Single Crop Research Articles

Consorciação com Crotalaria spectabilis e Urochloa ruziziensis reduzem o desempenho agronômico de milho?

ABSTRACT Depending on the climate and soil conditions and management, the intercropping of maize with soil cover plants may reduce the cereal agronomic performance. This study aimed to assess the effects of intercropping with Urochloa ruziziensis and Crotalaria spectabilis on the maize agronomic performance and the straw production and quality after the harvest. Three treatments (single maize and maize intercropped with U. ruziziensis and with C. spectabilis) were distributed into a randomized blocks design. The growth and production components and the maize grain yield were evaluated in the three systems, as well as the straw production and nitrogen accumulation in each system. The intercropping systems did not decrease the maize yield and agronomic performance, if compared to the single crop, regardless of the evaluated species. The intercropping with C. spectabilis and U. ruziziensis presented a straw production 24 and 11 % higher, respectively, and the intercropping with C. spectabilis resulted in a straw nitrogen accumulation 39 % higher, when compared to the single maize crop.

Enhancing water use efficiency and water productivity of maize-onion cropping system

Maize is the third most important grain crop in India after rice and wheat with respect to area and productivity. Water is increasingly becoming a major limiting factor for agriculture and hence efficient utilization of available water resources is most important. Drip irrigation system has substantially high irrigation efficiency as compared to surface irrigation method. In order to increase the water use efficiency and water productivity in a single crop, system basis is more important. Present study was conducted during the year 2017-18 at Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu for enhancing the water use and water productivity under maize-onion cropping system. The experimental trial was laid out in randomized block design with four replications. Maize crop was raised during kharif season and onion crop during rabi season. Treatments imposed were drip fertigation @ 50, 75, 100 and 125 per cent pan evaporation and conventional method of irrigation. The results revealed that drip fertigation methods performed better than conventional method of irrigation in maize-onion cropping system. Drip fertigation at 125 per cent pan evaporation regime in maize and onion crops recorded higher grain and bulb yield with a water saving of 29.3 and 28.8 per cent respectively as compared to conventional irrigation method. In both the crops drip fertigation at 100 per cent pan evaporation registered higher water use efficiency and water productivity. Higher net return (Rs 2,48,937) and B-C ratio (3.52) were registered in drip fertigation at 125 per cent pan evaporation followed by drip fertigation at 100 per cent pan evaporation regime.

Diversity of Na+ allocation in salt-tolerant species of the genus Vigna.

Wild species in the genus Vigna are a great resource of tolerance to various stresses including salinity. We have previously screened the genetic resources of the genus Vigna and identified several accessions that have independently evolved salt tolerance. However, many aspects of such tolerance have remained unknown. Thus, we used autoradiography with radioactive sodium (22Na+) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to visualize and compare Na+ allocation in Vigna angularis (Willd.) Ohwi & H.Ohashi (azuki bean), Vigna nakashimae (Ohwi) Ohwi & H.Ohashi, Vigna riukiuensis (Ohwi) Ohwi & H.Ohashi, Vigna luteola (Jacq.) Benth. and Vigna marina (Burm.) Merr.. The results indicated: 1) Tolerant accessions suppress Na+ accumulation compared to azuki bean. 2) V. nakashimae and V. marina does so by accumulating higher amount of K+, whereas V. riukiuensis and V. luteola does so by other mechanisms. 3) V. luteola avoids salt-shedding by allocating excess Na+ to newly expanded leaves. As the mechanisms of the tolerant species were different, they could be piled up in a single crop via classical breeding or by genetic engineering or genome editing.

FADN data to support policymaking: The potential of an additional survey

This paper aims to highlight the potential of a FADN additional survey when payment for organic farming is to be calculated in the rural development decision-making process. In fact, the number of organic farms included in the FADN is often too low to provide consistent results. The analysis is based on a direct survey conducted on a larger number of farms than those included in the FADN continuous sample, considering the organic grape-growing farms. The estimate of the appropriate support payments (amount per hectare) is based on the gross margin methodology which allows additional costs and income foregone at micro-level to be highlighted. The method uses the partial balance sheet of a single crop processing to compare costs and revenues of organic and conventional grape-growing farms and considering both certification and transaction costs.

Inter-cropping of radish with turnip and spinach under drip irrigation to evaluate the growth and yield attributes

Failure of specific crops in the area due to different factors like, insect, pest attack, low production because of using low quality seeds, low market prices due to growing of single crop on large scale etc. are the major problems. This experiment was designed to introduce best intercropping pattern to increase production by efficient land utilization and cover single crop failure through other crops and evaluate the best crops to intercrop. The experiment was laid out in Randomized Complete Block Design (RCBD) with four replications and four treatments. Main crop radish was sown as a single crop in T1, in T2 radish was intercropped with turnip, in T3 radish was intercropped with Spinach, while in T4 radish was grown between turnip and spinach. Plant to plant and row to row distance of 12 and 30 cm was maintained in all treatments and replications.
 Under lab experiments lowest germination percentage of radish seeds were 91.5% in T1, while highest was 93.5% in T4. Plant height of radish was lower in T1 as compared to other treatments and it was 31.2 cm. Maximum plant height of 38.4 cm was observed in T2 where Radish was grown with turnip and spinach. Significant difference was observed between treatments in Single tuberous root length. Shortest length of 22.9 cm was noted in T2 and longest was 28.8 cm in T3. In single tuberous root weight, lowest root weight of single root was noted in T2 with total weight of 369.6 (g) and highest was 471.2 (g) in T3. Leaf area was also affected by intercropping and smallest leaf area was observed and noted in T2 while the largest was in T3 where radish was intercropped with spinach. Minimum yield per hectare (tons) was obtained from T2 (14.5 tons/ha) and maximum yield was noted in T3 (20.8 tons/ha). The present study revealed that intercropping of radish with spinach would be most suitable for higher production. These results shows the significance of intercropping. In almost all parameters and treatments T3 performed well where radish was intercropped with Spinach and it is due to different nutrient requirements of both crops. Being a leafy and tuberous crops fertilizer and nutrient requirement of both crops are different. With other tuberous crop results in all parameters were low which confirms that tuberous crops compete for nutrients.

Regionalizing crop types to enhance global ecosystem modeling of maize production

Improving the prediction of crop production is critical for strategy development associated with global food security, particularly as the climate continues to change. Process-based ecosystem models are increasingly used for simulating global agricultural production. However, such simulations often use a single crop variety in global assessments, implying that major crops are identical across all regions of the world. To address this limitation, we applied a Bayesian approach to calibrate regional types of maize (Zea mays L), capturing the aggregated traits of local varieties, for DayCent ecosystem model simulations, using global crop production data from 2001 to 2013. We selected major cropping regions from the FAO Global Agro-Environmental Stratification as a basis for the regionalization and identified the most important model parameters through a global sensitivity analysis. We calibrated DayCent using the sampling importance resampling algorithm and found significant improvement in DayCent simulations of maize yields with the calibrated regional varieties. Compared to a single type of maize for the world, the regionalization of maize leads to reductions in root mean squared error of 11%, 31%, 27%, 30%, 19%, and 27% and reductions in bias of 59%, 59%, 50%, 81%, 32%, and 56% for Africa, East Asia, Europe, North America, South America, and South and Southeast Asia, respectively. We also found the optimum parameter values of radiation use efficiency are positively correlated with the income level of different regions, which indicates that breeding has enhanced the photosynthetic efficiency of maize in developed countries. There may also be opportunities for expanding crop breeding programs in developing countries to enhance photosynthesis efficiency and reduce the yield gap in these regions. This study highlights the importance of representing regional variation in crop types for achieving accurate predictions of crop yields.

Trends and Climate Response in the Phenology of Crops in Northeast China

Crop phenology is the process of crop growth and yield formation, which is largely driven by climatic conditions. It is vital to investigate the shifts in crop phenological processes in response to climate variability. Previous studies often only explored the response of a single crop phenology to climate change, and lacked comparative studies on the climate response in different crop phenology. We intend to investigate the trends in phenological change of three typical crops (i.e., maize, rice and soybean) in Northeast China (NEC) and their response to climate change during 1981–2010. Its main purpose is to reveal the differences in the sensitivity of different crop phenology to key climate factors [e.g., mean temperature (T), accumulated precipitation (AP) and accumulated sunshine hours (AS) during the crop growth period]. We found that the three crops have different phenological changes and varying ranges, and significant spatial heterogeneity in phenological changes. The results indicated that the lengths of different crop growth stages [e.g., the vegetative growth period (VGP), the reproductive growth period (RGP) and the whole growth period (WGP)] were negatively correlated with T, especially in VGP and WGP. However, the lengths of growth period of the three crops were positively correlated with AP and AS. For each 1°C increase in T, the number of days shortened in WGP (about 5 days) was the largest, and that in RGP (less than 2 days) was the smallest. Therefore, the increases in T during past 3 decades have significantly shortened VGP and WGP of three crops, but had slight and inconsistent effects on RGP. Moreover, changes in AP has slight impact on the growth periods of maize and rice, and significantly shortened RGP and WGP of soybean. Changes in AS exerted important and inconsistent effects on the phenology of three crops. This study indicated that there are significant differences in the sensitivity and response of different crop phenology to climate factors. Therefore, in evaluating the response and adaptation of crops to climate change, comparison and comprehensive analysis of multiple crops are helpful to deeply understand the impact of climate change on crop production.

Functional crop types are more important than diversity for the productivity, profit and risk of crop sequences in the inner Argentinean Pampas

CONTEXTOver the last 250 years, many natural ecosystems in South America, including the Inner Pampas of Argentina, evolved into extremely simplified agroecosystems. In the last 25 years, simplified soybean-based crop sequences have become widespread. Putative drivers of the wide adoption of this system include high soybean grain price, simplified agronomic management and low cost, but comparison of production, profit and risk in current and alternative crop sequences are lacking.OBJECTIVEOur aim was to compare the production, profit, and risk of seven crop sequences varying in their diversity, cropping intensity and the proportion of functional crop types.METHODSWe modelled seven typical or feasible crop sequences for two locations with different rainfall and soil water holding capacity for a 50-yr period. Crop sequences included wheat (W) or fallow during winter and soybean (S) or maize (M) as single and double crops during summer, and were characterized with indexes of agrobiodiversity (ABDI, from 0.33 to 1.00), cropping intensity (from 0.40 to 0.83) and soybean proportion (SP, from 0 to 1.00).RESULTS AND CONCLUSIONSW/M-M had the highest and S-S the lowest yield in both locations. The high yielding crop sequences had a lower stability of grain yield that those with a high proportion of soybean (S-S and W/S-S). Yield stability and CVaR (conditional value-at-risk) for gross margin and gross margin ratio were unrelated with ABDI. The prevalent, least diverse soybean monoculture (S-S) had the lowest cost and medium profit-risk metrics. Increasing the proportion of maize contributed to higher gross margin and gross margin ratio with a lower risk in some crop sequences. Crop sequences with high proportion of soybean (S-S and W/S-S) had low mean cost with a close to average CVaR. However, these were not the sequences with the highest gross margin or gross margin ratio. Our results demonstrated that functional crop types (cereal and legumes) were more important than diversity and cropping intensity for the profit and risk of crop sequences in the Inner Argentinean Pampas.SIGNIFICANCEEconomic risk-benefit analysis of crop sequences is necessary but not sufficient to understanding the drivers of grower's choices, which are greatly influenced by context regarding policies for grower support, taxes, and land ownership.

Profitability of various cropping patterns among arable crop farmers in Niger State, Nigeria

Descriptive Statistics and Net Farm Income model was used to analyze data collected from 120 Arable Farmers who adopted various cropping patterns in Niger State, Nigeria. The study specifically examined the socio-economic characteristics of arable farmers, profile the cropping patterns adopted, examined the profitability and highlighted the constraints to crop production among arable farmers in the study area. Results obtained from the study show that crop farming in the area is a male dominated. The mean age of farmers was 33years, 98.3% were married, 80.8% had one form of education or the other and 68.4% adopted a three-crop mix pattern in their crop production. Two and three crop mixes enterprise were profitable than sole cropping when gross income per ha was used as an index of profitability. Profitability was higher in single crop enterprise when returns/man day was used as an index but was higher in a two and three crop mix enterprise when net returns per ha was used as a measure of profitability. Bad roads, drought, theft of farm produce, poor extension per farm advisory services and lack of credit facilities respectively were the constraint to crop production. The study concludes that mixed cropping enterprises was more profitable than sole cropping. We recommend the promotion of mixed cropping among arable farmers for increased profitability and income to farm households, that the constraints identified be addressed by all concerned authorities so as to sustain crop production, reduce food insecurity and eradicate hunger and poverty among arable farmers in the area and Nigeria as a whole.

Productivity of spring vetch plants in single and mixed crops depending on meteorological conditions

Productivity of spring vetch plants in single and mixed crops depending on meteorological conditions

A CONUS-Wide Standardized Precipitation–Evapotranspiration Index for Major U.S. Row Crops

Abstract Agricultural droughts afflicting the contiguous United States (CONUS) are serious and costly natural hazards. Widespread damage to a single cash crop may be crippling to rural communities that produce it. While drought is insidious in nature, drought indices derived from meteorological data and drought impact reports both provide essential guidance to decision-makers about the location and intensity of developing and ongoing droughts. However, response to dry meteorological conditions is not consistent from one crop type to the next, making crop-specific drought appraisal difficult using weather data alone. Additionally, drought impact reports are often subjective, latent, or both. To rectify this, we developed drought indices using meteorological data, and phenological information for the row crops most commonly grown over CONUS: corn, soybeans, and winter wheat. These are referred to as crop-specific standardized precipitation–evapotranspiration indices (CSPEIs). CSPEIs correlate more closely with end-of-season yields than traditional meteorological indicators for the eastern two thirds of CONUS for corn, and offer an advantage in predicting winter wheat yields for the High Plains. CSPEIs do not always explain a higher fraction of variance than traditional meteorological indicators. In such cases, results provide insight on which meteorological indicators to use to most effectively supplement impacts information.

An Empirical Analysis of Profitability of Rice Cultivation In Kuttanad Wetland System, Southern Kerala

This paper aims to assess the profitability of rice cultivation and its determining factors in Kuttanad Wetland System situated in the southern part of Kerala. An analysis of the data collected from a sample of 172 farmers in the region revealed that the cost of rice cultivation in the region was higher, but the productivity was lower. As a result, the average per-acre revenue earned by farmers was just enough to cover the cost of cultivation without leasing and some normal profit. Although majority of farmers earned a profit, an assessment of the profitability of rice cultivation showed that per acre annual profit was only Rs. 15,205 due to single cropping, higher cost of production, lower productivity and absence of proper institutional intervention. The econometric analysis of the determinants of profitability reveals that land size, machinery cost, pesticide cost and age of farmer have a significant positive impact on profitability. In contrast, leased-in land, labour and fertiliser costs have a significant negative impact. The study suggests strengthening institutional support to farmers to promote rice cultivation in the region

Beet-arugula intercropping under green manuring and planting density induce to agro-economic advantages

ABSTRACT One of the biggest challenges in the intercropping system of two crops is to obtain the optimal dose of green manure and the adequate population density of the crops. So, the objective of this work was to evaluate the performance of beet and arugula intercropping, influenced by green manuring with Merremia aegyptia and Calotropis procera and arugula population densities in two cultivation years, in semi-arid environment. The experimental design used was in randomized complete blocks, with the treatments arranged in a 4 x 4 factorial scheme, with 4 repetitions. The first factor of this scheme consisted of equitable amounts of M. aegyptia and C. procera biomass (20, 35, 50 and 65 t ha-1 on a dry basis) and the second factor, by arugula population densities (40, 60, 80 and 100% of the recommended density for single cropping, corresponding to 400, 600, 800 and 1,000 thousand arugula plants ha-1). The production and its components were evaluated on beet and arugula. In addition to these characteristics, the following agro-economic indicators were also determined for each treatment: system productivity index (SPI), land equivalent coefficient (LEC) and monetary equivalent ratio (MER). The greatest agro-economic advantages of the beet with arugula intercropping were achieved with a system productivity index (SPI) of 53.47 t ha-1, land equivalent coefficient (LEC) of 0.84 and a monetary equivalent ratio (MER) of 1.56, respectively, combining 65 t ha-1 of M. aegyptia and C. procera biomass with the arugula population density of 1,000 thousand plants ha-1. The maximum optimized commercial productivity of beetroots in the system intercropped with arugula was 23.20 t ha-1 using 65 t ha-1 of M. aegyptia and C. procera and in the arugula population density of 1,000 thousand plants ha-1, while the maximum arugula productivity intercropped with beet was 9.65 t ha-1, in the same combination of green manures amount and arugula population density.

Crop coefficient estimated by degree-days for ‘Marandu’ palisadegrass and mixed forage

Considering profitability in pasture-based systems, investigating parameters affecting crop coefficients for irrigation management becomes important. In this experiment, we determined the crop coefficient of ‘Marandu’ palisadegrass based on accumulated degree-days and estimated plant water consumption under single (‘Marandu’ alisadegrass) and mixed (‘Marandu’ palisadegrass + black oats + Italian ryegrass) cropping regimes. The research was conducted at the Luiz de Queiroz College of Agriculture in Piracicaba, São Paulo, Brazil, between 2016 and 2017. Evapotranspiration was assessed using weighing lysimeters while crop evapotranspiration was calculated using mean weight variation. Reference evapotranspiration and degree-days were estimated. Data were obtained from an automated weather station. Equations and regression models relating crop coefficient with accumulated degree-days were generated for two seasons (spring/summer and autumn/winter) and evaluated for two year-cycles, from 2015 to 2018. The results showed better prediction accuracy for the single cropping system in spring/summer 2017–18. Highlights The model for determinating crop coefficient (Kc) by accumulated degree-days showed efficient for use in determination local. The use of the degree days for determination Kc is more vantage because only necessary to measure the air temperature (maximum and minimum). The equations for determining crop coefficient by accumulated degree days for the spring/summer season in Marandu palisade grass cultive show more precision that in mixture forage system.

Season-specific management strategies for rainfed soybean in the South American Pampas based on a seasonal precipitation forecast

CONTEXTGlobal climate change is resulting in more frequent and more damaging extreme events affecting the performance of production systems. It is imperative to develop good season-specific crop management recommendations to help farmers to improve their adaptive capacity to a changing climate one season at a time. OBJECTIVEWe aimed to evaluate the skill of the International Research Institute for Climate and Society (IRI) seasonal precipitation forecasts and the interaction between the forecasted seasonal precipitation scenarios and management practices for rainfed soybean cropping systems using a crop simulation model. METHODSWe used a crop simulation model (CROPGRO-Soybean) coupled with weather data to assess the potential use of the IRI seasonal precipitation forecasts as a tool to optimize season-specific management strategies for rainfed soybean in Uruguay. We used a total of 620–668 IRI seasonal precipitation forecasts released from 2003 to 2016 for each of the five weather stations located in the main soybean producing area. The analysis was performed for two soybean cropping systems (i.e., sown as a single crop or as double-cropped soybean), for which we considered combinations of sowing dates and maturity groups (11 sowing dates × 3 maturity groups combinations for each soybean cropping system). RESULTS AND CONCLUSIONSThe IRI seasonal precipitation forecasts were able to successfully forecast below-normal precipitation scenarios in 77% of the total predictions developed for this scenario considering all weather stations during the study period (2003–2016), while it was less accurate in forecasting above-normal precipitation scenarios (60% of success). We found that earlier sowing dates were a better strategy for years when an above-normal precipitation forecast was released for the December–January-February period (4.7 Mg ha−1 average seed yield). In contrast, delayed sowing dates were more appropriate for below-normal precipitation forecasts (3.7 Mg ha−1 average seed yield). Applying season-specific management practices farmers could potentially increase their soybean yields by up to 0.6 and 1.6 Mg ha−1, in years with below- or above-normal forecasted precipitations, respectively. The benefit of season-specific management will depend on the interaction among all management practices, the effective capacity of farmers to implement it, and the risk profile the farmer adopts and it is exposed to. SIGNIFICANCEHere we built a novel approach to assess the impact of considering seasonal precipitation forecasts for optimizing crop production. This assessment provided insights on how farmers can use seasonal precipitation forecasts to optimize rainfed soybean yield for a specific cropping season.

An intelligent system for crop identification and classification from UAV images using conjugated dense convolutional neural network

Crop identification and classification is an important aspect for modern agricultural sector. With development of unmanned aerial vehicle (UAV) systems, crop identification from RGB images is experiencing a paradigm shift from conventional image processing techniques to deep learning strategies because of successful breakthrough in convolutional neural networks (CNNs). UAV images are quite trustworthy to identify different crops due to its higher spatial resolution. For precision agriculture crop identification is the primal criteria. Identifying a specific type of crop in a land is essential for performing proper farming and that also helps to estimate the net yield production of a particular crop. Previous works are limited to identify a single crop from the RGB images captured by UAVs and have not explored the chance of multi-crop classification by implementing deep learning techniques. Multi crop identification tool is highly needed as designing separate tool for each type of crop is a cumbersome job, but if a tool can successfully differentiate multiple crops then that will be helpful for the agro experts. In contrast with the previous existing techniques, this article elucidates a new conjugated dense CNN (CD-CNN) architecture with a new activation function named SL-ReLU for intelligent classification of multiple crops from RGB images captured by UAV. CD-CNN integrates data fusion and feature map extraction in conjunction with classification process. Initially a dense block architecture is proposed with a new activation function, called SL-ReLU, associated with the convolution operation to mitigate the chance of unbounded convolved output and gradient explosion. Dense block architecture concatenates all the previous layer features for determining the new features. This reduces the chance of losing important features due to deepening of the CNN module. Later, two dense blocks are conjugated with the help of a conversion block for obtaining better performance. Unlike traditional CNN, CD-CNN omits the use of fully connected layer and that reduces the chance of feature loss due to random weight initialization. The proposed CD-CNN achieves a strong distinguishing capability from several classes of crops. Raw UAV images of five different crops are captured from different parts of India and then small candidate crop regions are extracted from the raw images with the help of Arc GIS 10.3.1 software and then the candidate regions are fed to CD-CNN for proper training purpose. Experimental results show that the proposed module can achieve an accuracy of 96.2% for the concerned data. Further, superiority of the proposed network is established after comparing with other machine learning techniques viz. RF-200 and SVM, and standard CNN architectures viz. AlexNet, VGG-16, VGG-19 and ResNet-50.

Ploughing the Field: YA in Translation

In lieu of an abstract: In agricultural terminology, ‘monoculture’ describes the cultivation of a single crop, to the exclusion of others. In YA, fictional monoculturalism can lead, at best, to monotonous stories in which the same (white, able, cisgender, heterosexual, and middle class) characters go on the same adventures. At worst, it can work to reinforce the monolithic social structures embedded in dominant Western culture. YA in translation has the powerful ability to disrupt any tendency of YA to tell a “single story” (Adichie) by expanding and diversifying the narratives that are available. As B.J. Epstein and Elizabeth L. Chapman suggest in <em>International LGBTQ+ Literature for Children and Young Adults</em>, “translations will help to create a change in society by importing new ideas” (5). Scholarship interrogating the function and practice of children’s and YA literature in translation has expanded over the past few years.1 In this edition of Ploughing the Field, we further investigate the development of YA in translation, specifically, by bringing together the perspectives of five academics and practitioners in an edited transcript of a roundtable discussion held at the YA Studies Association’s 2020 conference.

Onion-forage cropping systems on a Vertic Argiudoll in Uruguay: Onion yield and soil organic matter, aggregation, porosity and permeability

Hostipastoral systems in small farming units that integrate horticulture crops, as main cash crop, with sheep/cattle raising on cultivated forage, in Uruguay, present physical restrains to crops because of high consistency and compaction of the vertic soils and unique tillage requirements. The objective of this study was to evaluate the effect of different hortipastoral systems on soil organic matter, aggregation, porosity, permeability and onion yield during a cropping year in onion-forage hortipastoral systems. A long-term experiment was installed in 1995 in the National Institute for Agricultural Research (INIA-Las Brujas) in Uruguay, on fine (mixed) smectitic thermic superactive Vertic Argiudolls. The soil was previously under native grasslands, and treatments consisted of cropping systems for onion (Allium cepa L.) production, maintained for three years and then rotated. Onion yield was evaluated during the period 1998–2006, and in the cropping year 2006–2007, corresponding to the end of a 3-year cropping cycle, we evaluated soil organic matter and physical properties and functioning in the cropping systems: alfalfa, fescue, forage consortium (birdfoot trefoil, alfalfa and white clover), onion after alfalfa, onion after fescue, and onion after consortium. At four times during the cropping year, aggregate stability and organic matter were determined for the 0–0.05 m soil layer, whereas soil bulk density, porosity, and hydraulic conductivity were determined in 0–0.10 and 0.10–0.20 m layers. The results showed high variability in onion yields from 1998 to 2006 (10.8–36.4Mgha−1), mainly related to high rainfall (higher than 150 mm) that affected crop planting, and the low rainfall (lower than 30 mm) that affected growth and development. In two out of eight years, there were significant differences in onion yield among treatments, where alfalfa (1999) and fescue (2006) crops are more beneficial to onion yield than pasture consortium. To establish the onion crop in the vertic soil tillage is needed, but tilling the soil under forage crops reduces macro and increases microaggregation (reduces GMDw from 1.8 to 0.8 mm), increases macroporosity (close 0.07 to 0.25 m3 m−3) and functioning in terms of water and air permeability (Ka increase above 3 µm2 after tillage) and decreases the degree of compaction (under forages decreased DC from 69-84% to 58–60% in surface layer). In conclusion, there is a significant potential of hortipastoral systems and proper management of soil organic matter to minimize adverse effects of climate, and improve productivity stability of intensive production systems. Single forage crops, either legume or grass, are more beneficial to onion yield than consortium of grass plus legumes.

Memory-guided foraging and landscape design interact to determine ecosystem services

Many studies examine how the landscape affects memory-informed movement patterns, but very few examine how memory-informed foragers influence the landscape. This reverse relationship is an important factor in preventing the continued decline of many ecosystem services. We investigate this question in the context of crop pollination services by wild bees, a critical ecosystem service that is in steep decline. Many studies suggest that adding wild flower patches near crops can result in higher crop pollination services, but specific advice pertaining to the optimal location and density of these wild flower patches is lacking, as well as any estimate of the expected change in crop pollination services. In this work, we seek to understand what is the optimal placement of a flower patch relative to a single crop field, during crop bloom and considering spatial factors alone. We develop an individual based model of memory-based foraging by bumble bees to simulate bee movement from a single nest while the crop is in bloom, and measure the resulting crop pollination services. We consider a single crop field enhanced with a wild flower patch in a variable location, and measure crop flower visitation over the course of a single day. We analyze the pollination intensity and spatial distribution of flower visits to determine optimal wild flower patch placement for an isolated crop field. We find that the spatial arrangement of crop and wild flower patch have a significant effect on the number of crop flower visits, and that these effects arise from the memory-informed foraging pattern. The most effective planting locations are either in the centre of the crop field or on the far side of the crop field, away from the single bumble bee nest.

Analysis of crop water requirements and irrigation demands for rice: Implications for increasing effective rainfall

Determining the irrigation demands of rice in a humid region is essential for water conservation through improving rainfall utilization in southern China. In this paper, six representative rice areas in southern China were selected, and the daily reference evapotranspiration (ETo) of the representative stations during 1953–2017 was calculated using the Penman-Monteith method. The daily crop evapotranspiration (ETc) was computed using the single crop coefficient method, and the irrigation scheduling under flood irrigation (FI) was obtained by establishing a field water balance model. The characteristics and trends of the irrigation demand of early, middle and late rice, as well as the irrigation changes under a “double-to-single” cropping pattern, were also analyzed with advanced statistical tests. The results indicated that most of the abundant rainfall was concentrated in the growth period of early rice in southern China. Early rice required less water (135.0 mm) and even no irrigation in some wet years, while middle and late rice required more irrigation water (288.1 mm and 265.2 mm, respectively). The key reasons for the differences in irrigation demand among the three rice types are the amount and distribution characteristics of rainfall during the rice growth period. In addition, the irrigation demands of rice showed a downward trend as a response to a decrease in ETc and the increase in rainfall in the past 60 years, where rainfall was the dominant factor. With this increasing trend of rainfall and its better and efficient utilization in the future, rice irrigation demands may be further reduced or completely stopped. Under the “double-to-single” cropping pattern, irrigation demand decreased by 16% on average, and the irrigation frequency, as well as the drainage volume burden, were simultaneously reduced.