Yield-reducing Factors Research Articles

RTAL: An edge computing method for real-time rice lodging assessment

Rice is a globally important crop that plays an important role in feeding more and more of the world's population as we cope with climate change and population growth. Rice lodging is one of the main yield-reducing factors in rice production, which also has a direct impact on rice quality and leads to harvest difficulties, so it is very important to obtain lodging data in time. Lodging assessment is a tedious task that generally requires a lot of time and labor due to the large area of land involved. This study proposes an edge computing method for rice lodging areas suitable for unmanned aerial vehicles (UAVs) without post-processing, called Real Time Rice Lodging Area Calculation Method (RTAL). The RTAL method is based on deep learning and photogrammetry, and can calculate large-scale farmland lodging areas in real time using only edge computing devices. Tested on the edge computing device Nvidia Jetson Xavier NX, when the ground resolution is 0.1 m, the fastest prediction speed can reach 14417.9 m2/s. When the operation time of a single sortie is 80 min, the predicted area can reach 10 km2. This method greatly improves the efficiency of prediction, and can provide real-time help in rice yield measurement or disaster damage assessment.

Adverse Weather Impacts on Winter Wheat, Maize and Potato Yield Gaps in northern Belgium

Adverse weather conditions greatly reduce crop yields, leading to economic losses and lower food availability. The characterization of adverse weather and the quantification of their potential impact on arable farming is necessary to advise farmers on feasible and effective adaptation strategies and to support decision making in the agriculture sector. This research aims to analyze the impact of adverse weather on the yield of winter wheat, grain maize and late potato using a yield gap approach. A time-series analysis was performed to identify the relationship between (agro-)meteorological indicators and crop yields and yield gaps in Flanders (northern Belgium) based on 10 years of field trial and weather data. Indicators were calculated for different crop growth stages and multiple soils. Indicators related to high temperature, water deficit and water excess were analyzed, as the occurrence frequency and intensity of these weather events will most likely increase by 2030–2050. The concept of “yield gap” was used to analyze the effects of adverse weather in relation to other yield-reducing factors such as suboptimal management practices. Winter wheat preferred higher temperatures during grain filling and was negatively affected by wet conditions throughout the growing season. Maize was especially vulnerable to drought throughout the growing season. Potato was more affected by heat and drought stress during tuber bulking and by waterlogging during the early growth stages. The impact of adverse weather on crop yield was influenced by soil type, and optimal management practices mitigated the impact of adverse weather.

Understanding factors influencing wheat productivity in Ethiopian highlands

Summary Increasing yields in farmer fields is a priority to address increasing food demands. The study was conducted within four wheat-growing areas in Ethiopia: Debre Birhan, Hosaina, Sinana and Maychew. The objectives were to identify (1) best-bet soil fertility management options based on agronomic performance and economic evaluation and (2) key yield-reducing factors in farmer fields based on an agronomic survey among 55 participating farmers. Two types of on-farm experiments were conducted: researcher-managed trials that tested combinations of nutrients, including micronutrients, organic resources or both over two cropping seasons and farmer-managed trials comparing ‘improved practice’ against ‘farmer’s practice’. Fertilizer treatment affected wheat productivity in Debre Birhan (p < 0.01), a site limited in sulphur. Here, full NPK increased yields over the control (p < 0.05), whereas a combination of NPK and manure was better than the application of manure as the only source of added nutrients (p < 0.05). Applying half the recommended NPK with micronutrients and manure achieved similar yields as the full fertilizer treatment. In Hosaina, treatment had no significant effect on wheat productivity, although a combination of NPK and zinc resulted in an additional 26–57% yield relative to the other treatments. In Maychew, a significant treatment effect (p < 0.05) was observed. Here, the treatment with lower rates of nitrogen and phosphorous had lower yields than the full NPK treatment. A significant effect of plant densities on on-farm productivity was also observed. We conclude that although nutrient management including use of micronutrients is important in specific cases, investments to optimize plant densities have a huge potential to increase food productivity.

Copper stress alleviation in corn (Zea mays L.): Comparative efficiency of carbon nanotubes and carbon nanoparticles

Copper (Cu) stress is one of the predominant crop yield-reducing factors in agriculture. Application of carbon nanomaterials (CNMs) could have promotive effects on crop growth; however, their effects on alleviation of Cu stress for plants have rarely been documented. In this study, we investigated the comparative role of carbon nanotubes (CNTs) and carbon nanoparticles (CNPs) in corn (Zea mays) seed germination, seedling growth as well as Cu stress alleviation. The results showed that CNTs and CNPs stimulated corn seed germination by significantly increasing germination rate (GR), shortening the mean germination time (MGT), and increasing overall germination index (GI). They also significantly elongated seedling length and increased fresh biomass with optimal application rates ranging from 50 to 100mgL-1. Principle component analysis (PCA) confirmed that seed germination indexes and seedling growth were positively affected by CNTs or CNPs, but inversely influenced by high levels of Cu stress (> 20mgL-1). Furthermore, higher Cu accumulation and anti-oxidative enzyme activity (SOD, POD, CAT) were observed in plants co-exposed to Cu2+ and either CNTs or CNPs compared to plants exposed to Cu2+ alone. CNPs had stronger improvement on plant growth and Cu stress alleviation than CNTs, which suggest they may be cost-effective agriculture amendments to improve plant growth under heavy metal stress.

Dilemmas between barley cultivars under salinity stress

Salinity is one of reasons causing the greatest yield-reducing factors leads various physiological and biochemical effects. Recently, long non-coding RNAs (lncRNAs) became prominent as crucial regulators in various biological processes, including developmental processes and stress responses such as salinity. In this study, physiological responses of four Hordeum vulgare L. cvs genotypes (Beyşehir 99, Hasat, Konevi 98 and Tarm 92) were assessed for 150 mM salinity treatment for 3 days germination period, maize (CNT0018772) and rice (CNT0031477) lncRNAs localizations in barley chromosomes via fluorescence in situ hybridization (FISH). Physiological responses of barley varieties, including root and shoot heights, fresh and dry weights, water content (WC) and protein content were affected differently by 150 mM salinity stress. The germination percentage of Beyşehir 99 and Tarm 92 reduced while Konevi 98 was slightly increased. However, proline content changed only in Beyşehir 99. According to FISH results, the localizations of CNT0018772 and CNT0031477 were revealed up 6 and 8 signals, respectively. This study may contribute new insight into lncRNAs functions underlying the salinity tolerance mechanisms in different barley varieties.

WHEAT LODGING ASSESSMENT USING MULTISPECTRAL UAV DATA

Abstract. Lodging is a major yield-reducing factors in wheat, causing reductions up to 80%. Timely detection of lodging can reduce its impacts and support proper decisions regarding expected yield, crop price or its insurance. Since the incidence of lodging is heterogeneous within a field, very high-resolution remote sensing data can be viable for accurate and prompt spatio-temporal assessment of lodging severity. As such unmanned aerial vehicles (UAVs) provide a versatile and cost-effective solution to monitor crops on a small scale with sub-centimetre spatial resolution. In this study, we analysed the spectral variability between different grades of lodging severity (non-lodged (NL), moderate (ML), severe (SL) and very severe (VSL)) and classified them using high-resolution UAV data. Multispectral orthomosaic UAV images with 5cm resolution and nine bands (covering the VIS-NIR spectrum with Sentinel-2 filters) were acquired in May 2018 for two wheat fields in Bonifiche Ferraresi farm, Jolanda di Savoia, Italy. Concurrent to the time of image acquisition, a field campaign was carried out in which crop characteristics and lodging related parameters were collected. The results showed that reflectance magnitude increased with lodging severity and demonstrated that the red-edge and NIR bands can be used to clearly discriminate between NL and lodged (all grades) wheat and to some extent between different lodging classes (ML, SL and VSL). The nearest neighbourhood classification performed using an object-based segmentation yielded optimal results with an overall accuracy of 90%, thus demonstrating the use of multispectral UAV data as a promising tool for wheat lodging assessment.

Yield gaps in oil palm: A quantitative review of contributing factors

Abstract Oil palm, currently the world’s main vegetable oil crop, is characterised by a large productivity and a long life span (≥25 years). Peak oil yields of 12 t ha−1 yr−1 have been achieved in small plantations, and maximum theoretical yields as calculated with simulation models are 18.5 t oil ha−1 yr−1, yet average productivity worldwide has stagnated around 3 t oil ha−1 yr−1. Considering the threat of expansion into valuable rainforests, it is important that the factors underlying these existing yield gaps are understood and, where feasible, addressed. In this review, we present an overview of the available data on yield-determining, yield-limiting, and yield-reducing factors in oil palm; the effects of these factors on yield, as measured in case studies or calculated using computer models; and the underlying plant-physiological mechanisms. We distinguish four production levels: the potential, water-limited, nutrient-limited, and the actual yield. The potential yield over a plantation lifetime is determined by incoming photosynthetically active radiation (PAR), temperature, atmospheric CO2 concentration and planting material, assuming optimum plantation establishment, planting density (120–150 palms per hectares), canopy management (30–60 leaves depending on palm age), pollination, and harvesting. Water-limited yields in environments with water deficits >400 mm year−1 can be less than one-third of the potential yield, depending on additional factors such as temperature, wind speed, soil texture, and soil depth. Nutrient-limited yields of less than 50% of the potential yield have been recorded when nitrogen or potassium were not applied. Actual yields are influenced by yield-reducing factors such as unsuitable ground vegetation, pests, and diseases, and may be close to zero in case of severe infestations. Smallholders face particular constraints such as the use of counterfeit seed and insufficient fertiliser application. Closing yield gaps in existing plantations could increase global production by 15–20 Mt oil yr−1, which would limit the drive for further area expansion at a global scale. To increase yields in existing and future plantations in a sustainable way, all production factors mentioned need to be understood and addressed.

Delicate balance between pest and disease injuries, yield performance, and other ecosystem services in the complex coffee-based systems of Costa Rica

Pests and diseases are the main yield-reducing factors in simplified agricultural systems. Their role in complex, diverse, agricultural systems may, however, be less apparent because of the many interactions in which they are involved. However, it is essential to understand the relationships between pests and diseases, on the one hand, and ecosystem services (including crop production), on the other, to develop sustainable agroecosystems.Our study aims to illustrate these complex relationships based on the example of coffee agroecosystems in Costa Rica. We analysed a dataset consisting of 107 coffee plots characterized for their topoclimates, soils, coffee plant production characteristics, cropping practices, and pest and disease injuries. Meta-variables were created through cluster analyses to account for these different broad attributes of coffee-based agroecosystems. In particular, coffee injury profiles were determined on the basis of injury levels incurred by pathogens, nematodes, and insects over the course of one growing season. We used correspondence analysis to assess the levels of linkage between injury profiles and other agroecosystem meta-variables. Indicators of biodiversity based on shade diversity and of attainable yield were incorporated in the analysis as additional variables. Four groups of coffee-based agrosystems were identified, ranging from extensive (low-input, perennial polyculture) to intensive (unshaded high-input monoculture). Each group of coffee-based agroecosystem corresponds to varying levels of pest and disease injuries, crop yield, and ecosystem service provision, excluding coffee production. In each group of coffee-based agrosystem, we discussed the drivers of coffee production and explored potential avenues to improve sustainability based on ecosystem services provision. We highlighted that the physical characteristics of the environment, topoclimate and soil characteristics, are the main drivers of injury profiles and of resulting yield losses. Cropping practices and pest and disease management first need to be adapted to these physical characteristics. Where topoclimate and soils favour pest and disease development, potentially leading to heavy yield losses, system diversification can enhance ecosystem service provision, including production of other crops, thus helping to offset low coffee production. Where physical environmental characteristics hamper pests and diseases, increasing ecosystem service provision by incorporating shade trees may be considered, provided that coffee production is not significantly reduced. We conclude that there is an acute need to quantify losses caused by pests and diseases in agroforestry systems, in order to provide a rational basis for growers’ decisions and to better determine the value of economic incentives needed for ecosystem service provision.

Leading farmers in South East Australia have closed the exploitable wheat yield gap: Prospects for further improvement

Australian dry-land crop producers farm in regions with highly variable climate and soils. Farmers have responded to the pressures of rising costs by pioneering and adapting new technologies to narrow the gap between actual and water-limited yield. With yields reaching a plateau in many of the developed world’s cropping areas, it is possible that Australia’s leading farmers have similarly closed the exploitable yield gap and require technological breakthroughs to sustainably push the production frontier to new and higher levels. To assess the potential for Australian farmers to continue closing the yield gap, and possibly increase water-limited yield, the long-term farm production records of individual wheat fields of three leading farmers in South East Australia were used to ascertain the applicability of modelling to develop new and innovative practices. The cropping systems simulator APSIM was used to establish the attainable simulated yield based on the farmers’ chosen management inputs for wheat crops over a period of 16–20 years. A strong relationship (r2=0.89, RMSD=508kgha−1) was found between actual and simulated yields. This relationship indicates that yield-reducing factors not simulated by APSIM (weeds, disease etc.) were largely controlled on these farms and confirms APSIM’s suitability for this analysis. Over the 16–20 year study period, the average yield gaps on the three farms ranged from 480 to 770kgha−1; representing between 74 and 82% of their water-limited yield potential. For these leading farmers, the yield gap is only small and unlikely to be economically exploitable under current management practices. Consequently, three tactical management innovations with potential to improve farm wheat yield and reduce risk were evaluated. One innovation investigated whether farmers practicing no-till crop establishment, who were able to control weeds prior to sowing, could benefit from sowing current cultivars earlier than present-day practice. It was found that leading farmers are already sowing at the optimum time and sowing earlier would not increase yield because of greater risk of frost damage. Two other innovations were found to have practical application. The first used Yield Prophet® to assist farmers decide when to apply in-crop nitrogen fertiliser based on a more complete understanding of nitrogen and water requirements of crops in variable growing seasons. The second innovation involved sowing slow maturing wheat cultivars earlier than current practice but only in years with adequate stored soil water and early season rainfall. Both innovations were found to increase grain yield and reduce risk of over- or under-application of nitrogen fertiliser. Investigation of strategic and tactical management options to increase yield using simulation modelling for subsequent evaluation in the field has the potential to keep Australian farmers at the forefront of innovations in crop production.

YIELDSTAT – A spatial yield model for agricultural crops

The YIELDSTAT model for crop yields, an advanced hybrid of traditional non-linear regression approaches and expert knowledge databases, was developed to predict the spatial distribution of yields for a range of arable crops (winter wheat, winter barley, winter rye, winter triticale, spring barley, oats, potato, sugar beet, winter oil-seed rape, silage maize, clover, clover/grass mix, lucerne, lucerne/grass mix, fodder grass) and two grassland types (intensive, extensive) in eastern Germany across different scales up to the regional scale. YIELDSTAT accounts for a wide range of yield-influencing factors derived from weather, soil, relief and management data, as well as for the long-term changing atmospheric CO2 concentration and for the trend owing to progress in breeding and agro-technology. YIELDSTAT regression modules were derived from several hundred farm data sets from 1975 to 1990 and tested against recent yield observations from the Federal State of Thuringia, Germany. The model test was performed at three different spatial scales. YIELDSTAT successfully reproduced the observed data at all three scales, with a normalised mean bias error of 3.02% across all crops and scales. Model testing also revealed a number of weaknesses in the model, identifying yield-reducing factors that had not been considered previously. All in all, the model proved fitness-for-purpose for simulating spatial yields, also under assumed future climate conditions.

Antioxidative and physiological responses of two sunflower (Helianthus annuus) cultivars under PEG-mediated drought stress

Drought stress is one of the most important yield-reducing factors in crop production. Sunflower, an oilseed crop, is severely affected by abiotic stress. In this study, 2 sunflower cultivars (Musala and Aydın) were evaluated in terms of various biochemical and physiological responses under 2 different polyethylene glycol-mediated drought stress conditions. Stress-determining parameters such as malondialdehyde (MDA), hydrogen peroxide (H_{2}O_{2}), and proline contents were determined. Both cultivars showed similar responses at osmotic potentials of both -0.4 and -0.8 MPa. Aydın was less affected than Musala under these stress conditions. MDA, H_{2}O_{2}, and proline levels were similar at both -0.4 and -0.8 MPa osmotic potentials in the 2 different cultivars. The 2 cultivars differed significantly in ascorbate peroxidase and catalase enzyme activities, which were more prominent in Aydın for both stress levels. However, glutathione reductase activity did not appear to be an essential part of the antioxidative system in either of the cultivars. Engineering antioxidative enzyme levels might provide a potential mechanism to cope with drought stress in sunflower.

Impact and Interaction of Nitrogen and Phytophthora infestans as Yield-limiting and Yield-reducing Factors in Organic Potato (Solanum tuberosum L.) Crops

For organic potato producers, the two main challenges are disease and nutrient management. Both are limited by regulations that on the one hand prohibit the use of chemical fertilizers, especially nitrogen, and on the other hand prohibit most synthetic pesticides. Late blight caused by Phytophthora infestans is commonly thought to be the most yield-reducing factor. However, because there is no really effective fungicide available to control late blight, there are virtually no yield loss data available for organic farming conditions. In this paper, the state of the art of organic potato tuber growth under on-farm conditions with respect to disease and nutrient management is summarized by field trials and on-farm surveys on commercial organic crops carried out in the years 1995–1998. Soil nitrogen (N) levels, plant N uptake, disease development of P. infestans and potato yield were measured. Results indicated that N availability was most important in limiting yields in organic potato crops. From on-farm data, a model including disease development, growth duration of the crops until foliage decay and different parameters related to N status of the crop could explain 73% of the observed variation in yield. Only 25% of this variation in yield could be attributed to the influence of late blight. Differences in N availability explained 48%. In conclusion, several points emerged from the results. In organic farming, yields are mainly limited by nutrient availability in spring and early summer. The effects of late blight on yields may often be overestimated and cannot be deduced from results in conventional farming because of the strong interaction with nutrient status. Depending on N availability, tubers stop growing between mid-July (70–90 kg N ha−1 uptake), the end of July (110–140 kg N ha−1 uptake) and mid-August (140–180 kg N ha−1 uptake) due to N limitations. The higher the N status of a potato crop, the longer the growing period needed to achieve the attainable yield and the higher the probability that late blight stops further tuber growth and becomes the key tuber-yield-limiting factor. In the second part of this paper, the interactive effects of soil N availability and the impact of P. infestans on yield in the presence and absence of fungicides from 1996 to 1998 for mid-early main crops are reported. An empirical schematic model of disease impact depending on N availability was developed.

Spatial and temporal aspects of agricultural sustainability in the semi‐arid tropics: a case study in Mbeere district, Eastern Kenya

AbstractA study was conducted in the semi‐arid Mbeere district, Eastern Kenya, to determine spatial and temporal variability of crop yields and climatic factors and their impacts on sustainability of semi‐arid agriculture. Spatial aspects were assessed by conducting a survey of on‐farm crop yields and using a computer model (QUEFTS) to predict maize yields from soil chemical indices. Temporal aspects were studied using time‐series data (rainfall, temperature and crop yields). The study did not find a significant correlation between farmers' actual yields and QUEFTS predictions, and soil nutrients were thus not the only factors influencing maize yields in the study area. Other yield‐reducing factors (climate and management) not accounted for in the QUEFTS model played a role. Grain yields of staple food crops were highly variable, fluctuating in time and space, and suboptimal. Particular aspects of rainfall (e.g. the short rains, for cowpeas and beans) were more important than mean annual rainfall in determining crop yields, and factors other than rainfall and soil fertility apparently played a role. The observed low grain yields, large yield gaps and high rainfall variability challenge the sustainability of these farming systems. Copyright © 2008 John Wiley & Sons, Ltd

Analysis of a complex crop production system in interdependent agro-ecological zones: a methodological approach for potatoes in Argentina

This paper shows how a highly complex potato production system, like the Argentinian one, can be surveyed and analysed. A survey was complemented with the use of a Geographic Information System and a simulation model approach which improves the understanding of the possibilities for increasing future crop production by expanding the area cropped with potatoes and/or the yield. A yield gap analysis was also a useful and comprehensive tool to identify and rank yield defining, yield limiting and yield reducing factors for those agro-ecological zones where the potato is currently grown. With these procedures, the physiological age of seed tubers and virus diseases were identified as the most relevant factors limiting and reducing yield. Specific strategies can be developed to counteract their limiting and reducing effects upon seed quality and tuber yield. The approach of this work does not only apply for the specific situation of the potato crop in Argentina, but this framework could be successfully applied to other crops or production systems elsewhere.

LINKING MULTIPLE LAYERS OF INFORMATION FOR DIAGNOSING CAUSES OF SPATIAL YIELD VARIABILITY IN SOYBEAN

Soybean yields are highly variable across fields because of the complex interaction of many factors, includingweather, management, soil properties, fertility, pests, and weeds. However, only limited progress has been made on techniquesfor diagnosing reasons for yield variability and for identifying and managing different areas of the field to maximize profitor minimize environmental risk. The objective of this study was to develop a crop modelbased technique to attribute yieldlosses due to water stress, soybean cyst nematodes (SCN), soil pH, and weeds that may cause soybean yield variability.<br><br>The procedure computes yield as a function of: (a) sitespecific soil water parameter inputs to a crop model, and(b) residual site growth and yieldreducing factors not accounted for by the crop model and its spatially variable soil waterparameters. We used a twoyear data set from Iowa (1995 and 1997) to estimate parameters and evaluate yield predictionsusing a combined crop modelstatistical regression approach. The data were modified by imposing pH, SCN, and weed stressto 8 of 60 grids and a combination of these stresses to 8 grids. Thus, the dataset was constructed based on the both natural(field measured) and artificial yield variation. This was done to evaluate the accuracy with which the proposed techniquecould quantify losses.<br><br>The model indicated that water stress reduced soybean yields by an average 1092 and 710 kg ha 1 for 1995 and 1997,respectively. After taking into account water stress, pH, SCN, and weeds, the combined approach was able to explain 96%of yield variability over two years of data. The RMSE was 143 kg ha 1 . Our technique was able to reproduce the quantitiesof yield loss for each grid and attribute them to the correct causes. Standard errors of attribution accuracy were 185, 220,and 163 kg ha 1 for soybean yield losses due to soil pH, SCN, and weeds, respectively. The combined crop modelregressiontechnique was able to reproduce an observed spatial grid of yield according to both natural and imposed yield variation, andto quantify losses due to several factors.

Agro-ecological zoning at the regional level: spatio-temporal variation in potential yield of the potato crop in the Argentinian patagonia

The purposes of this study on potatoes were to perform an agro-ecological characterization, to estimate the potential yield of the crop with the LINTUL-POTATO simulation model, and to identify yield determining, yield limiting and yield reducing factors for the patagonic area of the Rı́o Negro valley (Argentina), where the potato crop acreage is rapidly expanding. The valley was divided into three areas based on differences in climate: high, medium and low valley, represented by the weather stations Cipolletti (SL 38° 57′, WL 67° 59′), Choele Choel (SL 39° 17′, WL 65° 39′) and Viedma (SL 40° 47′, WL 62° 59′), respectively. Suitable soils for potato cultivation were identified at the great group soil level: torrifluvents, torriorthents and haplargids. Potential yield of the crop proved similar to that obtained in other regions of the world (>25 tonne ha −1 dry matter) and because of high irradiance and ample thermal amplitude, dry matter content of the tubers can be higher than that obtained in other potato growing areas of Argentina. In late plantings, high temperature is the most important yield defining factor because of its negative effect on dry matter partitioning. Inadequate soil tillage after land reclamation, low soil fertility and persistently strong winds were identified as the most important yield limiting factors, whereas yield reducing factors are not yet in evidence in these new areas.

Agro-ecological zoning and potential yield of single or double cropping of potato in Argentina

Potato is the most important horticultural crop in Argentina and at present 100,000 ha are grown in different regions and seasons. The four possible growing seasons are defined as early (June–October), medium-early (July–November), medium-late (October–March) and late (February–June) and have already been characterized by assessing weather, soil and crop type, yield level and yield determining, yield limiting and yield reducing factors. However, there is scarce or no information on the possibilities of expanding actual crop frontiers, either at regional or national level and on the potential yield of the crop in different agro-ecological zones. Hence, in this work, we (1) characterize agro-ecological zones for potato production, (2) establish potential duration of the crop cycle and potential growing seasons, (3) estimate the potential yield of the crop in these zones and seasons and (4) demonstrate how Geographic Information Systems (GIS) for land evaluation and simulation models that establish potential yield of the crop can be used together to assess possibilities for increasing crop production at regional or national scales. Seven potential growing seasons ranging from <1500 to >3500°C day were identified for areas where one crop can be grown per year, whereas four areas were identified where there is a potential for a second crop of potatoes. In these areas and seasons, potential tuber yields ranged from <10 to >20 Mg ha −1 dry matter. The study identified suitable soils and ascertained the corresponding potential duration of the growing seasons and the potential yield for each of the suitable sites and seasons.

Global networking for assessment of impacts of global change on plant pests

Global change encompasses changes in atmospheric composition, climate and climate variability, and land cover and land use. The occurrence of these changes and their interactive effects on biological systems are worldwide; thus, an effective global change research and impact assessment program must be based on international and interdisciplinary research and communication. With this in mind, several collaborative research networks with a focus on global change have been established in the biological sciences. They include the Global Change and Terrestrial Ecosystems (GCTE) Core Project of the International Geosphere–Biosphere Programme (IGBP) which aims to predict the effects of global change on terrestrial ecosystems, including agriculture and production forestry. Because of the importance of plant pests (arthropods, microbial pathogens, weeds) as yield-reducing factors in agriculture and as early indicators of global change, GCTE initiated a network Activity on “Global Change Impacts on Pests, Diseases and Weeds” with the overall goal of developing a predictive capability for impact assessment and adaptation. The network's specific objectives, contributing research projects, initial results and future challenges are discussed.

Rice Pest Constraints in Tropical Asia: Quantification of Yield Losses Due to Rice Pests in a Range of Production Situations.

A series of experiments was conducted where a range of injuries due to rice pests (pathogens, insects, and weeds) was manipulated simultaneously with a range of production factors (fertilizer input, water supply, crop establishment method, variety) in different seasons and years. These factors were chosen to represent lowland rice production situations characterized in surveys conducted in tropical Asia and their corresponding range of attainable yield. Experiments complemented one another in exploring the response surface of rice yields to yield-limiting and yield-reducing factors. The resulting experimental data base consisted of 445 individual plots and involved 11 manipulated injuries in a range of attainable yields of 2 to 11 t ha-1. A first, nonparametric, multivariate analysis led to a hierarchy of potential injuries, from marginally (e.g., bacterial leaf blight) to extremely harmful (e.g., rice tungro disease). A second, parametric, multivariate approach resulted in a multiple regression model involving factors generated by principal component analysis on injuries that adequately described the variation in actual yield. One major finding was that some (attainable yield × injury factors) interactions significantly contributed to the description of variation in actual yield, indicating that some injuries (or their combinations) had a stronger or weaker yield-reducing effect, depending on the level of attainable yield. For instance, yield losses due to sheath blight, weed infestation, and rice tungro disease tend to increase, remain stable, and decrease, respectively, with increasing attainable yields. Back-computations using the principal component regression model estimated yield losses caused by individual injuries, using the mean injury levels in a population of farmers' fields surveyed across tropical Asia. The results indicate that sheath blight, brown spot, and leaf blast are diseases that cause important losses (between 1 and 10%) regionally. Among the insect injuries, only white heads caused by stem borers appear of relevance (2.3% yield losses). These injuries, however, do not match in importance those caused by weeds, whether outgrowing the rice crop canopy (WA) or not (WB), both types of injuries causing about 20% yield losses when considered individually. When all mean injuries were combined into one mean injury profile occurring at a regional attainable yield of 5.5 t ha-1, a mean yield loss of 37.2% was estimated, indicating that injuries were less than additive in their yield-reducing effects. Scenario analyses were conducted in a set of (production situations × injury profiles) combinations characterized from surveys in farmers' fields in tropical Asia. Depending on the scenario chosen, losses ranging from 24 to 41% were found.

Food Security in the face of Global Change

The ability to feed the world's rapidly growing population would be substantially achieved by reducing the “yield gap”, ie. by improving the management of agroecosystems to yield crops nearer to their maximum physiological potential. An enhanced integrated research effort is therefore needed to determine the relationships between potential yield and yield reducing factors; the incorporation of new understanding within a “systems analysis” framework will help develop improved management.The Global Change and Terrestrial Ecosystems Core Project of IGBP has launched a series of international research networks (the GCTE Crop Networks for wheat, rice, potato, cassava, sorghum, maize and groundnut) which aim to develop an improved predictive capacity of the effects of global change on crop production. They adopt a systems analysis approach and involve an iterative programme of experimentation and modelling. Network results are designed to develop a strong predictive capability of the effects of future scenarios on crop production, but they can also be used to help improve yields under today's environmental conditions. The rationale, objectives and modus operandum of the GCTE Crop Networks are described.Results of model comparisons in the GCTE Rice Network are summarised. The current rice models are able to provide a good estimate of yield potential in a wide range of rice growing environments where yield potential ranges from 6 to 15tha-1; and that in the temperature range -3 to +3°C there is general agreement on the yield change of about -4.5% (or -0.3tha-1) for each degree of temperature difference from the base climate.