Single Crop Research Articles

Crop yield forecasting and associated optimum lead time analysis based on multi-source environmental data across China

Accurate and timely crop yield forecasts can provide essential information to make conclusive agricultural policies and to conduct investments. Recent studies have used different machine learning techniques to develop such yield forecast systems for single crops at regional scales. However, no study has used multiple sources of environmental predictors (climate, soil, and vegetation) to forecast yields for three major crops in China. In this study, we adopted 7-year observed crop yield data (2013–2019) for three major grain crops (wheat, maize, and rice) across China, and three major data sets including climate, vegetation indices, and soil properties were used to develop a dynamic yield forecasting system based on the random forest (RF) model. The RF model showed good performance for estimating yields of all three crops with correlation coefficient (r) higher than 0.75 and normalized root means square errors (nRMSE) lower than 18.0%. Our results also showed that crop yields can be satisfactorily forecasted at one to three months prior to harvest. The optimum lead time for yield forecasting depended on crop types. In addition, we found the major predictors influencing crop yield varied between crops. In general, solar radiation and vegetation indices (especially during jointing to milk development stages) were identified as the main predictor for winter wheat; vegetation indices (throughout the growing season) and drought (especially during emergence to tasseling stages) were the most important predictors for spring maize; soil moisture (throughout the growing season) was the dominant predictor for summer maize, late rice, and mid rice; precipitation (especially during booting to heading stages) was the main predictor for early rice. Our study provides insights into practical crop yield forecasting and the understanding of yield response to environmental conditions at a large scale across China. The methods undertaken in this research can be easily implemented in other countries with available information on climate, soil, and vegetation conditions.

Better irrigation management using the satellite-based adjusted single crop coefficient (aKc) for over sixty crop types in California, USA

Several Surface Energy Balance (SEB) models are currently used to unleash the boundless potential of Geographic Information System (GIS) and Remote Sensing (RS) techniques. Their main output, namely the actual evapotranspiration (ETa), is required for the assessment of water budget at basin, regional and national levels. Still, the lack of the required expertise coupled with sometimes missing input data limits their usage by researchers, policy makers, water managers and farmers. In this study, a novel comprehensive monthly adjusted crop coefficients (aKc) list was produced, in which these coefficients are usually used to retrieve the much-needed ETa using the climate-sensitive reference crop evapotranspiration (ET0). More particularly, the ETa is calculated for more than sixty different crop types present in the Mediterranean-climate California, United States. It is based on the two consecutive years of 2018 and 2019, which were wet and dry, respectively. The Google Earth Engine (GEE) version of the Surface Energy Balance Algorithm for Land-Improved (SEBALI), surnamed SEBALIGEE, with an Absolute Mean Error (AME) of 9.56 mm/month in the study area was used along the annual crop map from the United States Department of Agriculture (USDA). The main results showed that the average monthly aKc values ranged between 0.45 and 1.53. More specifically, October and November presented the lowest aKc values averaged among all crop types with aKc values around 0.76. On the other hand, March, April and June had the largest average aKc values at nearly 1.17. The monthly aKc values of the 60 crops studied in the plain of California, would potentially assist authorities and hydrologists to better calculate the actual monthly plant water consumption, in a simpler way over the upcoming years.

Avian species richness in traditional rice ecosystems: a case study from upper Myanmar

Rice Oryza sativa ecosystems provide foraging and nesting habitat for a variety of birds. Myanmar is a major rice-producing nation and yet bird use of rice ecosystems remains largely unstudied. We present the results of a case study of avian species richness in a traditional rice ecosystem at Limpha Village in upper Myanmar. The rice field at Limpha occupies 17.5 ha where a single crop is produced each year without chemical inputs (fertilizer and pesticides). Village lands are contiguous with the buffer zone of Htamanthi Wildlife Sanctuary. We conducted bird surveys of the rice field during dry and wet seasons (2013–20) and documented the occurrence of 85 species (exclusive of Buttonquail these included 58 resident species, 20 migratory species, six species with both resident and migratory populations in upper Myanmar), including 10 species of conservation concern. Species richness was greatest during the dry season when an influx of Palearctic migrants was present. We ranked 52 species as Common, 23 as Uncommon, and 10 as Rare. Most birds used the rice field as foraging rather than breeding habitat. Insectivore was the most common feeding guild (43 species), followed by Omnivore (22 species), Carnivore (12 species), Granivore (6 species), Frugivore (1 species), and Nectarivore (1 species) guilds. We observed eight species associated with domestic Water Buffalo Bubalus bubalis and 15 species foraging at active fires or in burned areas in the rice field. Piles of rice straw are important foraging sites for several species. Low intensity agricultural practices, habitat heterogeneity, and proximity to the nearby swamp, forest, & Chindwin River are probably responsible for the relatively high avian species richness at Limpha. Future agricultural intensification could negatively impact avian species richness in the Limpha rice field. Our findings suggest that traditional rice agriculture is compatible with conservation objectives in the buffer zone of Htamanthi Wildlife Sanctuary. Our study, however, requires replication before generalizations can be made concerning the value of traditional rice ecosystems to avian conservation in Myanmar.

Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine

With the decline of cultivated land quality and area in recent decades, the intensification of land use plays an important role in meeting the growing demand for food. Cropping intensity refers to the number of crop planting cycles in one year, which is important for improving food production and safety at the local, regional and national scales. Therefore, it is necessary to develop an accurate high spatial resolution dataset of cropping intensity. The existing datasets of cropping intensity were generally developed based on MODIS or Landsat images, both of which have defects in spatial and temporal resolutions. In this paper, we improved the quality of the dataset on the Google Earth Engine (GEE) platform, and developed a new algorithm incorporating crop phenology. The algorithm was based on the Landsat 7/8 and Sentine-2A/B time series imageries to map the 30 m cropping intensity in the Huaihe basin in 2018 by extracting complete growth cycle. Results show that single cropping, double cropping and triple cropping in the Huaihe basin accounted for 41.6%, 57.7% and 0.7% of the total cultivated area in 2018, respectively, and the proportion of multiple cropping reached 58.4%. The accuracy of single cropping, double cropping and triple cropping are 92.93%, 91.39%, and 72.78% respectively. The overall accuracy is 91.38% and the kappa coefficient is 0.84. This algorithm accurately captures the seasonal dynamics of planting patterns in arable land, which can be used to produce cropping intensity products with high-resolution and provide a reference for large-scale regional vegetation monitoring.

The complexity of measuring cropland use intensity: An empirical study

CONTEXTCropland intensification promises additional food supply without further expanding croplands into natural ecosystems. Cropland use intensity measures the degree of intensification by various indicators, both from input and output perspectives; all of these indicators are however not always coherent—which may cause confusion and send contradicting signals to policymakers. Few empirical studies have been conducted to relate and compare various intensity indicators. OBJECTIVEIn this study, we start with a hypothesis that cropland use intensity measured by different indicators may differ. We then test such a hypothesis based on empirical evidence in a typical multi-cropped region (i.e., Jinxian County) in Jiangxi Province—a major breadbasket in Southern China. METHODSWe focus on two widely used indicators, i.e., multi-cropping frequency (MCF) and crop growth duration (GDa), measured by a hybrid time-series remote sensing dataset which fuses MODIS and Gaofen-1 images for the year 2015. We map these two indicators independently at a 16 m spatial resolution. For each pixel, MCF takes value from 1 to 3 corresponding to single-, double- and triple- cropping; while GDa is quantified by the accumulative crop growth days within the study year. We relate the values of two indicators, summarize the descriptive statistics of GDa grouped by MCF categories, and compare MCF and GDa values by using a box-whisker chart, a bivariate map, and a set of statistical tests. RESULTS AND CONCLUSIONSWe find that a significant overlap of GDa exists between single cropping and double cropping both visually and statistically. In other words, the different cropland intensity levels measured by MCF appear to be the same if measured by GDa. The box-whisker chart shows that the GDa of single cropping in the upper quarter is substantially longer than the GDa of double cropping in the lower quarter. The non-parametric tests show that the overlapped GDa between single cropping and double cropping exists substantially, which may cause confusion. Moreover, the bivariate map shows that single cropping with shorter growth duration (i.e., consistent low intensity) and double cropping with longer growth duration (i.e., consistent high intensity) account for 44% and 28%, respectively, while the rest manifested as inconsistent which accounts for another 28% of the total cropland area. SIGNIFICANCEThese results confirm our hypothesis—the inconsistency among intensification indicators. Different measurements may convey contradicting messages for policymakers in pursuing the sustainable intensification goals, which suggests more efforts are required to understand the multidimensionality of the process of cropland intensification for distinct policy measurements.

Constructing Crop Portraits Based on Graph Databases Is Essential to Agricultural Data Mining

Neo4j is a graph database that can use not only data, but also data relationships. Crop portraits, a kind of property graph, model the crop entity in the real world based on data to realize the networked management of crop knowledge. The existing crop knowledge base has shortcomings such as single crop variety, incomplete description, and lack of agricultural knowledge. Constructing crop portraits can provide a comprehensive description of crops and make up for these shortcomings. This research used agricultural question-and-answer data and popular science data obtained by text crawling as the original data, selected labels to establish a crop portrait that including three categories (crops, pesticides, and diseases and pests), and used the graph database (Neo4j) to store and display these portrait data. Information mining found that the crop portrait revealed the occurrence trend of diseases and pests, exhibited a nonintrinsic connection between different diseases and pests, and provided a variety of pesticides to choose from for control of diseases and pests. The results showed that constructing crop portraits is beneficial to agricultural analysis, and has practical application values and theoretical research prospects in the field of big data analytics.

Improvement of Intercropping Performance Assessment in a System with Difference of Crops’ Cycle Durations: Calculation Methods for Rice-cassava Association

Crop performance evaluation in intercropping involving booth crops with different cropping cycle durations is still a concern for agronomic research while farmers already adopted this agro-system in spite of yield decreasing of one of the component. Agronomic trial involving rice-cassava intercropping with different densities of cassava planting was undertaken in western Côte d'Ivoire to determine most reliable parameters for performance index calculation comparing the conventional to new proposed methods. In a Fisher design, number of root/plant, cassava yield, cassava yield/2 rice cycles, 1000 rice grain weight, and rice grain yield were collected for exploring their reliability on global yield determination. Area Time Equivalent Ratio (ATER) and Crop Performance Ratio Time corrected were calculated as conventional method of performance index assessment while, average yield of cassava for two cycle of rice (ATER1) and the mean for both cropping cycles of rice (ATER2) were proposed as new methods. Except the variation of yield in single cropping, no significant effect of cassava planting density was recorded in global production and ATER value overlapped the threshold value of 1 in some extend. Only calculated yield were reliable for global production pointing out ATER1 as the most consistent method for rice-cassava intercropping performance determination. Rice grain yield and half of cassava yield calculated on basis of two rice cropping cycle were revealed as the most reliable parameters for ATER calculation highlighting such method viability for intercropping performance assessment when involving two crops with different cycle durations.

Measuring the Impact of the Multiple Cropping Index of Cultivated Land during Continuous and Rapid Rise of Urbanization in China: A Study from 2000 to 2015

With the continuous and rapid rise of urbanization in China, land use transition research has been carried out extensively. Multiple cropping is the content of land use recessive morphology research, and it is also a common agricultural system in China. Accordingly, further research on multiple cropping index (MCI) can enrich the land use transition research and help to evaluate China’s food security. In order to examine the spatiotemporal changes and factors influencing the MCI of cultivated land in China, we collected MODIS remote sensing image data and land use classification data and conducted a remote sensing inversion on China’s MCI from 2000, 2005, 2010, and 2015. The spatial distributions and evolution processes of the MCI were explored through spatial mapping, statistical analysis, and processing with the Geographic Information System; moreover, the influencing factors of MCI were explored quantitatively with principal component regression. The results were as follows: (1) at the provincial scale, the average MCI across Guangdong, Guangxi, Hainan, Henan, Anhui, and Jiangsu was high; meanwhile, the average MCI across Heilongjiang, Inner Mongolia, Ningxia, and Qinghai was low. Between 2000 and 2015, the number of provinces with low MCI increased gradually, and the average MCI decreased greatly in the southern provinces. (2) At the county scale, the Taihang Mountains, Qinling Mountains, and Hengduan Mountains formed the boundary of China’s single cropping and multiple cropping indices. Dynamic changes in China’s MCI were obvious, and the number of counties with MCI change values lower than 0 increased gradually. Last, (3) natural conditions, nonagricultural process, cultivated land quality, and agricultural intensification demonstrated different degrees of impact on the MCI; in particular, the influence of nonagricultural industries, pesticides, and agricultural plastic film on the MCI proved especially important. Future research should strengthen the existing work on related transformations in farmers’ livelihoods, especially in terms of the return of rural labor force, the body of agricultural production, agricultural ecological issues, and the balance between increased crop production and reduced environmental pollution. In addition, agricultural policy design should pay more attention to cultivated land quality, the farmer who cultivates the land, and the multiple cropping potential of cultivated land.

English

Intercropping is considered as an improved system of multiple cropping systems which safeguards crop stand and improves crop production. The main goal of intercropping is to produce high yield from piece of a land by judicious use of available resources which otherwise may not be exploited by a single crop. A study was executed to investigate productivity and resource use in a maize–grain legume intercropping at University of Agriculture, Faisalabad during 2017 and 2018. Experimental treatments included maize, mungbean, mash-bean, and cowpea monocultures (sole crops), and intercropping combinations of maize + mungbean, maize + mash-bean, and maize + cowpea. Highest maize grain yield was observed in maize sole cropping (6520 and 6813 kg ha-1) and maize + mungbean intercropping (6375 and 6542 kg ha-1) during 2017 and 2018 growing seasons, respectively. Maximum seed yield in grain legumes was observed in mung and mash bean sole cropping during both years. Land equivalent ratio (LER) was maximum in maize + cowpea (1.83 and 1.87) and maize + mungbean intercropping (1.77 and 1.80) during both years, respectively. Maximum net economic return (ER) of PKR 134158 ha-1 (≈900 USD) was obtained from maize + mash bean intercropping system with highest benefit cost ratio (2.03) during 2017 while PKR 149358 ha-1 (≈1003 USD) along with benefit cost ratio (2.15) during 2018. Overall, LER and ER results indicated that maize-grain legume intercropping systems were beneficial in terms of land resource utilization and economic returns. The maize-grain legume intercropping systems are more sustainable option for small land-holding farmers in Pakistan. © 2021 Friends Science Publishers

Plant Disease Identification Using the Unmanned Aerial Vehicle Images

Important factors in plant development and the elimination of both qualitative and quantitative declines in crop yield are early and effective identification and evaluation of plant illnesses. Optical techniques such as RGB imaging, multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence have shown their ability for early outbreak detection and quantification of plant diseases in automated, objective, and reproducible detection systems. Recently, as an optical inspection that offers additional information on crop plant vitality, 3D scanning has also been introduced. Different platforms from proximal to remote sensing are available for multiscale monitoring of single crop organs or entire fields. Accurate and accurate disease detection is enabled by highly sophisticated and advanced data analysis methods that lead to new sensor data insights for complex plant-pathogen systems.Non-destructive, sensor-based techniques help and extend the detection of plant diseases through visual and/or molecular approaches. Precision agriculture and plant phenotyping are the most important fields of use of sensor-based analyses. We suggest UAV base imaging for plant disease detection in this article.

Agro-economic efficiency in radish-arugula intercropping as a function of green manuring and population density

The vegetables intercropping practice triumph in family farming depends on several production factors, including the crops involved, green manuring and plant density of component crops. Therefore, the aim of this work was to assess whether there is agro-economic viability in the radish-arugula intercropping, in different equitable amounts of M. aegyptia and C. procera biomass (20, 35, 50 and 65 t ha-1 on base dry) and in diverse arugula population densities (40, 60, 80 and 100% of that recommended density for single crop - RDSC), combined with 100% of the RDSC for radish in two cropping years. The characteristics, the commercial productivity of radish roots and the arugula green mass yield were evaluated and in the intercropped system, the agronomic indices: land equivalent ratio (LER), intercropping advantage (IA), actual yield loss (AYL), productive efficiency index (PEI), score of the canonical variable (Z), and the economic indicators: gross income (GI), net income (NI), rate of return (RR) and profit margin (PM). The greatest agro-economic advantages of the radish-arugula intercropping were achieved with an LER of 1.64, PEI of 0.86, Z of 1.54, GI of R$ 45,543.92 ha-1, NI of R$ 24,662,31 ha-1, RR of R$ 2.20 for each real invested, and PM of 56.37%, respectively, in the combination of 65 t ha-1 of M. aegyptia and C. procera biomass and arugula population density of 100% of the RDSC, corresponding to the density of 1 million plants per hectare.

Emission factors and global warming potential as influenced by fertilizer management for the cultivation of rice under varied growing seasons

Greenhouse gas (GHG) emissions from paddy fields are intensified during rice production, and are generally reported based on single or double cropping. It needs to be known how emission factors, GHG emission patterns, and global warming potential are affected by growing three rice crops yearly under the principle of integrated plant nutrient system fertilization (IPNSF). Cowdung and vermicompost were used as IPNSF and compared with commercial fertilization alone. A static close chamber technique was used to measure GHG emissions. The peak periods of CH4 emissions were 20–37, 22–42, and 19–38 days after transplanting during dry, wet, and premonsoon seasons, respectively. The use of cow dung significantly enhanced total N2O, CH4, and CO2 fluxes by 5–10%, 15–23%, and 9–20%, respectively, compared to vermicompost. CH4 emissions for each kg grain production were 39–80, 45–63, and 43–57 gm in the dry, premonsoon, and wet seasons, respectively. Vermicompost significantly reduced CH4, CO2, and N2O fluxes by 13–19%, 17–21%, and 4–9%, respectively, along with a reduction in GHG emission factors by 8–17% and global warming potential by 13–17% compared to cow dung. Moreover, vermicompost and cow dung significantly improved rice grain yields in all three growing seasons compared to commercial fertilizer alone. In conclusion, the use of vermicompost as an IPNSF could be a viable technique for improving grain yield and for reducing GHG emissions from paddy fields during year-round rice cultivation.

Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters.

Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.

Prediction of crop coefficients from fraction of ground cover and height: Practical application to vegetable, field and fruit crops with focus on parameterization

The A&P approach, developed by Allen and Pereira (2009), estimates single and basal crop coefficients (Kc and Kcb) from the observed fraction of ground cover (fc) and crop height (h). The practical application of the A&P for several crops was reviewed and tested in a companion paper (Pereira et al., 2020). The current study further addresses the derivation of optimal values for A&P parameter values representing canopy transparency (ML) and stomatal adjustment (Fr), and tests the resulting model performance. Values reported in literature of ML and Fr were analysed. Optimal ML and Fr values were derived by a numerical search that minimized the differences between Kcb A&P with standard Kcb for vegetable, field, and fruit crops as tabulated by Pereira et al. (2021a, 2021b) and Rallo et al. (2021). Sources for fc were literature reviews supplemented by a remote sensing survey. Computed Kcb and Kc for mid- and end-season together with associated parameters values were tabulated. To improve the usability of the ML and Fr parameters a cross validation was performed, which consisted of the linear regression between Kcb computed by A&P and observed Kcb relative to independent data sets obtained from field observations. Results show that both series of Kcb match well, with regression coefficients very close to 1.0, coefficients of determination near 1.0, and root mean square errors (RMSE) of 0.06 for the annual crops and RMSE = 0.07 for the trees and vines. These errors represent less than 10% of most of the computed tabulated Kcb. The tabulated Fr and ML of this paper can be regarded as defaults to support A&P field practice when observations of fc and h are performed. Therefore, the A&P approach shows to be appropriate for use in irrigation scheduling and planning when fc and h are observed using ground and/or remote sensing, hence supporting irrigation water savings.

Smallholder farmer preferences for diversifying farming with cover crops of sustainable farm management: A discrete choice experiment in Northwest China

This study investigated smallholder farmers' adoption preferences for cropping patterns of cover crops, duration of adoption, and supplementing technical support to reduce agrochemicals regarding sustainable farm management (SFM). A discrete choice experiment was conducted in the Hexi Corridor (D1) and the west Loess Plateau (D2) in northwest China. The results show that farmers in D1 preferred intercropping and rotating cover crops to the current single cropping system, while farmers in D2 prioritized intercropping over rotation and least preferred planting cover crops in marginal land in the double-cropping system. Farmers in both sites preferred 1 or 3 years of adoption over 5 years. Additionally, farmers in D2 preferred technical assistance to reduce the high proportion of agrochemicals. The results indicate that younger farmers with higher annual household incomes were most likely to opt out in D1. In D2, more agricultural labor inputs and raising livestock had positive effects on the adoption of the package. Moreover, farmers who cultivated cash crops were more reluctant to move away from the current cropping system. This study highlights the uses of cover crop intercropping and rotation combining with technical assistance in different agriculturally intensive cropping systems that may be useful in guiding site-specific policy.

Successive double cropping of potato minitubers under insect proof net house for increased seed productivity

Non-availability of disease-free quality seed material is the most important factor affecting low productivity of potato in India. Successive cropping of disease free minitubers under net house conditions, in the north western plains of India, can be an important intervention for utilizing this expensive vector free space and prevailing potato growing climate of the region in the most sustainable way. A field study was undertaken during 2016-18 to evaluate this hypothesis for its productivity and economics over single crop conventionally taken in the region. Minitubers of six potato varieties were grown in single and successive cropping patters under net house in the region. The summed yield attributes of their early (DCE) and late planted (DCL) successive crops were compared to single cropping (SC) on per meter square basis. Corresponding observations on morphological traits and yield attributes were quite encouraging. Minituber yield of under size, seed size and combined was 1.99, 1.88 and 1.98 times higher in double crop as compared to conventional single crop. As a result, cost of production declined (2.52 times) and net profit increased (3.15 times) in double crop. Cost of production per tuber was reduced in this technique from ₹ 2.01-1.28 per minituber. Higher profits and productivity under successive planting are achievable and this innovative technology can be easily adopted by the stakeholders for improving multiplication of early generation planting material, which would translate into availability of increased seed material after stipulated multiplications.

FAO56 crop and water stress coefficients for cotton using subsurface drip irrigation in an arid US climate

Water shortages are a continuing reality for today’s irrigated agriculture especially in the arid US southwest where cotton (Gossypium hirsutum L.) is an important crop. Decreasing water availability in the region coupled with high cotton water requirements are inducing water conservation practices, including adoption of subsurface drip irrigation (SDI) and deficit irrigation management. A 3-year cotton study (2016, 2017, and 2018) was conducted on a sandy loam soil using SDI at Maricopa, Arizona, USA. The objectives were to determine the actual single (Kc act) and basal (Kcb act) crop coefficients, corresponding growth stage lengths, and water stress adjustment criterion for estimating crop evapotranspiration (ETc) by the widely used FAO56 crop coefficient procedures. Treatments evaluated were full irrigation (100%) and deficit irrigation (70%), both replicated three times and managed at an optimum N rate. Irrigation scheduling was based on a FAO56 Kcb curve locally developed in earlier studies at the Maricopa location using furrow irrigation. Actual ETc (ETc act) was determined by a soil water balance using 0–1.9-m soil profile water content measurements made every 6–14-days. The study also quantified the effects on ETc act and yield due to the deficit irrigation treatment. ETc act rates were high for the 100% treatment, often exceeding 12.0 mm d−1 during the mid-season stage in all years. Cumulative ETc act for the 100% treatment was 961, 1039, and 1008 mm; and lint yield was 1470, 1780, and 2200 kg ha−1 in 2016, 2017, and 2018, respectively. Treatment effects on both ETc act and cotton yield were significant in all years. Cumulative ETc act for the 70% treatment was 21–23% less than that for the 100% in each year. Lint yields for the 70% treatments were reduced 15–16% in two of the years, and by 36% in 2017 when soil water stress occurred earlier in the growing season. The 3-year average values of Kc act and Kcb act at mid-season were 1.31 and 1.26, respectively. Though higher than those previously reported for cotton at this location, these mid-season values are considered appropriate in this arid climate when using SDI and were consistent with the FAO56 mid-season standard cotton values after local climate adjustment. The observed average Kc act and Kcb act during the initial stage were ≈ 0.21 and they were 0.37 at the end of season, closely agreeing with the FAO56 tabulated cotton values for those stages. The average growth stage lengths determined for the SDI cotton are 33, 42, 51, and 28 for the initial, development, mid-season, and late season stages, respectively. The FAO56 water stress coefficient (Ks) was modeled for the 70% treatment using five different baseline values of depletion fraction for no stress (p) that varied from 0.60 to 0.70 in 0.025 increments. Best correlation coefficients (r) between the ETc act ratio of the 70% and 100% treatments with the modeled Ks of the 70% treatment were obtained with baseline p values of 0.60, 0.675, and 0.675 in 2016, 2017, and 2018, respectively. The three 3-year average baseline p value of 0.65, which is the same baseline p value for cotton in FAO56, is recommended for estimating ETc reduction due to water stress.

Evolution of soil cover and dry shoot phytomass accumulation of autumn/winter cover crops and its effect on the agronomic performance of soybean grown in succession

The cultivation of cover crops under no-tillage systems can promote positive improvements in soil attributes and promote increases in grain yield of successor crops, such as soybeans. The aim of this work was to evaluate the evolution of soil cover and accumulation of dry shoot phytomass of autumn/winter cover crops and their impact on the agronomic performance of soybean grown in succession. Black oats (Avena strigosa), rye (Secale cereale) and turnip (Raphanus sativus) were used as cover species in single crops, dual and triple intercropping, in addition to the fallow and weeded controls. The percentage of soil cover (%) and the accumulation of dry shoot phytomass were evaluated at 30, 45, 60, 75, 90 and 105 days after sowing (DAS). In soybean crop, plant height, number of productive nodes per plant, number of pods per plant, mass of a thousand grains and grain yield were evaluated. The cultivation of turnip single provided the fastest soil coverage, with 90.0% at 45 DAS. The intercropping between rye + turnip provided the largest accumulations of dry shoot phytomass, greater than 7.0 Mg ha-1 at 105 DAS. The highest soybean yields were obtained in succession to the cultivation of black oat and rye in single, yielding approximately 3,300 kg ha-1, 50% higher compared to fallow and weeded treatments.

Aphid honeydew may be the predominant sugar source for Aphidius parasitoids even in nectar-providing intercrops

The nectar provision hypothesis predicts that the introduction of nectar-producing plants in agroecosystems benefits parasitoid populations in the field and enhances biological control. Intercropping is a common crop diversification scheme that may bring complementary nectar sources for parasitoids and increase herbivore pest control. For instance, intercropping cereals with faba beans introduces nectar sources in usually sugar-devoid systems (i.e. cereal single crops). However, the nectar provision hypothesis has never been evaluated at the field scale in such intercropping systems. To test this hypothesis, we evaluated if sugar is a limiting factor for Aphidius parasitoids in single triticale crops and if their nectar feeding activity increases in faba bean-triticale intercrops. Aphidius feeding patterns were evaluated from their sugar profiles, using high-performance liquid chromatography (HPLC). In parallel, aphid density and parasitism rates were estimated at the edge and in the centre of single crops and intercrops. Sugar analyses revealed that honeydew was always the main sugar source for parasitoids, and although a significant proportion of parasitoid populations were recorded to feed on nectar, this proportion did not increase in intercrops. Besides, parasitism rates did not increase in intercrops, nor were aphid populations reduced. Thus, our results do not support the nectar provision hypothesis, but rather suggest that although nectar provision benefits parasitoid populations in some systems, its effects on biological control are highly context-dependent. They also confirm that honeydew can be a major food source for parasitoids, which may not necessarily be sugar limited at the field scale.

Comparative analysis of the economic returns to management in single crop, fish, poultry and an integrated farming system in Obudu, Cross River State

A four year (2016, 2017, 2018, and 2019) field experiment was conducted to compare the economic returns to investment in single enterprise with integrated farming comprising of rice farm alongside with fish culture and poultry farm using mostly organic waste as feeds. In 2016 a rice farm was cultivated on a piece of land already procured for the experiment, fertilizers, herbicides and pesticides were all applied. At maturity the rice was harvested, processed, bagged in 100kg bags and sold. Economic returns to management were calculated according to the methods of CIMMYT, (1988). In 2017, only fish farm was established, fed and harvested at maturity and sold to consumers. Economic returns to management were also calculated and recorded. In 2018, a poultry farm was established, 200 broilers were reared to maturity and sold, economic returns to management were calculated and recorded. Then in 2019, an integrated farm comprising rice farm, fish farm and poultry farm were all established in the same field. About 70% of the feeds used were organic waste collected mostly from poultry droppings. The silt from the pond was also used as fertilizer for the rice farm, while the rice bran was also fed to the poultry. The output from the farms were harvested and sold, and the economic returns to investment for each farm calculated. The result showed that the returns to investment were significantly (p<0.05) higher in integrated farming system than in the single enterprise farm, due to the low cost of production using low cost inputs in integrated farming compare to the use of high cost synthetic inputs in single enterprise. The paper therefore recommended that farmers should adopt integrated farming system with low cost inputs for higher profit than the single enterprise farming.