding progress. To analyse the potential of using GP in spring barley a population of 375 breeding lines of two-row and six-row barley were tested in multi-environment trials in 2019–2022. The genetic diversity of the row-types was examined and used as a factor in the predictions, and the potential to predict untested locations using yield data from other locations was explored. This resulted in an overall predictive ability of 0.61 for yield (kg/ha), with 0.57 and 0.19 for the separate two-row and the six-row breeding lines, respectively. Together this displays the potential of implementing GP in breeding programs and the genetic gain in spring barley market varieties developed through GP will help in quantifying the benefit of GP over conventional breeding in the future.

Valorization of soybean by-products for sustainable waste processing with health benefits.

Soybean is a rapidly growing agricultural crop, fueled by the rising global demand for animal feed, plant-based proteins and essential nutrients for human consumption. Soybeans contain a wide range of essential nutrients that are vital for health and may play a significant role in disease prevention. Their nutritious composition has led to a diverse range of soy-based foods and derivatives available on the market. A substantial amount of soybeans is allocated to the animal feed sector, human consumption, nutraceuticals and other industrial applications. Soybean has superior protein quality and digestibility compared to other legumes. It also contains abundant amounts of isoflavones, phytosterols and minerals that augment its nutritional value as a constituent of the human diet. Many different by-products are produced during the processing of soy. Due to a lack of sustainable food manufacturing practices, a sustainable amount of these by-products is discarded as waste, posing environmental challenges. Developing an effective waste management system for soybean by-products can help address public health concerns and provide a cost-efficient way to repurpose valuable components of soy, such as soy meal, okara and soy whey. Such valorization can mitigate the environmental impact of waste and contribute to malnutrition. This study aims to evaluate the sustainable use of soy by-products to help preserve biodiversity and reduce food insecurity. This article thoroughly examines the fundamental components of soybean use in the fields of health and soybean by-product processing. It provides a summary of cost-efficient, feasible and ideal processing technologies. © 2024 Society of Chemical Industry.

Impacts of alfalfa nutrient concentrate on pellet physical attributes, growth performance, metabolism and nutritional quality of rainbow trout, Oncorhynchus mykiss

This study addresses the escalating demand for aquatic feed by exploring the potential of alfalfa nutrient concentrate (ANC) as feed ingredient for rainbow trout. Test diets contained varying ANC levels (0%, 5%, 10%, 15%, and 20%) to replace fishmeal (32% in the 0% ANC diet) to achieve equal digestible protein and were processed using a cooking extrusion method. Analysis of feed pellets showed that pellet density increased with ANC levels (P < 0.001), resulting in sinking pellets at 20% ANC. Water stability and durability were improved while oil leakage decreased with increasing ANC levels (P < 0.05). Two feeding trials were conducted to test the diets in flow-through water systems with three replicates per diet. The first 10-week trial evaluated their impact on feeding, fecal physical quality, and the apparent digestibility coefficient (ADC) of dietary nutrients in rainbow trout (initial body weight , 18.0 ± 0.2 g). ANC inclusion did not impact the palatability and satiety feed intake of the fish (P > 0.05). However, the ADC of dry matter and phosphorus significantly decreased in fish fed the 20% ANC diet (P < 0.05). The second 9-week trial investigated the growth performance, nutrition quality, and metabolism of rainbow trout (initial body weight, 19.0 ± 0.2 g). While all fish exhibited substantial growth, fish fed diets with 10% to 20% ANC displayed lower specific growth rate and higher feed conversion ratio compared to those fed with 0% or 5% ANC (P < 0.05). The whole body protein content was higher in fish fed 5% ANC compared to all other treatments (P = 0.030). The biochemical parameters of plasma were similar across treatments, except for a decrease in plasma phosphorus levels in fish fed a 10% ANC diet compared to those fed a 0% ANC diet. (P = 0.033). Significant changes were observed in liver metabolism including tricarboxylic acid cycle, amino acid and energy metabolism pathways in fish fed the 20% ANC diet versus the 0% ANC diet (P < 0.05). These results demonstrate that ANC inclusion improved pellet physical quality without impairing feeding behavior and nutritional quality of the fish but inclusion ≥10% in the diet reduced fish growth. This study offers the first comprehensive assessment of the potential of ANC used in fish feed involving feed management, feeding evaluation, and the biological response.

The Effects of Egg- and Substrate-Associated Microbiota on the Larval Performance of the Housefly, Musca domestica.

Increasing human population size and income growth are causing an increasing demand for food and feed. Insects are a more sustainable alternative to conventional animal source proteins, as they can convert waste and by-products from the agricultural industry into biomass for commercial feed for livestock and, potentially, serve as a food source for human consumption. Moreover, insects together with their microorganisms have been shown to play a pivotal role in the development of insects and in the breakdown of complex growth substrates, and are, therefore, closely tied to insect production. This study aims to determine if the removal of egg- and substrate-associated microorganisms impacts larval performance (growth, final biomass, and the survival rate) of M. domestica Linnaeus. Four treatments are tested: disinfected eggs and non-autoclaved substrate, non-disinfected eggs and autoclaved substrate, disinfected eggs and autoclaved substrate, and a control without any removal of microbiota. The results show a significant decrease in the final biomass of larvae subjected to the treatments with only disinfected eggs, only autoclaved substrate, and both compared to the control, and a significant decrease in survival rate for non-disinfected eggs and autoclaved substrate and disinfected eggs and autoclaved substrate compared to the control group. Moreover, larval growth shows a significant difference across days within all treatments. Together, this suggests that the microorganisms of housefly eggs and the growth substrate play an important role in biomass, which is critical in commercial insect production. Together this suggest, that more studies are needed to examine these parameters with respect to more commercially relevant substrates.

Strategic pathways for a bioeconomy with high value-added products: Lessons learnt from the Latvian forest sector

Climate change, the increasing global demand for food and feed and the loss of biodiversity, requires a shift towards a sustainable, innovative and knowledge-based bioeconomy. Simultaneously, the implementation of a strategic approach to the utilization of the sector and associated bioresources that aligns with the principles of sustainability represents a significant challenge. The aim of this study is therefore to develop a methodology for policy and decision makers to facilitate a knowledge-based bioeconomy policy planning framework. To map the development opportunities of the bioeconomy, a detailed analysis was conducted on the Latvian forest sector. This entailed an examination of the system's components within the sector, which involved the collection and analysis of statistical data, reports, and official information from forest sector stakeholders, with the objective of obtaining a comprehensive overview of Latvia's forest sector. Following the identification of the current enablers and constraints of the sector, a selection of niche products with high value-added for sector development was made. During group model building experts: (1) developed a SWOT matrix for the Latvian forest sector; (2) selected three wood-based high value-added products – textile from trees; particle board; natural thermal packaging; (3) elaborated SWOT/TOWS analysis to facilitate strategy development for the niche products. The developed methodology incorporates a synthesis of established scientific methods, including SWOT, TOWS, and AHP, with a bioeconomy system component analysis approach. The methodology therefore not only systematises the process of forest sector analysis, but also applies the findings to the development of sector development pathways aimed at shifting from low value-added to high value-added production. The innovation of the methodology lies not only in the combination of methods used, but also in the insights gained, which allow for a narrower product development perspective to be taken in order to understand the enablers and constraints of the sector and vice versa.

Applying equineRSU and seasonal livestock correction to wider equine stud farm types

ABSTRACT To adjust for potential overestimations in the nitrogen excretion by horses within Overseer®, a revised stock unit system has been proposed (equineRSU). These equineRSU were generated and validated using a medium-sized equine farm as a model farm. The aim of this study was to test the application of the equineRSU and seasonal stock number adjustment on a more complex farming model. Livestock numbers and management data were captured prospectively for the base property (128.8 ha) of a large multi-property commercial breeding stud (3 support properties, 556 ha total area) between June 2022 and May 2023. The monthly on-farm metabolisable energy (ME) requirement and pasture demand were deterministically modelled with both a customised feed budget using livestock class and weight or using the equineRSU and monthly adjusted stock numbers. There were multiple complex movements of horses on and off the property in the different stock classes across the year, with stock management reflecting seasonal periods of high stocking density. There was good agreement (6% variance) between actual ME demand (complex feed budget) and estimated feed demand (simplistic equineRSU model) of 4,387,187 MJ vs 4,102,770 MJ. This suggests that the equineRSU could be used on complex equine farm systems within Overseer®.

Estimating energy flows in the long run: Agriculture in the United States, 1800–2020

This article explores the methods of a prior larger research project to understand flows in the US energy economy, quantifying energy use across American history (1800–2020). As a case study, it uses a subset of this data—agricultural energy use—to examine the methods, sources, and problems around estimating the production and consumption of energy at a national level. By combining statistical data with primary sources (like government and private studies on livestock feed demands), we produce a database that sums all energy used both on-field and in the processing and production of food more generally—and offer several counterintuitive conclusions. Per-capita agricultural energy use actually fell between 1800 and 2020. During this time period, the overall per-capita energy expenditure on food (in processing and cooking) remained fairly steady. We conclude the article by noting various uses for the data in reframing long-term agricultural trends and their environmental impacts. Energy flows are a fundamental component of social metabolism research. What this paper adds to this work is an unusual American case, one in which per capita on-field energy use declined.

FRAGILITY OF GRANULED COMBINED FEEDS

The compound feed industry produces compound feed by combining different ingredients (types of raw materials) in different combinations and proportions. This determines the name "combi feed". Compound feeds are composed in such a way that the disadvantages of some ingredients (low protein content, lack of vitamins, etc.) are compensated by the advantages of other ingredients. The main purpose of compound feed is to produce compound feed that meets the nutritional needs of livestock, domestic animals and poultry and ensures their growth, development and safety. The role of compound feed has increased with the development of industrial agriculture. The demand for compound feed on industrial livestock and poultry farms has increased significantly. Compound feed becomes a connecting link between nature and animals. Animals are kept in cages or stables and are deprived of contact with wild nature, so they receive all the nutrients necessary for growth and development with food. There are different types of compound feed, including loose, granulated and briquetted, characterized by such quality indicators as fragility.

Pearl millet a promising fodder crop for changing climate: a review.

The agricultural sector faces colossal challenges amid environmental changes and a burgeoning human population. In this context, crops must adapt to evolving climatic conditions while meeting increasing production demands. The dairy industry is anticipated to hold the highest value in the agriculture sector in future. The rise in the livestock population is expected to result in an increased demand for fodder feed. Consequently, it is crucial to seek alternative options, as crops demand fewer resources and are resilient to climate change. Pearl millet offers an apposite key to these bottlenecks, as it is a promising climate resilience crop with significantly low energy, water and carbon footprints compared to other crops. Numerous studies have explored its potential as a fodder crop, revealing promising performance. Despite its capabilities, pearl millet has often been overlooked. To date, few efforts have been made to document molecular aspects of fodder-related traits. However, several QTLs and candidate genes related to forage quality have been identified in other fodder crops, which can be harnessed to enhance the forage quality of pearl millet. Lately, excellent genomic resources have been developed in pearl millet allowing deployment of cutting-edge genomics-assisted breeding for achieving a higher rate of genetic gains. This review would facilitate a deeper understanding of various aspects of fodder pearl millet in retrospect along with the future challenges and their solution. This knowledge may pave the way for designing efficient breeding strategies in pearl millet thereby supporting sustainable agriculture and livestock production in a changing world.

Dairy Farmers’ Perception on Dairy Cattle Feeds and Feeding System: Brewery By-product Utilization Practice at Kombolcha Regiopolitan City, Ethiopia

A survey was conducted in three selected kebeles of Kombolcha Regiopolitan City in South Wollo Zone. The selected kebeles were in proximity to brewery factory, Ethiopia. A semi-structured questionnaire was used to collect perception data from purposively selected three kebeles and a total of 57 randomly selected dairy farmers. Data collected were analyzed with a statistical package for social sciences (Version 20) and Microsoft Excel (2010). Smallholder dairy farmers had average herd size of 10.7 and 0.08 hectare total dairy farm per household. Average daily milk yield and lactation length of dairy cows was 10.8 liter/day and 9.5 months, respectively. The majority of farmers (59.6%) reared dairy cows with 50-75% exotic blood. The most smallholder dairy farmers satisfied their feed demand from the market and had a trend of providing both dense and poor quality affordable feed ingredients. Smallholder dairy farmers gave priority to feed different kind of cattle in the order of lactating cows, calve, pregnant, heifers and dry cows and used (100%) wet brewery spent grain as a source of dairy feed. Though, there was a brewery by-product supply deficit for half of year. The majority smallholder dairy farmers delivered brewery by-product directly from the brewery factories and followed by wholesalers and retailers. The majority of smallholder dairy farmers (96.4%) stated that the brewery spent grain was fed and stored freshly using different conservation techniques, and the remaining stored in ensiled (1.8%) and dried (1.8%) forms. Smallholder dairy farmers had no brewery spent grain ensiling practice and feeding brewery yeast to dairy animals. Smallholder dairy farmers indicated that high price due to abnormal market chain and shortage of brewery spent grain supply were the major challenges to sustain dairy industry in the study area, and the majority (40.6%) claimed to get swift solutions at high cost and less accessible brewery by-product.

Modelling reverse osmosis process and multiple effect evaporator process of common effluent treatment plant using artificial neural network

This paper presents the study on modeling the performance of Textile and Tannery Common Effluent Treatment Plant (CETP) using Artificial Neural Networks (ANN). The process of Reverse Osmosis (RO) Process and Multiple Effect Evaporator (MEE) process of CETP is modelled using ANNs. Two ANN models were developed to predict the performance of CETP dealing with highly saline effluents. Model-I (RO Model) predicts TDS Rejection (%) by taking pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS), Chemical Oxygen Demand (COD) of RO feed as input parameters thus providing estimate of salt concentration. Model-II (MEE Model) predicts Steam Output needed by taking pH, TDS, Feed flow as input parameters. Levenberg-Marquardt backpropagation (trainlm) algorithm was used as training function for creation of both of ANN models and Hyperbolic Tangent Sigmoid (tansig) was used as transfer function. Separate model validation has been performed to test the predictability of the model and at last prediction accuracy were analysed to show its efficiency in prediction.

Discrimination of maize transgenic and non-transgenic varieties by laser induced spectroscopy (LIBS) and machine learning algorithms

BackgroundIn the last decades, the production and consumption of genetically modified agricultural products have increased markedly due to the worldwide population growth and rising demand for food and feed. Consequently, genetically modified crops have been extensively produced and consumed, which required identifying and discriminating transgenic and non-transgenic products. ResultsLaser-induced breakdown spectroscopy (LIBS) combined with chemometric methods was applied to identify and discriminate two varieties of conventional (not-genetically modified, NGM) maize from four varieties of transgenic maize (genetically modified, GM). The LIBS spectra acquired under reduced pressure (100 Torr) conditions over two ranges, i.e., 175–330 nm and 275–770 nm, were subjected to Standard Normal Variation (SNV) and multivariate methods such as Principal Component Analysis (PCA) to reduce data matrices dimensionality and spectral noise. The supervised machine learning algorithms k-nearest neighbor (k-NN) and support vector machine (SVM) have been applied to discriminate among NGM and GM maize reserving 25 % of data for external validation. The training data were employed for hyperparameter optimization of classifiers using the Leave-One-Out Cross-Validation (LOOCV) method. Considering all six maize varieties simultaneously, the highest training accuracy achieved was 90.56 %, with an external validation accuracy of 88.33 %. In an alternative approach based on pairwise combinations of one GM variety against one NGM variety, the best outcome achieved was 100 % LOOCV and external validation accuracy. ConclusionsThese results showed that LIBS supported by appropriate chemometric methods represents an alternative screening technique for identifying and discriminating transgenic from non-transgenic maize.

Tools and Techniques to Accelerate Crop Breeding.

As climate changes and a growing global population continue to escalate the need for greater production capabilities of food crops, technological advances in agricultural and crop research will remain a necessity. While great advances in crop improvement over the past century have contributed to massive increases in yield, classic breeding schemes lack the rate of genetic gain needed to meet future demands. In the past decade, new breeding techniques and tools have been developed to aid in crop improvement. One such advancement is the use of speed breeding. Speed breeding is known as the application of methods that significantly reduce the time between crop generations, thereby streamlining breeding and research efforts. These rapid-generation advancement tactics help to accelerate the pace of crop improvement efforts to sustain food security and meet the food, feed, and fiber demands of the world's growing population. Speed breeding may be achieved through a variety of techniques, including environmental optimization, genomic selection, CRISPR-Cas9 technology, and epigenomic tools. This review aims to discuss these prominent advances in crop breeding technologies and techniques that have the potential to greatly improve plant breeders' ability to rapidly produce vital cultivars.

A comparative analysis reveals the genomic diversity among 8 Muscovy duck populations.

The Muscovy duck (Cairina moschata) is a waterfowl indigenous to the neotropical regions of Central and South America. It has low demand for concentrated feed and strong adaptability to different rearing conditions. After introduced to China through Eurasian commercial trade, Muscovy ducks have a domestication history of around 300 years in the Fujian Province of China. In the 1990s, the commodity Muscovy duck breed "Crimo," cultivated in Europe, entered the Chinese market for consumption and breeding purposes. Due to the different selective breeding processes, Muscovy ducks have various populational traits and lack transparency of their genetic background. To remove this burden in the Muscovy duck breeding process, we analyzed genomic data from 8 populations totaling 83 individuals. We identify 11.24 million single nucleotide polymorphisms (SNPs) and categorized these individuals into the Fujian-bred and the Crimo populations according to phylogenetic analyses. We then delved deeper into their evolutionary relationships through assessing population structure, calculating fixation index (FST) values, and measuring genetic distances. Our exploration of runs of homozygosity (ROHs) and homozygous-by-descent (HBD) uncovered genomic regions enriched for genes implicated in fatty acid metabolism, development, and immunity pathways. Selective sweep analyses further indicated strong selective pressures exerted on genes including TECR, STAT2, and TRAF5. These findings provide insights into genetic variations of Muscovy ducks, thus offering valuable information regarding genetic diversity, population conservation, and genome associated with the breeding of Muscovy ducks.

Evaluation of the Microbial Quality of Hermetia illucens Larvae for Animal Feed and Human Consumption: Study of Different Type of Rearing Substrates.

In the context of climate change and depletion of natural resources, meeting the growing demand for animal feed and human food through sufficient, nutritious, safe, and affordable sources of protein is becoming a priority. The use of Hermetia illucens, the black soldier fly (BSF), has emerged as a strategy to enhance the circularity of the agri-food chain, but its microbiological safety remains a concern. The aim of the present study was to systematically review available data on the microbiological quality of BSF and to investigate the impact of using four different rearing substrates including classic options allowed by the EU regulation (cereals, fruits, vegetables) and options not allowed by EU regulations regarding vegetable agri-food (co-products, food at shelf life, and meat). A total of 13 studies were collected and synthesized, including 910 sample results, while 102 new sample results were collected from the present experiments in three farms. Both datasets combined revealed a high level of contamination of larvae, potentially transmitted through the substrate. The main pathogenic bacteria identified were Bacillus cereus, Clostridium perfringens, Cronobacter spp., Escherichia coli, Salmonella spp., and Staphylococcus aureus coagulase-positive, while Campylobacter spp. and Listeria monocytogenes were not detected. Any of these four substrates were excluded for their use in insect rearing; however, safety concerns were confirmed and must be managed by the operators of the sector using microbial inactivation treatment after the harvest of the larvae in order to propose safe products for the market. The results obtained will guide the definition of the control criteria and optimize the following manufacturing steps.

Mapping the dynamics of intensive forage acreage during 2008–2022 in Google Earth Engine using time series Landsat images and a phenology-based algorithm

The increasing demand for livestock feed in China has led to a remarkable increase in forage acreage, particularly in forage fields under intensive agricultural management. However, there are no maps currently available to demonstrate and document the spatial and temporal patterns of the continuous expansion of intensive forage. Here we proposed a pixel- and phenology-based approach to map intensive forage by utilizing time series Landsat images and the Google Earth Engine platform. Given the challenge of accurately identifying intensive forage fields in diverse regions using fixed multi-temporal Landsat data, along with the distinct phenological characteristics of intensive forage fields that undergo multiple annual harvests, we proposed an innovative data mining approach based on dynamic monthly composite images (DMCI). This approach aims to enhance the algorithm’s efficiency and precision in identifying intensive forage. As a pilot study, we analyzed all available Landsat images (1732 scenes) from 2008 to 2022 in a county located in the farming-pastoral ecotone of northern China and tracked the historical dynamics of intensive forage expansion over five epochs at three-year intervals. The accuracy assessment of the intensive forage maps for the five epochs using validation sample points showed that the overall accuracy and Mathews correlation coefficient ranged from 97.1 % to 98.9 % and 0.93 to 0.98 respectively. The intensive forage acreage in the study area exhibited a dramatic expansion from 2008 to 2022, particularly after the 2012s. This study demonstrates the potential of our DMCI- and phenology-based algorithm and time series Landsat images for tracking the dynamics of intensive forage acreage at 30-m resolution in temperate single-cropping regions.

A preliminary analysis and estimation of the status of feed and fodder in Kerala

The livestock sector is experiencing continuous global growth, projected to surge by 60–70 per cent by 2050, with developing countries bearing the brunt of this expansion. This trend strongly suggests that the demand for and supply of feed and fodder in developing nations must increase proportionally to avoid a crisis. However, severe data scarcity hampers efforts to determine what and how much to produce. This situation is mirrored in the Indian state scenario, including Kerala. This paper addresses these challenges by analysing the dynamics of feed and fodder demand and supply in Kerala, utilizing a blend of primary and secondary data. It examines the unique characteristics and likely reasons behind them, contrasting Kerala's situation with that of other developing countries. The findings reveal a deficit in feed and fodder supply in the state, reflecting broader trends in developing countries, albeit with a surplus of green fodder. The study also delves into the determinants of agricultural income for animal-rearing farmers in Kerala, highlighting distinct social dimensions. Considering these insights, the study recommends critical policy interventions to address the feed and fodder deficit, emphasizing the potential for leveraging indigenous approaches to mitigate the shortfall.