Diego O. Ferraro

Evaluation of environmental impact indicators using fuzzy logic to assess the mixed cropping systems of the Inland Pampa, Argentina

Sustainable agriculture requires an adequate analysis framework. Fuzzy logic-based and field scale indicators were developed to evaluate the effects of pesticides and tillage on agroecosystems. All the assumptions and rules for making inferences reflect the current knowledge and the expert perception and judgment about the potential environmental impact of pesticides and tillage. The proposed indicators require four input variables: (1) number and type of applied pesticides, (2) rate of applied pesticides, (3) number and type of tillage tools, and (4) land capability class of each field. In regards to pesticide impact, the indicators consider the toxicity effects on: (1) mammals and (2) insects, while the tillage impact is evaluated taking into account the effects of different tillage operations on: (1) the retention of crop stubble on the soil surface, and (2) the stability of soil aggregates. Two overall outputs were obtained: (1) pesticide index and (2) tillage index. The developed indicators were used to compare the potential environmental effect of current practices carried out in Inland Pampa (Argentina). Concerning pesticide use, cropping winter wheat did less harm to the agroecosystem than cropping maize and sunflower. The overall values of tillage index were similar within crops. However, large differences in pesticide and tillage effects were found among tillage systems. Soybean showed the highest variability in both indexes. The type of analysis carried out in this study using farm-level variables may help find more sustainable ways to manage agricultural inputs.

Farm and landscape level variables as indicators of sustainable land-use in the Argentine Inland-Pampa

Sustainable land-use evaluation in agricultural systems needs to accommodate the landscape mosaic. Landscape characteristics, together with data gathered by farmers, were used to classify farms according to a scale related to sustainable land-use. The value of this scale as a tool enabling land managers to use information recorded by farmers and to diagnose land-use sustainability as a means of improving their land-management strategies was evaluated. Sustainability values were not related to any landscape in particular; farms with high sustainability could be found adjacent to farms with low sustainability. This indicates two important facts. First, differing management alternatives were mainly controlled by human decisions that in some way disregarded the ecological systems where the farms were located. Second, the observed variability demonstrates that there is room for improvement, especially by reducing inputs, without harming stability or productivity. An examination of the effect of incoming technologies on sustainability suggests other variables that could be considered in the calculation of farm sustainability indexes, such as those reflecting crop water-use efficiency, soil physical and biological characteristics, biological indicators of wildlife status, and intensification of grazing or harvesting of biomass for human and animal use.

Fuzzy knowledge-based model for soil condition assessment in Argentinean cropping systems

A knowledge-based system (KBS) for assessing soil condition in agroecosystems is presented. The KBS was built through expert opinion elicitation and available scientific data using fuzzy logic. The system is structured into three main elements: (1) input variables that represent the physical domain of soil condition assessment and are related to environmental and crop management conditions; (2) primary modules that describe the fuzzy nature of the soil indicators and; (3) secondary modules that represent the elicited knowledge on soil condition assessment from an expert panel. The application of the KBS on data on crop fields from Inland Pampa (Argentina) indicated that soil nitrogen depletion poses a hazard for soil health as no crop was able to accomplish more than 50% of the sustainability criteria elicited for soil nitrogen extraction from the system. Conversely, soil carbon and physical conditions exhibited values closer to the desirable scenarios elicited by the fuzzy if-then rules, with values of 0.84, 0.71 and 0.74 for maize, soybean and wheat, respectively, where higher indicator values reflect better soil condition assessment. No significant differences were observed in the overall soil degradation module between crops, with values of 0.64 for maize and wheat and 0.67 for soybean. The KBS developed in this work provided an alternative modeling tool for assessing agroecosystem condition when knowledge regarding long-term assessment is imprecise and uncertain.

Ecosystem Services Provided by Agroecosystems: A Qualitative and Quantitative Assessment of this Relationship in the Pampa Region, Argentina

The development of an analytical framework relating agricultural conditions and ecosystem services (ES) provision could be very useful for developing land-use systems which sustain natural resources for future use. According to this, a conceptual network was developed, based on literature review and expert knowledge, about the functional relationships between agricultural management and ES provision in the Pampa region (Argentina). We selected eight ES to develop this conceptual network: (1) carbon (C) balance, (2) nitrogen (N) balance, (3) groundwater contamination control, (4) soil water balance, (5) soil structural maintenance, (6) N2O emission control, (7) regulation of biotic adversities, and (8) biodiversity maintenance. This conceptual network revealed a high degree of interdependence among ES provided by Pampean agroecosystems, finding two trade-offs, and two synergies among them. Then, we analyzed the conceptual network structure, and found that both environmental and management variables influenced ES provision. Finally, we selected four ES to parameterize and quantify along 10 growing seasons (2000/2001–2009/2010) through a probabilistic methodology called Bayesian Networks. Only N balance was negatively impacted by agricultural management; while C balance, groundwater contamination control, and N2O emission control were not. Outcomes of our work emphasize the idea that qualitative and quantitative methodologies should be implemented together to assess ES provision in Pampean agroecosystems, as well as in other agricultural systems.

The use of hedges to reduce winter wheat losses caused by Italian ryegrass (Lolium multiflorum)

Abstract We hypothesized that short duration plant interference aiming to change mainly light intensity and red:far red ratio during crop establishment in Italian ryegrass-infested winter wheat fields, could hinder the development of Italian ryegrass and thus reduce its effect on wheat yield. To test this hypothesis, wheat was planted between previously established live (low red:far red ratio) or dead (high red:far red ratio), barnyardgrass or maize, plant hedges. Experimental results demonstrated that the presence of live or dead plant hedges during the first 30 days of crop establishment and also hedge orientation were important factors regulating weed and crop biomass production and competitive relationships. In barnyardgrass hedge treatments wheat yield was improved up to 67% and Italian ryegrass production was reduced by more than 20%. In plots with maize live hedges oriented N–S the biomass production of wheat was independent of production of Italian ryegrass. Data presented here suggests that there is room for developing weed control technologies on the basis of understanding photosensory processes of weed and crop species.

Weed Vegetation of Sugarcane Cropping Systems of Northern Argentina: Data-Mining Methods for Assessing the Environmental and Management Effects on Species Composition

Abstract Weed composition may vary because of natural environment, management practices, and their interactions. In this study we presented a systematic approach for analyzing the relative importance of environmental and management factors on weed composition of the most conspicuous species in sugarcane. A data-mining approach represented by k-means cluster and classification and regression trees (CART) were used for analyzing the 11 most frequent weeds recorded in sugarcane cropping systems of northern Argentina. Data of weed abundance and explanatory factors contained records from 1976 sugarcane fields over 2 consecutive years. The k-means method selected five different weed clusters. One cluster contained 44% of the data and exhibited the lowest overall weed abundance. The other four clusters were dominated by three perennial species, bermudagrass, johnsongrass, and purple nutsedge, and the annual itchgrass. The CART model was able to explain 44% of the sugarcanes weed composition variability. Four of the five clusters were represented in the terminal nodes of the final CART model. Sugarcane burning before harvesting was the first factor selected in the CART, and all nodes resulting from this split were characterized by low abundance of weeds. Regarding the predictive power of the variables, rainfall and the genotype identity were the most important predictors. These results have management implications as they indicate that the genotype identity would be a more important factor than crop age when designing sugarcane weed management. Moreover, the abiotic control of crop–weed interaction would be more related to rainfall than the environmental heterogeneity related to soil type, for example soil fertility. Although all these exploratory patterns resulting from the CART data-mining procedure should be refined, it became clear that this information may be used to develop an experimental framework to study the factors driving weed assembly. Nomenclature: Bermudagrass, Cynodon dactylon Pers. (CYNDA); johnsongrass, Sorghum halepense (L.) Pers. (SORHA); purple nutsedge, Cyperus rotundus L. (CYPRO); itchgrass, Rottboellia exaltata (L.) L.f.(ROOEX).

A comparison of two sensitivity analysis techniques based on four bayesian models representing ecosystem services provision in the Argentine Pampas

Abstract Sensitivity analyses (SAs) identify how an output variable of a model is modified by changes in the input variables. These analyses are a good way for assessing the performance of probabilistic models, like Bayesian Networks (BN). However, there are several commonly used SAs in BN literature, and formal comparisons about their outcomes are scarce. We used four previously developed BNs which represent ecosystem services provision in Pampean agroecosystems (Argentina) in order to test two local sensitivity approaches widely used. These SAs were: 1) One-at-a-time, used in BNs but more commonly in linear modelling; and 2) Sensitivity to findings, specific to BN modelling. Results showed that both analyses provided an adequate overview of BN behaviour. Furthermore, analyses produced a similar influence ranking of input variables over each output variable. Even though their interchangeably application could be an alternative in our bayesian models, we believe that OAT is the suitable one to implement here because of its capacity to demonstrate the relation (positive or negative) between input and output variables. In summary, we provided insights about two sensitivity techniques in BNs based on a case study which may be useful for ecological modellers.

Effects of Cultivar and Irrigated Water Quality on Sugarcane Residue Decomposition

In sugarcane cropping systems, green-cane harvesting has progressively replaced the traditional burning of standing crop prior to harvest, increasing the role of decomposition as a mechanism to replenish soil nutrients. We examined the impact of cultivar choice and irrigation water quality on decomposition of sugarcane residue. In two independent litterbag experiments, we isolated the effects of changes in plant residue quality and irrigated water quality. Cultivar residue exhibited significant variation of carbon and nitrogen concentrations and carbon to nitrogen ratio. Decomposition rate varied among cultivars, and those with greater carbon-to-nitrogen ratios decomposed faster than cultivars with lower ratios. Soil irrigated with river water showed a lower mineral and organic nitrogen concentration and decomposition rate than those irrigated with industry effluent wastewater. These results provide empirical evidence that both cultivar choice and water used for irrigation have a limited but significant impact on plant residue decomposition.