Crop Management Recommendations Research Articles

A probabilistic framework for forecasting maize yield response to agricultural inputs with sub-seasonal climate predictions

Crop yield results from the complex interaction between genotype, management, and environment. While farmers have control over what genotype to plant and how to manage it, their decisions are often sub-optimal due to climate variability. Sub-seasonal climate predictions embrace the great potential to improve risk analysis and decision-making. However, adequate frameworks integrating future weather uncertainty to predict crop outcomes are lacking. Maize (Zea mays L.) yields are highly sensitive to weather anomalies, and very responsive to plant density (plants m−2). Thus, economic optimal plat density is conditional to the seasonal weather conditions and can be anticipated with seasonal prospects. The aims of this study were to (i) design a model that describes the yield-to-plant density relationship (herein termed as yield–density) as a function of weather variables, and provides probabilistic forecasts for the economic optimum plant density (EOPD), and (ii) analyze the model predictive performance and sources of uncertainty. We present a novel approach to enable decision-making in agriculture using sub-seasonal climate predictions and Bayesian modeling. This model may inform crop management recommendations and accounts for various sources of uncertainty. A Bayesian hierarchical shrinkage model was fitted to the response of maize yield–density trials performed during the 2010–2019 period across seven states in the United States, identifying the relative importance of key weather, crop, and soil variables. Tercile forecasts of precipitation and temperature from the International Research Institute were used to forecast EOPD before the start of the season. The variables with the greatest influence on the yield–density relationship were weather anomalies, especially those variables indicating months with above-normal temperatures. Improvements on climate forecasting may also improve forecasts on yield responses to management, as we found reduced bias and error (by a factor >10), and greater precision (e.g. R2 increased from 0.26 to 0.32) for cases where weather forecasts matched observations. This study may contribute to the development of decision-support tools that can trigger discussions between farmers and consultants about management strategies and their associated risks.

Interpretable machine learning methods to explain on-farm yield variability of high productivity wheat in Northwest India

The increasing availability of complex, geo-referenced on-farm data demands analytical frameworks that can guide crop management recommendations. Recent developments in interpretable machine learning techniques offer opportunities to use these methods in agronomic studies. Our objectives were two-fold: (1) to assess the performance of different machine learning methods to explain on-farm wheat yield variability in the Northwestern Indo-Gangetic Plains of India, and (2) to identify the most important drivers and interactions explaining wheat yield variability. A suite of fine-tuned machine learning models (ridge and lasso regression, classification and regression trees, k-nearest neighbor, support vector machines, gradient boosting, extreme gradient boosting, and random forest) were statistically compared using the R 2 , root mean square error (RMSE), and mean absolute error (MAE). The best performing model was again fine-tuned using a grid search approach for the bias-variance trade-off. Three post-hoc model agnostic techniques were used to interpret the best performing model: variable importance (a variable was considered “important” if shuffling its values increased or decreased the model error considerably), interaction strength (based on Friedman’s H-statistic), and two-way interaction (i.e., how much of the total variability in wheat yield was explained by a particular two-way interaction). Model outputs were compared against empirical data to contextualize results and provide a blueprint for future analysis in other production systems. Tree-based and decision boundary-based methods outperformed regression-based methods in explaining wheat yield variability. Random forest was the best performing method in terms of goodness-of-fit and model precision and accuracy with RMSE, MAE, and R 2 ranging between 367 and 470 kg ha −1 , 276–345 kg ha −1 , and 0.44–0.63, respectively. Random forest was then used for selection of important variables and interactions. The most important management variables explaining wheat yield variability were nitrogen application rate and crop residue management, whereas the average of monthly cumulative solar radiation during February and March (coinciding with reproductive phase of wheat) was the most important biophysical variable. The effect size of these variables on wheat yield ranged between 227 kg ha −1 for nitrogen application rate to 372 kg ha −1 for cumulative solar radiation during February and March. The effect of important interactions on wheat yield was detected in the data namely the interaction between crop residue management and disease management and, nitrogen application rate and seeding rate. For instance, farmers’ fields with moderate disease incidence yielded 750 kg ha −1 less when crop residues were removed than when crop residues were retained. Similarly, wheat yield response to residue retention was higher under low seed and N application rates. As an inductive research approach, the appropriate application of interpretable machine learning methods can be used to extract agronomically actionable information from large-scale farmer field data. • Data-driven agronomic research requires new analytical and methodological approaches. • Machine learning methods were used to disentangle complex relationships in farmer field data. • Model-agnostic tools were used to derive agronomic interpretations. • Residue management and N application rate were important management variables for wheat yield. • Residue management interacted with other practices to explain wheat yield variability.

Evaluating maize yield response to fertilizer and soil in Mexico using ground and satellite approaches

Crop management recommendations that are tailored to local conditions offer promise for improved farmer livelihoods, input use efficiency, and crop output. An important step towards achieving localized recommendations is to establish repeatable, low-cost approaches to diagnosing nutrient constraints. Satellite remote sensing is particularly attractive in this context for its ability to monitor crop conditions and provide yield estimates at much lower expenses than extensive field measurements. Here we evaluate nutrient constraints for maize yields in central Mexico by utilizing an extensive field dataset on soil properties, fertilizer practices, and yields that were collected as part of the MasAgro program. We also compare insights from satellite-based yield estimates to those derived from crop-cut yields. Overall we find strong positive associations between maize yields and fertilizer inputs for N, P, and K in farms at lower elevations, characterized by larger, commercial farms. In higher elevations where farms are typically smaller and use less responsive varieties, we still find significant but slightly less positive associations with N and P. In both regions, we observe significant negative associations between yield and soil pH, consistent with an unmet need for micronutrient fertilizers in the region. Satellite-based yields were able to reproduce the positive associations and large yield response to fertilizer N and P, but were not sensitive enough to detect significant effects of secondary factors, namely fertilizer K and soil pH. We conclude by suggesting specific interventions that could be experimentally tested in the region.

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.

Agronomic management of industrial hemp alters foliar traits and herbivore performance

Agronomic management decisions can alter plant foliar traits, especially nutritional quality, with potential to influence plant–herbivore interactions. Herbivores balance consumption of plant tissue for nutritional gains related to growth and development while contending with plant traits that may deter herbivory or are toxic. This study evaluates the influence of management decisions on the foliar quality of industrial hemp (Cannabis sativa) and the impact on the performance of fall armyworm (Spodoptera frugiperda). We included three common management decisions in this study: fertilization rate, cultivar choice, and planting date. In a no-choice feeding trial, fall armyworm larvae were fed leaves of field-grown hemp from three different cultivars that received different rates of fertilizer and were planted on two different dates. We quantified levels of foliar nitrogen, the ratio of carbon to nitrogen, leaf mass per unit area, and concentrations of tetrahydrocannabinol and cannabidiol, and determined the influence of foliar quality on herbivore performance. Fertilization rate, cultivar, and the interactions of planting date with both fertilization rate and cultivar influenced multiple aspects of hemp foliar quality. Overall, fertilization had the largest influence on foliar quality and subsequent herbivore performance. Variation in foliar traits influenced herbivore performance. Foliar nitrogen had a positive impact on fall armyworm performance and the ratios of carbon and total cannabinoids to nitrogen had negative impacts on fall armyworm performance. Our findings show that management decisions in industrial hemp can affect plant–insect interactions through changes in foliar quality. These outcomes suggest that developing crop management recommendations for pest management will be important for a re-emerging crop like industrial hemp.

Scaling up from crop to farm level: Co-innovation framework to improve vegetable farm systems sustainability

CONTEXTSuccessful scaling up from crop level research to adoption by farmers depends on its practitioners being aware of the constraints that arise as scaling up proceeds. Promising ideas from crop level research are not adopted by farmers in many cases. OBJECTIVEScaling up is explored here in a study conducted in five vegetable farms in south Uruguay, where tomato production was one of the main sources of income. We aimed to evaluate the redesign process at both levels (crop and farm) focusing on: synergies and trade-offs between crop yield and farm system improvement and how critical crop constraints could be solved at farm level. METHODSImproved crop management practices were proposed following yield gap analysis recommendations developed in previous studies, aiming to maximise crop yield and inputs use efficiency. At the farm level, a multi-year plan defining crops choice, crops area and allocation of fields to crops was designed to match resource demands to supply, especially for labour. Redesign plans were implemented for one year, with 66% of redesigned activities adopted at farm level and 86% at crop level. RESULTS AND CONCLUSIONSWe found several examples where farm system adjustment conflicted with crop yield maximization. We also found constraints prioritised as bottlenecks for crop yield improvement, which require solutions at the farm level. For instance, fertigation problems (quantity and timing) explained part of the yield gap in tomato. Overcoming these constraints requires farm system settings, e.g. adjustment of cropping area to water availability, development of suitable water reservoirs and infrastructure for water distribution and fertilisers injection, besides adjustment of a fertigation plan according to crop demand, soil fertility and greenhouse environment at crop level. Crop yield limiting factors identified through yield gap analysis, considered inside a farm system redesign process, could be a powerful tool to better targeting crop management recommendations to overall farm performance improvement. SIGNIFICANCEThe consideration of farm systems as a whole showed greater economic and environmental impacts than focusing only on the main crop due to synergies and trade-offs among farm system components. Multi-scale analysis showed that recommendations from crop research need to be tailored to farm context and objectives considering synergies and trade-offs.

Balancing Economic and Environmental Performance for Small-Scale Rice Farmers in Peru

Integrated research efforts are needed to provide insights for simultaneously achieving food production and environmental goals in rice production systems. In this study we evaluated eight Sustainable Rice Platform performance indicators (net profit, labor productivity, grain yield, water use efficiency, nitrogen use efficiency, phosphorus use efficiency, pesticide use efficiency, and greenhouse gas emissions) for 65 rice farmers in Peru. The aim was to 1) identify the major management factors influencing profitability, 2) evaluate potential tradeoffs between economic and environmental sustainability, and 3) understand farmer perceptions and decision-making processes related to profitability. For data analysis, farmers were grouped into profitability categories based on net profit: top (top 20%), middle (middle 60%), and bottom (bottom 20%). Our results indicate that rice yields, grain sale price, and total spending drive profitability in this region of Peru. An important finding is that the top profitability group achieved higher yields while using lower inputs of labor, pesticides, elemental P, and irrigation hours, resulting in improved environmental performance (e.g. higher nutrient and water use efficiency and lower greenhouse gas emissions). Results from focus group discussions highlighted that rice production has changed in recent decades, and there is a need for improved extension support to cope with increasing pests, disease, and water scarcity. Moreover, farmers indicated they are vulnerable to exploitation from vendors along the rice supply chain, with limited access to credit facilities and market channels and little negotiating power over the cost of inputs and rice sale price. Overall, our results suggest that rice productivity and profitability can be improved in this region without negatively influencing environmental footprint per unit production, yet this is unlikely to occur unless key agronomic and socioeconomic challenges are addressed, such as improved access to crop management recommendations, alternative financing and credit options, and technologies that decrease the costs of production.

Farmer participatory research in agricultural extension programs: A case study of fertilizer management in tropical rice

AbstractAgricultural extension requires close communication with farmers, and researchers must consider farmers’ perspectives on crop management. Farmers tend to take into account the canopy appearance when they decide on fertilizer application, and this is often neglected in crop management recommendations by researchers. Our objectives were to dissect the growth characteristics that farmers implicitly account for in nutrient management of tropical rice. Farmer participatory trials were conducted in irrigated and rainfed lowlands in the Philippines during the wet seasons of 2014, 2015, and 2016. Each year, 30 participating farmers made decisions on fertilizer management for plots with different seedling ages and planting densities. These treatments greatly changed the canopy appearance, and affected farmer decisions on nitrogen (N) management, particularly in the first year. We found that plant height and leaf greenness were the major determinants of their decisions in irrigated lowlands. Under rainfed conditions, the risk of drought made farmers focus on tillering rather than plant elongation and leaf color during early growth stages, and on canopy cover and plant elongation during later stages. Across years and water regimes, farmers applied 78% more N than researchers without generally increasing grain yield. Since crop diagnosis is a key for successful management by farmers, guidelines for efficient nutrient management should include numerical targets for the traits emphasized by farmers. That will help farmers better understand their crops, and the guidelines will be more user-friendly than providing only a fertilizer application prescription.

Determining optimum nitrogen management as a function of planting date for spring wheat (Triticum aestivum L.) under semi-arid conditions using a modeling approach

A crop modeling approach can be used to assess various crop management options and water productivity to improve crop production for different environments. The goal of this study was to determine optimum planting dates and nitrogen management options using a systems analysis approach based on the Cropping System Model (CSM) CERES-Wheat of DSSAT v 4.6. Wheat experiments were conducted from 2007 to 2013 under semi-arid conditions at the experimental fields of the Nuclear Institute for Agriculture and Biology (NIAB), Faisalabad, Pakistan. The CSM-CERES-Wheat model was calibrated for wheat (Triticum aestivum L.) cultivar Sehar-2006 from a 3-year field experiment data on flowering day, maturity day, canopy cover, grain yield, biomass, grain nitrogen content, and nitrogen harvest index. After calibration, the CSM-CERES-Wheat model produced satisfactory simulations for wheat phenology and crop growth parameters. The model was then evaluated using six years of independent datasets for both irrigated and rainfed conditions. The evaluation showed that the model performed well as indicated by the accurate simulation of wheat phenology [the normalized root mean square error (NRMSE) = 3%] and crop growth parameters such as crop cover (NRMSE = 7%–13%), biomass (NRMSE = 17%), and grain yield (NRMSE = 9%) against measured data. The model's performance was less satisfactory for biomass production under high soil moisture or rainfed conditions. Focusing on differences in temperature and rainfall patterns during the potential growing season, the model was used to estimate the optimum planting dates and in-season nitrogen management options using 39 years (1974–2013) of historical weather data for short-term adaptation against climate variability. The scenario simulations for planting dates showed that growing wheat under rainfed conditions at the study region is not a viable option. However, based on the simulation results, we concluded that early planting from November 1st to 10th for irrigated conditions with applications of 150 kg N ha−1 in three equal splits with the first at sowing and the rest two splits during vegetative growth phase could result in higher crop yield and water use efficiency. This study showed that the model can be a promising tool for providing crop management recommendations under high-temperature conditions found in the semi-arid conditions.

Adopción e impacto de las tecnologías agropecuarias generadas en el Ecuador

El conocimiento de los efectos que tienen las tecnologías en la sociedad es el instrumento esencial para motivar el desarrollo de la investigación, ya que brinda insumos a los tomadores de decisiones y generadores de políticas que permiten proyectar el impacto de futuras inversiones. En Ecuador, el principal centro público encargado de la investigación y desarrollo de tecnologías en el sector agropecuario es el Instituto Nacional de Investigaciones Agropecuarias (INIAP), que desarrolla material genético, alternativas de manejo para incrementar y agregar valor a la producción, alternativas para el manejo del suelo y agua y la conservación de los recursos genéticos. Con el objetivo de estimar la adopción e impacto de las tecnologías agropecuarias que se generan en el país, se analizaron 37 estudios de adopción, impacto y rentabilidad económica de tecnologías generadas por el INIAP, publicados en el período 2007-2017. La tasa de adopción promedio a nivel nacional de las variedades desarrolladas por el INIAP fue del 37%, con una tasa interna de retorno promedio del 33%. Los impactos fueron positivos a nivel económico, ambiental y productivo para los agricultores que adoptaron las tecnologías. Estos resultados sirven de apoyo a los políticos y tomadores de decisiones en el país para el direccionamiento y planificación estratégica de la investigación, que permita el desarrollo de una agricultura sostenible y de referencia para la región.

Mapping field-scale yield gaps for maize: An example from Bangladesh

Accurate estimation of the size and spatial distribution of the yield gap has many practical applications, including relevance to precision agriculture and technology targeting. The objectives of this study were to illustrate a methodology to create a yield gap map and to discuss its potential uses to provide optimal crop management recommendations to the farmers. We used the HybridMaize crop simulation model to estimate potential yield for maize grown in the winter season in northwestern Bangladesh. This is a high yielding environment, where farmers achieve yields as high as 12Mg/ha. The model predicted a mean potential yield of 12.87Mg/ha. We used a RapidEye satellite image acquired around tasseling to identify the maize fields, calculate ground cover and its regression to actual yield from farmers’ fields. Next, the regression was applied to all the maize pixels in the image to calculate actual yield. In the last step, we created a yield gap map based on the difference between potential and actual yield. Yield gap maps will enable agronomists to identify production constraints on farmers’ fields with large yield gaps. Alternatively, by learning from the farmers with the highest actual yields and analyzing their data, it will be possible to generate region or field specific, optimized crop management recommendations.

Improving irrigated rice production in the Senegal River Valley through experiential learning and innovation

Research facilitating farmer–researcher collaboration and experiential learning may provide the missing element to tailor crop management recommendations to meet farmers’ needs. We tested different crop management systems for irrigated rice in three seasons of adaptive research trials in three locations in the middle Senegal River Valley. Our objectives were to assess the agronomic and socio-economic viability of Recommended Management Practices (RMPs) compared to the System of Rice Intensification (SRI) and Farmers’ Practices (FPs). During the 2008 dry season, RMP and SRI significantly increased yields over FP by 2.3 and 2.6tha−1 across sites. Farmers analyzed their experiences in post-experiment meetings. They appreciated SRI’s yield and water-saving potential, but found it labor demanding, especially for weed management requirements that coincided with horticultural activities. Conversely, farmers described RMP’s elevated herbicide rate as costly, and indicated that because of poorly functioning agro-chemical markets, herbicide volumes larger than typically used in FP might be difficult to reliably source. To modify management systems to fit farmers’ needs and assets, we collaboratively developed a fourth, ‘Farmer Adapted Practice’ (FAP) that blended RMP and SRI. FAP used intermittent irrigation during the late vegetative stage, recommended crop density, intermediate seedling age, and a single round of mechanical weeding followed by localized herbicide application. Farmers compared FAP against the initial management systems in the subsequent seasons. Though no yield differences were found between RMP, SRI and FAP, each yielded significantly more (+1.0, +1.1 and +1.5tha−1) than FP. FAP also reduced labor requirements without increasing weed biomass compared to RMP or SRI, and used 40% and 10% less herbicide than RMP and FP, respectively. Cumulative distribution functions showed that FAP increased net profit potential and decreased economic risk. Prior to the 2009 dry season trials, the Senegalese state eliminated herbicide subsidies, doubling their cost. RMP, SRI and FAP yielded 2.9, 3.0 and 3.1tha−1 more than FP. FAP again reduced weeding labor and herbicide requirements while lowering production risk across sites. This study demonstrates the value-added outcomes that result from research that facilitates farmer–researcher collaboration to learn from, innovate and tailor management systems to fit local circumstances.

Estimating irrigation water use for maize in the Southeastern USA: A modeling approach

Increased crop production and expansion of irrigated acreage in the southeastern USA have increased agricultural water use during the past two decades. To optimize irrigation water use, it is important to know when to irrigate and how much water should be applied. The objectives of this study were (1) to evaluate the Cropping System Model (CSM)-CERES-Maize model with measured data of the amount of water required for supplemental irrigation and (2) to apply the CSM-CERES-Maize model for estimating irrigation water use for maize in the southeastern USA. The CSM-CERES-Maize model was evaluated for 2000–2004 for five counties that represent the dominant maize production regions in South Georgia. For each county, historical daily weather data, three representative soil profiles, and specific crop management recommendations were used as input for the model. The simulated results were then compared with observed data obtained during the same period. The amount of water required for irrigation for each growing season was simulated for 58 years using historical weather data from 1950 to 2007 for 88 selected counties that corresponded to the most important agricultural production region in Georgia. Both monthly and annual water demand for maize was determined for each county. The total seasonal amount of water required for irrigation across counties and years ranged from 136 to 281mm, with an average of 227mm. The irrigation requirements among months varied from 10 to 79mm, with the highest amount required for May. The results from the evaluation showed that the model was able to simulate the amount of water required for maize irrigation in good agreement with the observed data. This demonstrated the potential application of the CSM-CERES-Maize model as a tool for estimating water demand for irrigation. The estimated water requirements for supplemental irrigation can be used by both policy makers and local farmers for planning the amount of water required for supplemental irrigation as well as for improvements in irrigation management for water conservation.

Vulnerability of smallholder farmers to ENSO-related drought in Indonesia

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 38:155-169 (2009) - DOI: https://doi.org/10.3354/cr00778 Vulnerability of smallholder farmers to ENSO-related drought in Indonesia Alwin Keil1,*, Nils Teufel2, Dodo Gunawan3, Constanze Leemhuis4 1Department of Agricultural Economics and Social Sciences in the Tropics and Subtropics, Universität Hohenheim (490a), 70593 Stuttgart, Germany 2International Livestock Research Institute (ILRI), Todapur Road, Pusa, New Delhi 110012, India 3Research and Development Center, Meteorological and Geophysical Agency (BMG), Jl. Angkasa 1 No. 2, Kemayoran, Jakarta, Indonesia 4Center for Development Research (ZEF), Walter-Flex-Str. 3, 53113 Bonn, Germany *Email: alwin.keil@uni-hohenheim.de ABSTRACT: Crop production in Southeast Asia is subject to considerable climate variability caused by the El Niño–Southern Oscillation phenomenon. El Niño causes comparatively dry conditions leading to substantial declines in crop yields, with severe consequences for the welfare of local farm households. Using an interdisciplinary modelling approach that combines regression analysis with linear programming (LP) and stochastic simulation, and integrates climatic and hydrologic modelling results, the objective of the present study is to assess the impact of El Niño on agricultural incomes of smallholder farmers in Central Sulawesi, Indonesia, and to derive suitable crop management strategies to mitigate income reductions. We identify 5 farm classes by cluster analysis. Our LP model maximises their cash balance at the end of the time period most severely affected by El Niño. Main activities are the cultivation of rice, maize and cocoa. Accounting for water supply, external Cobb-Douglas production functions generate output according to level of production intensity and predicted weather patterns. Stochastic simulation accounts for variations in crop yields due to factors not captured by the production functions. Iterative model runs produce probability distributions of the model outcomes for each household class, whereby the downside risk of failing to achieve a specified minimum level of income is particularly policy relevant. The results illustrate that drought-related crop management recommendations must be tailored to farm households according to their location, farming system and resource endowment. Furthermore, our findings demonstrate the importance of policy measures aimed at an ex post alleviation of drought impacts. KEY WORDS: ENSO · Risk management · Linear programming · Stochastic simulation · Indonesia Full text in pdf format PreviousNextCite this article as: Keil A, Teufel N, Gunawan D, Leemhuis C (2009) Vulnerability of smallholder farmers to ENSO-related drought in Indonesia. Clim Res 38:155-169. https://doi.org/10.3354/cr00778Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 38, No. 2. Online publication date: February 10, 2009 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2009 Inter-Research.

Spatial pattern detection modeling of thrips (Thrips tabaci) on onion fields

Onion (Allium cepa) is one of the most cultivated and consumed vegetables in Brazil and its importance is due to the large laborforce involved. One of the main pests that affect this crop is the Onion Thrips (Thrips tabaci), but the spatial distribution of this insect, although important, has not been considered in crop management recommendations, experimental planning or sampling procedures. Our purpose here is to consider statistical tools to detect and model spatial patterns of the occurrence of the onion thrips. In order to characterize the spatial distribution pattern of the Onion Thrips a survey was carried out to record the number of insects in each development phase on onion plant leaves, on different dates and sample locations, in four rural properties with neighboring farms under different infestation levels and planting methods. The Mantel randomization test proved to be a useful tool to test for spatial correlation which, when detected, was described by a mixed spatial Poisson model with a geostatistical random component and parameters allowing for a characterization of the spatial pattern, as well as the production of prediction maps of susceptibility to levels of infestation throughout the area.

Review Of The Sugar End Disorder In Potato (Solanum tuberosum, L.)

Processing quality has become increasingly important to the potato industry. A serious defect in product quality is the physiological disorder known as sugar ends, also referred to as dark ends, jelly ends, translucent ends, and/or glassy ends. Symptoms of the most common type of sugar end include relatively low starch and high sugar content in the basal end of the tuber. Tubers with the sugar end disorder produce French fries that are dark on one end, making the fries unacceptable to consumers. Sugar end potatoes cause time and profit losses at the processing plant and may require blending of loads to meet product specifications, along with the need to change processing protocols. Sugar end can be costly to growers if the tubers have insufficient quality and the crop is rejected for processing. Conditions conducive to the development of sugar ends include high soil temperatures, transitory soil moisture deficits, and insufficient or excess nitrogen fertilization. Tubers are most sensitive to these stresses during early bulking. Historically, several theories have been proposed concerning the mechanism of sugar end development. Research shows that stressed plants produce adequate amounts of assimilate to support continued tuber growth, but accumulate large amounts of sucrose in the basal tissues of the tuber immediately following stress. Accumulated evidence suggests that heat stress and water deficit induce changes in the activities of certain key carbohydrate metabolizing enzymes shifting the tuber from a starch synthesizing function to one of starch mobilization. Genetic resistance to sugar end development is known and the identified traits are highly heritable. This review summarizes sugar end investigations to-date, and presents a list of research-based crop management recommendations that assist potato producers in minimizing the potential for sugar end development in the field.

The effect of toposequence position on soil properties, hydrology, and yield of rainfed lowland rice in Southeast Asia

A large proportion of rainfed lowland rice in Southeast Asia is grown in gently sloping areas along toposequences with differences in elevation of a few meters. These small differences in elevation can lead to differentiation in soil properties and hydrological conditions, which in turn may affect crop performance and yield. It may be appropriate to replace blanket crop management recommendations in rainfed areas with toposequence-specific management recommendations. However, thorough statistical analyses of the relationships between toposequence position and field and crop conditions are lacking. In this paper, we statistically analysed the effect of toposequence position on soil properties, hydrological conditions, yield, and yield increase due to weed control and/or fertilizer management in rainfed areas in four villages in Indonesia and Thailand each in 2000–2002. Differences were substantial in field hydrology (average depth of ponded surface water and of groundwater, number of days without ponded surface water), exchangeable K, organic C, and clay content depending on toposequence position. There were also differences in other soil properties, including N, P, CEC, pH, sand, silt, bulk density, yield, and the magnitude of yield increase due to intensive weed control and/or recommended fertilizer application, but these effects were not consistent across countries, seasons, and years. The hypothesis that toposequence position would be a useful recommendation domain for weed control and fertilizer recommendations was not supported by our statistical results. The reasons why toposequence position has an inconsistent statistical effect could be (1) that the variability of the field conditions is larger among villages than among toposequence positions, and/or (2) that farmers already respond to differences in field conditions in their prevalent management practices, thus masking the effects of toposequence-specific variation on yield. Our findings suggest that despite the large toposequence effects on soil nutrient and water availability, weed and fertilizer management recommendations should be field-specific and time-specific rather than toposequence-specific.

Using On‐Farm Data to Validate Crop Management Recommendations and Implementation

Although farmers are encouraged to collect field‐specific crop management data to optimize on‐farm decisions, farm‐derived data are often discounted as a source of information for making broader management and environmental evaluations. This study was conducted to illustrate the use of on‐farm data for validating management recommendations and implementation. Data collected by farmers and crop management consultants from 1997 to 2000 from 1119 central Pennsylvania fields were used to evaluate the impact of soil series, potentially available added N (PAAN), and rainfall on corn (Zea mays L.) yield, N required to produce a unit of grain, and PAAN in excess or deficit of crop requirement. Results showed that although observed yields generally followed the same relative ranking as the established soil series yield capabilities, they were consistently lower by 13 to 30%. Also, the amount of PAAN per kilogram of corn grain observed was relatively consistent with PAAN recommendations for the most productive soils and about 25% higher for the least productive soils. Actual PAAN was on average 28 kg ha−1 higher than PAAN recommended for observed yields. The higher actual PAAN could be due to the difference between observed yields and established yield capabilities used by farmers to determine PAAN recommendations. Because data also showed that rainfall had a significant effect on observed yields, the inability to predict rainfall was another major cause for the difference between actual and recommended PAAN. The analysis showed the database was useful for evaluating corn yields and N management and confirmed that N practices are generally consistent with recommendations.

A framework to improve fertilizer recommendations for irrigated rice in West Africa

A framework to optimize soil fertility management in irrigated rice, based on soil and climate variability, fertilizer and paddy prices and farmers' objectives is presented. The framework uses three models in succession. The dynamic ecophysiological ORYZAS model simulates potential rice yields under irrigation, based on weather conditions, cultivar choice and sowing date. This yield potential is used in the static FERRIZ model, together with site specific information on recovery efficiency of applied N, P and K, indigenous soil N, P and K supply, and maximum N, P and K accumulation and dilution in rice dry matter. Resulting outputs are required fertilizer doses to obtain different target yields depending on yield potential and soil nutrient supply. The framework allows sensitivity analysis and agro-economic evaluation of different fertilizer options. In a last step, the dynamic decision tool RIDEV is used to simulate optimal timing of different management actions such as fertilizer application, weeding, and harvest. Resulting integrated crop management recommendations ensure high resource use efficiency adapted to local socio-economic and bio-physical conditions. Provided necessary input data are available, this framework can be applied to field, perimeter or regional scale. The framework was applied to the Office du Niger in Mali, using field data of 58 farmers. In the wet season, fertilizer doses adjusted to three soil fertility classes outperformed current uniform recommendations only slightly, except on soils with very low K supply (10 cases). Only on these soils was application of K profitable. Profit-optimizing fertilizer doses had high costs and low value/cost ratios. Target yields close to potential yield increased farmers' risk. Adjusting fertilizer doses to the lower yield potential in the dry season reduced costs and risk without reducing profit, resulting in better value/cost ratios. New simple recommendations for both wet and dry seasons were formulated.